Knitro user options

Knitro has a great number and variety of user option settings and although it tries to choose the best settings by default, often significant performance improvements can be realized by choosing some non-default option settings.

Note

The hessopt user option cannnot be changed after calling KN_solve(). You must first call KN_free() and then reload the model before changing hessopt and solving again.

Note

In the pre-Knitro 11.0 API, user option names begin with KTR_, instead of KN_.

Index

User options are defined in the knitro.h and summarized in the following index. To see a more detailed description of an individual option and its possible values click on the option name. The importance of each option is related to its category (General, Derivatives, etc…), 1 being the most important parameters.

General options

Option name

Importance

Purpose

algorithm

1

Indicates which algorithm to use to solve the problem

blasoption

2

Specifies the BLAS/LAPACK function library to use for basic vector and matrix computations

blasoptionlib

3

Specifies a dynamic library name that contains object code for BLAS/LAPACK functions

bndrange

3

Specifies max limits on the magnitude of constraint and variable bounds

cg_maxit

2

Determines the maximum allowable number of inner conjugate gradient (CG) iterations

cg_pmem

3

Specifies number of nonzero elements per hessian column when computing preconditioner

cg_precond

2

Specifies whether or not to apply preconditioning during CG iterations in barrier algorithms

cg_stoptol

3

Relative stopping tolerance for CG subproblems

convex

1

Identify convex models and apply specializations often beneficial for convex models

cpuplatform

2

Specifies the target instruction set architecture for the machine on which Knitro is running

datacheck

2

Specifies whether to perform more extensive data checks

delta

3

Specifies the initial trust region radius scaling factor

eval_fcga

3

Specifies that gradients are provided together with functions in one callback

honorbnds

1

Indicates whether or not to enforce satisfaction of simple variable bounds

initpenalty

3

Initial penalty value used in Knitro merit function

linesearch_maxtrials

3

Indicates the maximum allowable number of trial points during the linesearch

linesearch

2

Indicates which linesearch strategy to use for the Interior/Direct or SQP algorithm

linsolver_ooc

3

Indicates whether to use Intel MKL PARDISO out-of-core solve of linear systems

linsolver

2

Indicates which linear solver to use to solve linear systems arising in Knitro algorithms

linsolver_maxitref

3

Specifies the maximum number of iterative refinement steps when solving the linear system

linsolver_pivottol

3

Specifies the initial pivot threshold used in factorization routines

ncvx_qcqp_init

2

Initialization strategy for non-convex QPs and QCQPs

objrange

3

Specifies the extreme limits of the objective function for purposes of determining unboundedness

presolve

1

Determine whether or not to use the Knitro presolver

presolve_initpt

2

Controls whether Knitro presolver can shift user-supplied initial point

presolve_level

2

Knitro presolve level to enable

presolve_passes

2

Number of passes through the Knitro presolver

presolve_tol

3

Determines the tolerance used by the Knitro presolver

presolveop_tighten

2

Determine whether bounds are tightened by the Knitro presolver

restarts

2

Specifies whether to enable automatic restarts

restarts_maxit

3

Maximum number of iterations before restarting when restarts are enabled

scale

1

Specifies whether to perform problem scaling

soc

3

Specifies whether or not to try second order corrections (SOC)

strat_warm_start

2

Specifies whether or not to invoke a warm-start strategy

Derivatives options

Option name

Importance

Purpose

bfgs_scaling

2

Specifies the initial scaling for the BFGS or L-BFGS Hessian approximation

derivcheck

1

Determine whether or not to perform a derivative check on the model

derivcheck_terminate

3

Determine whether or not to terminate after the derivative check

derivcheck_tol

3

Specifies the relative tolerance used for detecting derivative errors

derivcheck_type

3

Specifies whether to use forward or central finite differencing for the derivative checker

findiff_relstepsize

2

Specifies a relative stepsize when computing finite-difference gradients

gradopt

1

Specifies how to compute the gradients of the objective and constraint functions

hessian_no_f

3

Determines whether or not to allow Knitro to request Hessian evaluations without the objective component included.

hessopt

1

Specifies how to compute the (approximate) Hessian of the Lagrangian

lmsize

2

Specifies the number of limited memory pairs stored when approximating the Hessian

Termination options

Option name

Importance

Purpose

feastol

1

Specifies the final relative stopping tolerance for the feasibility error

feastol_abs

1

Specifies the final absolute stopping tolerance for the feasibility error

findiff_terminate

2

Specifies the termination criteria when using finite-difference gradients

fstopval

2

Used to implement a custom stopping condition based on the objective function value

ftol

2

The optimization process will terminate if feasible and the relative change in the objective function is less than ftol

ftol_iters

3

Number of consecutive feasible iterations where the relative change in the objective function is less than ftol before Knitro stops

infeastol

2

Specifies the (relative) tolerance used for declaring infeasibility of a model

infeastol_iters

3

Stop if number of consecutive infeasible iterations where the relative change in the feasibility error is less than infeasftol reaches this value

maxfevals

2

Specifies the maximum number of function evaluations before termination.

maxit

1

Specifies the maximum number of iterations before termination

maxtime_cpu

2

Specifies, in seconds, the maximum allowable CPU time before termination

maxtime_real

1

Specifies, in seconds, the maximum allowable real time before termination

opttol

1

Specifies the final relative stopping tolerance for the KKT (optimality) error

opttol_abs

1

Specifies the final absolute stopping tolerance for the KKT (optimality) error

xtol

1

The optimization process will terminate if the relative change of the solution point estimate is less than xtol

xtol_iters

3

Number of consecutive iterations where change of the solution point estimate is less than xtol before Knitro stops

Barrier options

Option name

Importance

Purpose

bar_conic_enable

1

Enable special treatments for conic constraints in the Interior/Direct algorithm

bar_directinterval

1

Controls the maximum number of consecutive conjugate gradient (CG) steps

bar_feasible

1

Specifies whether special emphasis is placed on getting and staying feasible

bar_feasmodetol

3

Specifies the tolerance in equation that determines whether Knitro will force subsequent iterates to remain feasible

bar_initmu

2

Specifies the initial value for the barrier parameter \mu used

bar_initpi_mpec

3

Specifies the initial value for the MPEC penalty parameter \pi

bar_initpt

2

Indicates initial point strategy for x, slacks and multipliers

bar_linsys

2

Indicates linear system form to use for Interior/Direct algorithm

bar_linsys_storage

2

Indicates linear system storage approach to use for Interior/Direct algorithm

bar_maxcorrectors

2

Specifies the maximum number of corrector steps allowed for primal-dual steps

bar_maxcrossit

3

Specifies the maximum number of crossover iterations before termination

bar_maxrefactor

3

Indicates the maximum number of refactorizations of the KKT system per iteration

bar_murule

1

Indicates which strategy to use for modifying the barrier parameter \mu

bar_penaltycons

2

Indicates whether a penalty approach is applied to the constraints

bar_penaltyrule

3

Indicates which penalty parameter strategy to use for determining whether or not to accept a trial iterate

bar_refinement

3

Specifies whether to try to refine the barrier solution for better precision

bar_relaxcons

2

Indicates whether a relaxation approach is applied to the constraints

bar_slackboundpush

3

Indicates minimum amount by which initial slack variables are pushed inside the bounds

bar_switchobj

3

Indicates objective function used when the barrier algorithms switch to a pure feasibility phase

bar_switchrule

3

Indicates whether or not the barrier algorithms will allow switching from an optimality phase to a pure feasibility phase

bar_watchdog

3

Specifies whether to enable watchdog heuristic

Active-set options

Option name

Importance

Purpose

act_lpalg

3

Indicates which algorithm to use for linear programming (LP) subproblems (only when using Cplex or Xpress)

act_lpfeastol

3

Feasibility tolerance for the linear programming solver in the Knitro Active Set or SQP algorithms

act_lppenalty

1

Indicate whether to use penalty formulation for linear programming subproblems

act_lppresolve

3

Controls presolve for linear programming subproblems (only when using Cplex or Xpress)

act_lpsolver

1

Indicates which linear programming solver the Knitro Active Set or SQP algorithms use

act_parametric

2

Solve parametric linear programming subproblems instead of standard LPs

act_qpalg

1

Indicates which algorithm to use to solve quadratic programming (QP) subproblems

act_qppenalty

2

Indicate whether to use penalty formulation for quadratic programming subproblems in SQP

cplexlibname

3

Name of the Xpress library when act_lpsolver=KN_ACT_LPSOLVER_CPLEX

xpresslibname

3

Name of the Xpress library when act_lpsolver=KN_ACT_LPSOLVER_XPRESS

MIP options

Option name

Importance

Purpose

mip_branchrule

1

Specifies which branching rule to use for MIP branch and bound procedure

mip_clique

2

Specifies rules for adding clique cuts

mip_cutfactor

2

Specifies a limit on the number of cuts added to a node subproblem

mip_cutoff

3

Specifies the MIP objective cutoff value

mip_cutting_plane

2

Specifies how to apply the cutting plane procedure

mip_debug

2

Specifies debugging level for MIP solution

mip_gub_branch

3

Specifies whether or not to branch on generalized upper bounds (GUBs)

mip_heuristic_diving

1

Specifies whether to enable the diving heuristic

mip_heuristic_feaspump

1

Specifies whether to enable the feasibility pump heuristic

mip_heuristic_lns

2

Specifies whether to enable large neighborhood search (LNS) heuristics

mip_heuristic_maxit

2

Specifies the maximum number of iterations to allow for MIP heuristics

mip_heuristic_mpec

1

Specifies whether to enable the MPEC heuristic

mip_heuristic_strategy

1

Specifies the global strategy and effort for MIP heuristics

mip_heuristic_terminate

2

Specifies the condition for terminating the MIP heuristic

mip_implications

2

Specifies whether or not to add constraints to the MIP derived from logical implications

mip_integer_tol

3

Specifies the threshold for deciding whether or not a variable is determined to be an integer

mip_intvar_strategy

2

Specifies how to handle integer variables

mip_knapsack

2

Specifies rules for adding MIP knapsack cuts

mip_liftproject

2

Adds lift and project cuts for MIP

mip_lpalg

2

Specifies which algorithm to use for any linear programming (LP) subproblem solves

mip_maxnodes

2

Specifies the maximum number of nodes explored (0 means no limit)

mip_maxsolves

3

Specifies the maximum number of subproblem solves allowed (0 means no limit)

mip_maxtime_cpu

2

Specifies the maximum allowable CPU time in seconds for the complete MIP solution

mip_maxtime_real

1

Specifies the maximum allowable real time in seconds for the complete MIP solution

mip_method

1

Specifies which MIP method to use

mip_mir

2

Specifies rules for adding mixed integer rounding cuts

mip_multistart

3

Enable multi-start at the branch-and-bound level

mip_nodealg

1

Specifies which algorithm to use for standard node subproblem solves in MIP

mip_numthreads

1

Specifies the number of threads for the MIP branch-and-bound method

mip_opt_gap_abs

1

The absolute optimality gap stop tolerance for MIP

mip_opt_gap_rel

1

The relative optimality gap stop tolerance for MIP

mip_outinterval

1

Specifies node printing interval for mip_outlevel when mip_outlevel > 0

mip_outlevel

1

Specifies how much MIP information to print

mip_outsub

3

Specifies MIP subproblem solve debug output control

mip_pseudoinit

3

Specifies the method used to initialize pseudo-costs

mip_relaxable

2

Specifies whether integer variables are relaxable

mip_rootalg

2

Specifies which algorithm to use for the root node solve in MIP

mip_rounding

2

Specifies the MIP rounding rule to apply

mip_selectdir

2

Specifies the MIP node selection direction rule for choosing the next node in the branch and bound tree

mip_selectrule

1

Specifies the MIP select rule for choosing the next node in the branch and bound tree

mip_strong_candlim

3

Specifies the maximum number of candidates to explore for MIP strong branching

mip_strong_level

3

Specifies the maximum number of tree levels on which to perform MIP strong branching

mip_strong_maxit

3

Specifies the maximum number of iterations to allow for MIP strong branching solves

mip_terminate

1

Specifies conditions for terminating the MIP algorithm

mip_zerohalf

2

Specifies rules for adding zero-half cuts

Multi-algorithm options

Option name

Importance

Purpose

ma_maxtime_cpu

3

Specifies the maximum allowable CPU time before termination for the multi-algorithm procedure

ma_maxtime_real

2

Specifies the maximum allowable real time before termination for the multi-algorithm procedure

ma_outsub

1

Enable writing algorithm output to files for the multi-algorithm procedure

ma_terminate

1

Define the termination condition for the multi-algorithm procedure

Multi-start options

Option name

Importance

Purpose

ms_deterministic

2

Indicates whether Knitro multi-start procedure will be deterministic

ms_enable

1

Indicates whether Knitro will solve from multiple start points to find a better local minimum

ms_maxbndrange

2

Specifies the maximum range that an unbounded variable can take when determining new start points

ms_maxsolves

1

Specifies how many start points to try in multi-start

ms_maxtime_cpu

3

Specifies, in seconds, the maximum allowable CPU time before termination

ms_maxtime_real

2

Specifies, in seconds, the maximum allowable real time before termination

ms_num_to_save

2

Specifies the number of distinct feasible points to save in a file named

ms_numthreads

1

Specify the number of threads to use for multi-start

ms_outsub

2

Enable writing algorithm output to files for the parallel multi-start procedure

ms_savetol

2

Specifies the tolerance for deciding if two feasible points are distinct

ms_seed

2

Seed value used to generate random initial points in multi-start

ms_startptrange

1

Specifies the maximum range that each variable can take when determining new start points

ms_terminate

1

Specifies the condition for terminating multi-start

Parallelism options

Option name

Importance

Purpose

blas_numthreads

2

Specify the number of threads to use for BLAS operations

concurrent_evals

1

Determines whether or not function and derivative evaluations can take place concurrently in parallel

conic_numthreads

2

Specify the number of threads to use for operations in the conic solver

linsolver_numthreads

2

Specify the number of threads to use for linear system solve operations

numthreads

1

Specify the number of threads to use for all parallel computing features

Output options

Option name

Importance

Purpose

debug

2

Controls the level of debugging output

newpoint

2

Specifies additional action to take after every iteration in a solve of a continuous problem

out_csvinfo

3

Specifies whether to create knitro_solve.csv information file

out_csvname

3

Specify non-default filename when using out_csvinfo

out_hints

2

Print diagnostic hints (e.g. on user option settings) after solving

outappend

2

Specifies whether output should be started in a new file, or appended to existing files

outdir

2

Specifies a single directory as the location to write all output files

outlev

1

Controls the level of output produced by Knitro

outmode

1

Specifies where to direct the output from Knitro

outname

2

Specify filename (default knitro.log) when directing output to a file via outmode

Tuner options

Option name

Importance

Purpose

tuner

1

Indicates whether to invoke the Knitro-Tuner

tuner_maxtime_cpu

2

Specifies the maximum allowable CPU time before terminating the Knitro-Tuner

tuner_maxtime_real

1

Specifies the maximum allowable real time before terminating the Knitro-Tuner

tuner_optionsfile

1

Can be used to specify the location of a Tuner options file

tuner_outsub

2

Enable writing additional Tuner subproblem solve output to files for the Knitro-Tuner procedure

tuner_terminate

1

Define the termination condition for the Knitro-Tuner procedure

General options

type algorithm
type alg
type KN_PARAM_ALG
#define KN_PARAM_ALGORITHM            1003
#define KN_PARAM_ALG                  1003
#  define KN_ALG_AUTOMATIC               0
#  define KN_ALG_AUTO                    0
#  define KN_ALG_BAR_DIRECT              1
#  define KN_ALG_BAR_CG                  2
#  define KN_ALG_ACT_CG                  3
#  define KN_ALG_ACT_SQP                 4
#  define KN_ALG_MULTI                   5

Indicates which algorithm to use to solve the problem

  • 0 (auto) let Knitro automatically choose an algorithm, based on the problem characteristics.

  • 1 (direct) use the Interior/Direct algorithm.

  • 2 (cg) use the Interior/CG algorithm.

  • 3 (active) use the Active Set algorithm.

  • 4 (sqp) use the SQP algorithm.

  • 5 (multi) run all algorithms, perhaps in parallel (see Algorithms).

Default value: 0

type blasoption
type KN_PARAM_BLASOPTION
#define KN_PARAM_BLASOPTION           1042
#  define KN_BLASOPTION_KNITRO           0
#  define KN_BLASOPTION_INTEL            1
#  define KN_BLASOPTION_DYNAMIC          2
#  define KN_BLASOPTION_BLIS             3

Specifies the BLAS/LAPACK function library to use for basic vector and matrix computations.

  • 0 (knitro) Use Knitro built-in functions.

  • 1 (intel) Use Intel Math Kernel Library (MKL) functions on available platforms.

  • 2 (dynamic) Use the dynamic library specified with option blasoptionlib.

  • 3 (blis) Use BLIS functions on available platforms (currently not available on Windows OS).

Default value: 1

Note

BLAS and LAPACK functions from Intel Math Kernel Library (MKL) are provided with the Knitro distribution. The number of threads to use for the MKL BLAS are specified with blas_numthreads. On platforms, where the intel MKL is not available, the Knitro built-in functions are used by default.

BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage) functions are used throughout Knitro for fundamental vector and matrix calculations. The CPU time spent in these operations can be measured by setting option debug = 1 and examining the output file kdbg_profile*.txt. Some optimization problems are observed to spend very little CPU time in BLAS/LAPACK operations, while others spend more than 50%. Be aware that the different function implementations can return slightly different answers due to roundoff errors in double precision arithmetic. Thus, changing the value of blasoption sometimes alters the iterates generated by Knitro, or even the final solution point.

The Knitro option uses built-in BLAS/LAPACK functions based on standard netlib routines (www.netlib.org). The intel option uses MKL functions written especially for x86 and x86_64 processor architectures. On a machine running an Intel processor, testing indicates that the MKL functions can significantly reduce the CPU time in BLAS/LAPACK operations. The dynamic option allows users to load any library that implements the functions declared in the file include/blas_lapack.h. Specify the library name with option blasoptionlib.

type blasoptionlib
type KN_PARAM_BLASOPTIONLIB
#define KN_PARAM_BLASOPTIONLIB        1045

Specifies a dynamic library name that contains object code for BLAS/LAPACK functions.

The library must implement all the functions declared in the file include/blas_lapack.h.

Note

This option has no effect unless blasoption = 2.

type bndrange
type KN_PARAM_BNDRANGE
#define KN_PARAM_BNDRANGE             1112

Specifies max limits on the magnitude of constraint and variable bounds. Any constraint or variable bounds whose magnitude is greater than or equal to bndrange will be treated as infinite by Knitro. Using very large, finite bounds is discouraged (and is generally an indication of a poorly scaled model).

Default value: 1.0e20

type cg_maxit
type KN_PARAM_CG_MAXIT
#define KN_PARAM_CG_MAXIT             1013

Determines the maximum allowable number of inner conjugate gradient (CG) iterations per Knitro minor iteration.

  • -1

    Let Knitro automatically determine a value.

  • 0 Knitro will set a maximum value based on the problem size.

  • n At most n>0 CG iterations may be performed during one minor iteration of Knitro.

Default value: -1

type cg_pmem
type KN_PARAM_CG_PMEM
#define KN_PARAM_CG_PMEM              1103

Specifies the amount of nonzero elements per column of the Hessian of the Lagrangian which are retained when computing the incomplete Cholesky preconditioner.

  • n At most n>0 nonzero elements per column.

Default value: 10

type cg_precond
type KN_PARAM_CG_PRECOND
#define KN_PARAM_CG_PRECOND           1041
#  define KN_CG_PRECOND_NONE             0
#  define KN_CG_PRECOND_CHOL             1

Specifies whether an incomplete Cholesky preconditioner is applied during CG iterations in barrier algorithms.

  • 0 (no) Not applied.

  • 1 (chol) Preconditioner is applied.

Default value: 0

type cg_stoptol
type KN_PARAM_CG_STOPTOL
#define KN_PARAM_CG_STOPTOL           1099

Specifies the relative stopping tolerance used for the conjugate gradient (CG) subproblem solves.

Default value: 1.0e-2

type convex
type KN_PARAM_CONVEX
#define KN_PARAM_CONVEX               1114
#  define KN_CONVEX_AUTO                -1
#  define KN_CONVEX_NO                   0
#  define KN_CONVEX_YES                  1

Declare the problem as convex by setting KN_CONVEX_YES or non-convex by setting KN_CONVEX_NO. Otherwise, Knitro will try to determine this automatically, but may only be able to do so for simple model forms such as QPs or QCQPs. If your model is specified as (or automatically determined to be) convex, this will cause Knitro to apply specializations and tunings that are often beneficial for convex models to speed up the solution. Currently this option is only active for the Interior/Direct algorithm, but may be applied to other algorithms in the future.

Default value: -1

type cpuplatform
type KN_PARAM_CPUPLATFORM
#define KN_PARAM_CPUPLATFORM          1120
#  define KN_CPUPLATFORM_AUTO           -1
#  define KN_CPUPLATFORM_COMPATIBLE      1
#  define KN_CPUPLATFORM_SSE2            2
#  define KN_CPUPLATFORM_AVX             3
#  define KN_CPUPLATFORM_AVX2            4
#  define KN_CPUPLATFORM_AVX512          5 /* EXPERIMENTAL */

This option can be used to specify the target instruction set architecture for the machine on which Knitro is running. This can be used, for example (especially using the setting KN_CPUPLATFORM_COMPATIBLE), to try to produce more consistent Knitro performance across various architectures (at the expense of, perhaps, slower performance on some platforms). This option is currently only used for the Intel Math Kernal Library (MKL) functions used inside Knitro.

Default value: -1

type KN_PARAM_DATACHECK
#define KN_PARAM_DATACHECK            1087
#  define KN_DATACHECK_NO                0
#  define KN_DATACHECK_YES               1

Specifies whether to perform more extensive data checks to look for errors in the problem input to Knitro (in particular, this option looks for errors in the sparse Jacobian and/or sparse Hessian structure). The datacheck may have a non-trivial cost for large problems. It is turned on by default, but can be turned off for improved speed.

Default value: 1

type delta
type KN_PARAM_DELTA
#define KN_PARAM_DELTA                1020

Specifies the initial trust region radius scaling factor used to determine the initial trust region size.

Default value: 1.0e0

type eval_fcga
type KN_PARAM_EVAL_FCGA
#define KN_PARAM_EVAL_FCGA            1116
#  define KN_EVAL_FCGA_NO                0
#  define KN_EVAL_FCGA_YES               1

Use this option to tell Knitro that you are providing the first derivatives (i.e. gradients) in the same callback routine used for your function evaluations.

Default value: 0

type honorbnds
type KN_PARAM_HONORBNDS
#define KN_PARAM_HONORBNDS            1002
#  define KN_HONORBNDS_AUTO             -1
#  define KN_HONORBNDS_NO                0
#  define KN_HONORBNDS_ALWAYS            1
#  define KN_HONORBNDS_INITPT            2

Indicates whether or not to enforce satisfaction of simple variable bounds throughout the optimization. The API function KN_set_var_honorbnds() can be used to set this option for each variable individually. This option and the bar_feasible option may be useful in applications where functions are undefined outside the region defined by inequalities.

  • -1 (auto) Knitro automatically determine the best setting.

  • 0 (no) Knitro does not require that the bounds on the variables be satisfied at intermediate iterates.

  • 1 (always) Knitro enforces that the initial point and all subsequent solution estimates satisfy the bounds on the variables.

  • 2 (initpt) Knitro enforces that the initial point satisfies the bounds on the variables.

Default value: -1

Note

Note that setting honorbnds = 1 (always) or 2 (initpt) or using the default auto option may cause Knitro to shift the value of a user-provided initial point so that it lies sufficiently inside the (possibly presolved) bounds. Setting honorbnds = 0 (no) will prevent Knitro from shifting a user-provided initial point.

type initpenalty
type KN_PARAM_INITPENALTY
#define KN_PARAM_INITPENALTY          1097

Specifies the initial penalty parameter used in the Knitro merit functions. The Knitro merit functions are used to balance improvements in the objective function versus improvements in feasibility. A larger initial penalty value places more weight initially on feasibility in the merit function.

Default value: 1.0e1

type linesearch
type KN_PARAM_LINESEARCH
#define KN_PARAM_LINESEARCH           1095
#  define KN_LINESEARCH_AUTO             0
#  define KN_LINESEARCH_BACKTRACK        1
#  define KN_LINESEARCH_INTERPOLATE      2
#  define KN_LINESEARCH_WEAKWOLFE        3

Indicates which linesearch strategy to use for the Interior/Direct or SQP algorithm to search for a new acceptable iterate. This option has no effect on the Interior/CG or Active Set algorithm.

  • 0 (auto) Let Knitro automatically choose the strategy.

  • 1 (backtrack) Use a simple backtracking scheme.

  • 2 (interpolate) Use a cubic interpolation scheme.

  • 3 (weakwolfe) Use a linesearch that satisfies the weak Wolfe conditions (unconstrained only).

Default value: 0

type linesearch_maxtrials
type KN_PARAM_LINESEARCH_MAXTRIALS
#define KN_PARAM_LINESEARCH_MAXTRIALS 1044

Indicates the maximum allowable number of trial points during the linesearch of the Interior/Direct or SQP algorithm before treating the linesearch step as a failure and generating a new step.

This option has no effect on the Interior/CG or Active Set algorithm.

Default value: 3

type linsolver
type KN_PARAM_LINSOLVER
#define KN_PARAM_LINSOLVER            1057
#  define KN_LINSOLVER_AUTO              0
#  define KN_LINSOLVER_INTERNAL          1
#  define KN_LINSOLVER_HYBRID            2
#  define KN_LINSOLVER_DENSEQR           3
#  define KN_LINSOLVER_MA27              4
#  define KN_LINSOLVER_MA57              5
#  define KN_LINSOLVER_MKLPARDISO        6
#  define KN_LINSOLVER_MA97              7
#  define KN_LINSOLVER_MA86              8

Indicates which linear solver to use to solve linear systems arising in Knitro algorithms.

  • 0 (auto) Let Knitro automatically choose the linear solver.

  • 1 (internal) Not currently used; reserved for future use. Same as auto for now.

  • 2 (hybrid) Use a hybrid approach where the solver chosen depends on the particular linear system which needs to be solved.

  • 3 (qr) Use a dense QR method. This approach uses LAPACK QR routines. Since it uses a dense method, it is only efficient for small problems. It may often be the most efficient method for small problems with dense Jacobians or Hessian matrices.

  • 4 (ma27) Use the HSL MA27 sparse symmetric indefinite solver.

  • 5 (ma57) Use the HSL MA57 sparse symmetric indefinite solver.

  • 6 (mklpardiso) Use the Intel MKL PARDISO (parallel, deterministic) sparse symmetric indefinite solver.

  • 7 (ma97) Use the HSL MA97 (parallel, deterministic) sparse symmetric indefinite solver.

  • 8 (ma86) Use the HSL MA86 (parallel, non-deterministic) sparse symmetric indefinite solver.

Default value: 0

Note

The QR linear solver, the HSL MA57/MA86/MA97 linear solvers and the Intel MKL PARDISO solver all make frequent use of Basic Linear Algebra Subroutines (BLAS) for internal linear algebra operations. If using any of these it is highly recommended to use optimized BLAS for your particular machine. This can result in dramatic speedup. This BLAS library is optimized for Intel processors and can be selected by setting blasoption=intel. Please read the notes under the blasoption user option in this section for more details about the BLAS options in Knitro and how to make sure that the Intel MKL BLAS or other user-specified BLAS can be used by Knitro. You may also achieve speedups using multi-threaded BLAS with these solvers by setting numthreads>1 or blas_numthreads>1 when using the solvers.

Additionally, the HSL solvers MA86 and MA97 and the Intel MKL PARDISO solver are specifically designed to exploit parallelism (beyond BLAS parallelism) to achieve speedups on large problems. You may try setting numthreads>1 or linsolver_numthreads>1 (with blas_numthreads =1) when using these solvers, to obtain greater speedups.

type linsolver_maxitref
type KN_PARAM_LINSOLVER_MAXITREF
#define KN_PARAM_LINSOLVER_MAXITREF   1130

Indicates the maximum allowable number of iterative refinement steps applied when a linear system is solved inside Knitro. Iterative refinement steps may be applied when there are significant errors (e.g. large residuals) in the linear system solves. Applying more iterative refinement steps may improve the numerical accuracy of the linear solves at extra cost.

Default value: 2

type linsolver_ooc
type KN_PARAM_LINSOLVER_OOC
#define KN_PARAM_LINSOLVER_OOC        1076
#  define KN_LINSOLVER_OOC_NO            0
#  define KN_LINSOLVER_OOC_MAYBE         1
#  define KN_LINSOLVER_OOC_YES           2

Indicates whether to use Intel MKL PARDISO out-of-core solve of linear systems when linsolver = mklpardiso.

This option is only active when linsolver = mklpardiso.

  • 0 (no) Do not use Intel MKL PARDISO out-of-core option.

  • 1 (maybe) Maybe solve out-of-core depending on how much space is needed.

  • 2 (yes) Solve linear systems out-of-core when using Intel MKL PARDISO.

Default value: 0

Note

See the Intel MKL PARDISO documentation for more details on how this option works.

type linsolver_pivottol
type KN_PARAM_LINSOLVER_PIVOTTOL
#define KN_PARAM_LINSOLVER_PIVOTTOL   1029

Specifies the initial pivot threshold used in factorization routines.

The value should be in the range [0, …, 0.5] with higher values resulting in more pivoting (more stable factorizations). Values less than 0 will be set to 0 and values larger than 0.5 will be set to 0.5. If linsolver_pivottol is non-positive, initially no pivoting will be performed. Smaller values may improve the speed of the code but higher values are recommended for more stability (for example, if the problem appears to be very ill-conditioned).

Default value: 1.0e-8

type ncvx_qcqp_init
type KN_PARAM_NCVX_QCQP_INIT
#define KN_PARAM_NCVX_QCQP_INIT       1139
#  define KN_NCVX_QCQP_INIT_AUTO        -1
#  define KN_NCVX_QCQP_INIT_NONE         0
#  define KN_NCVX_QCQP_INIT_LINEAR       1
#  define KN_NCVX_QCQP_INIT_HYBRID       2
#  define KN_NCVX_QCQP_INIT_PENALTY      3
#  define KN_NCVX_QCQP_INIT_CVXQUAD      4

Specifies the initialization strategy used for non-convex QPs and QCQPs. In particular, these strategies may be more likely to cause Knitro to find global or better local solutions on non-convex quadratic programs (QPs) or non-convex quadratically constrained quadratic programs (QCQPs).

  • -1 (auto) Knitro will automatically determine the strategy.

  • 0 (none) No special initialization strategy is used.

  • 1 (linear) Initialize by solving a linear relaxation.

  • 2 (hybrid) Initialize by solving a hybrid formulation.

  • 3 (penalty) Initialize by solving a penalty formulation.

  • 4 (cvxquad) Initialize by solving a convex quadratic relaxation.

Default value: -1

type objrange
type KN_PARAM_OBJRANGE
#define KN_PARAM_OBJRANGE             1026

Specifies the extreme limits of the objective function for purposes of determining unboundedness.

If the magnitude of the objective function becomes greater than objrange for a feasible iterate, then the problem is determined to be unbounded and Knitro proceeds no further.

Default value: 1.0e20

type presolve
type KN_PARAM_PRESOLVE
#define KN_PARAM_PRESOLVE             1059
#  define KN_PRESOLVE_NO                 0
#  define KN_PRESOLVE_YES                1

Determine whether or not to use the Knitro presolver to try to simplify the model by removing variables or constraints.

  • 0 (no) Do not use the Knitro presolver.

  • 1 (yes) Enable the Knitro presolver.

Default value: 1

type presolve_level
type KN_PARAM_PRESOLVE_LEVEL
#define KN_PARAM_PRESOLVE_LEVEL       1122
#  define KN_PRESOLVE_LEVEL_AUTO        -1
#  define KN_PRESOLVE_LEVEL_1            1
#  define KN_PRESOLVE_LEVEL_2            2

Set the level of presolve operations to enable through the Knitro presolver. A higher presolve level enables more complex presolve operations.

  • -1 (auto) Let Knitro automatically choose the presolve level.

  • 1 (level1) Enable the most basic presolve operations.

  • 2 (level2) Enable more advanced presolve operations.

Default value: -1

type presolve_initpt
type KN_PARAM_PRESOLVE_INITPT
#define KN_PARAM_PRESOLVE_INITPT      1127
#  define KN_PRESOLVE_INITPT_AUTO       -1
#  define KN_PRESOLVE_INITPT_NOSHIFT     0
#  define KN_PRESOLVE_INITPT_LINSHIFT    1
#  define KN_PRESOLVE_INITPT_ANYSHIFT    2

Control whether the Knitro presolver can shift a user-supplied initial point.

  • -1 (auto) Let Knitro automatically choose whether to allow shifting.

  • 0 (noshift) Do not allow presolver to shift user-supplied initial point.

  • 1 (linshift) Allow presolver to shift user-supplied initial point if it only appears in linear constraints.

  • 2 (anyshift) Allow presolver to shift any user-supplied initial point.

Default value: -1

type presolve_passes
type KN_PARAM_PRESOLVE_PASSES
#define KN_PARAM_PRESOLVE_PASSES      1121

Set a maximum limit on the number of passes through the Knitro presolve operations.

Default value: 10

type presolve_tol
type KN_PARAM_PRESOLVE_TOL
#define KN_PARAM_PRESOLVE_TOL         1060

Determines the tolerance used by the Knitro presolver to remove variables and constraints from the model. If you believe the Knitro presolver is incorrectly modifying the model, use a smaller value for this tolerance (or turn the presolver off).

Default value: 1.0e-6

type presolveop_tighten
type KN_PARAM_PRESOLVEOP_TIGHTEN
#define KN_PARAM_PRESOLVEOP_TIGHTEN   1125
#  define KN_PRESOLVEOP_TIGHTEN_AUTO    -1
#  define KN_PRESOLVEOP_TIGHTEN_NONE     0
#  define KN_PRESOLVEOP_TIGHTEN_VARBND   1

Determine whether or not to enable the Knitro presolve operation to tighten variable bounds.

  • -1 (auto) Automatically determined (may depend on the algorithm).

  • 0 (none) Do not tighten variable bounds (unless it removes a constraint).

  • 1 (varbnd) Enable tightening variable bounds always.

Default value: -1

type restarts
type KN_PARAM_RESTARTS
#define KN_PARAM_RESTARTS             1100

Specifies whether or not to enable automatic restarts in Knitro. When enabled, if a Knitro algorithm seems to be converging slowly or not converging, the algorithm will automatically restart, which may help with convergence.

  • 0 No automatic restarts allowed.

  • n At most n>0 automatic restarts may be performed.

Default value: 0

type restarts_maxit
type KN_PARAM_RESTARTS_MAXIT
#define KN_PARAM_RESTARTS_MAXIT       1101

When restarts are enabled, this option can be used to specify a maximum number of iterations before enforcing a restart.

  • 0 No iteration limit on restarts enforced.

  • n At most n>0 iterations are allowed without convergence before enforcing an automatic restart, if restarts are enabled.

Default value: 0

type scale
type KN_PARAM_SCALE
#define KN_PARAM_SCALE                1017
#  define KN_SCALE_NEVER                 0
#  define KN_SCALE_NO                    0
#  define KN_SCALE_USER_INTERNAL         1
#  define KN_SCALE_USER_NONE             2
#  define KN_SCALE_INTERNAL              3

Specifies whether to perform problem scaling of the objective function, constraint functions, or possibly variables.

If scaling is performed, internal computations, including some aspects of the optimality tests, are based on the scaled values, though the feasibility error is always computed in terms of the original, unscaled values.

  • 0 (no) No scaling is performed.

  • 1 (user_internal) User provided scaling is used if defined, otherwise Knitro internal scaling is applied.

  • 2 (user_none) User provided scaling is used if defined, otherwise no scaling is applied.

  • 3 (internal) Knitro internal scaling is applied.

Default value: 1

type soc
type KN_PARAM_SOC
#define KN_PARAM_SOC                  1019
#  define KN_SOC_NO                      0
#  define KN_SOC_MAYBE                   1
#  define KN_SOC_YES                     2

Specifies whether or not to try second order corrections (SOC).

A second order correction may be beneficial for problems with highly nonlinear constraints.

  • 0 (no) No second order correction steps are attempted.

  • 1 (maybe) Second order correction steps may be attempted on some iterations.

  • 2 (yes) Second order correction steps are always attempted if the original step is rejected and there are nonlinear constraints.

Default value: 1

type strat_warm_start
type KN_PARAM_STRAT_WARM_START
#define KN_PARAM_STRAT_WARM_START     1118
#  define KN_STRAT_WARM_START_NO         0
#  define KN_STRAT_WARM_START_YES        1

Specifies whether or not to invoke a warm-start strategy.

A warm-start strategy may be beneficial when an initial point close to the solution can be provided. For example, this may occur when solving a sequence of closely related problems, and the solution from one problem can be used to initialize (or warm-start) the next problem in the sequence. The Knitro warm-start strategy will use this information to tune the algorithms to try to converge more quickly in this case. If the initial point is not sufficiently close to the solution, or is too infeasible, the warm-start strategy may not be helpful.

This option is currently only used for the Knitro barrier/interior-point algorithms. In this case it may also be useful to experiment with different (smaller than default) values for the initial barrier parameter bar_initmu. In general, the closer the initial point is to the solution, the smaller this value should be (Knitro will try by default to initialize this to a good value when applying a warm-start strategy).

  • 0 (no) No warm-start strategy is applied.

  • 1 (yes) Knitro will apply a warm-start strategy with special tunings.

Default value: 0

Derivatives options

type bfgs_scaling
type KN_PARAM_BFGS_SCALING
#define KN_PARAM_BFGS_SCALING         1131
#  define KN_BFGS_SCALING_DYNAMIC        0
#  define KN_BFGS_SCALING_INVHESS        1
#  define KN_BFGS_SCALING_HESS           2

Specify the initial scaling to use for the BFGS or L-BFGS Hessian approximation.

  • 0 (dynamic) Dynamically choose which scaling to use.

  • 1 (invhess) The scaling approximates the scale of the inverse Hessian.

  • 2 (hess) The scaling approximates the scale of the Hessian.

Default value: 0

type derivcheck
type KN_PARAM_DERIVCHECK
#define KN_PARAM_DERIVCHECK           1080
#  define KN_DERIVCHECK_NONE             0
#  define KN_DERIVCHECK_FIRST            1
#  define KN_DERIVCHECK_SECOND           2
#  define KN_DERIVCHECK_ALL              3

Determine whether or not to perform a derivative check on the model.

  • 0 (none) Do not perform a derivative check.

  • 1 (first) Check first derivatives only.

  • 2 (second) Check second derivatives (i.e. the Hessian) only.

  • 3 (all) Check both first and second derivatives.

Default value: 0

type derivcheck_terminate
type KN_PARAM_DERIVCHECK_TERMINATE
#define KN_PARAM_DERIVCHECK_TERMINATE 1088
#  define KN_DERIVCHECK_STOPERROR        1
#  define KN_DERIVCHECK_STOPALWAYS       2

Determine whether to always terminate after the derivative check or only when the derivative checker detects a possible error.

  • 1 (error) Terminate only when an error is detected.

  • 2 (always) Always terminate when the derivative check is finished.

Default value: 1

type derivcheck_tol
type KN_PARAM_DERIVCHECK_TOL
#define KN_PARAM_DERIVCHECK_TOL       1082

Specifies the relative tolerance used for detecting derivative errors, when the Knitro derivative checker is enabled.

Default value: 1.0e-6

type derivcheck_type
type KN_PARAM_DERIVCHECK_TYPE
#define KN_PARAM_DERIVCHECK_TYPE      1081
#  define KN_DERIVCHECK_FORWARD          1
#  define KN_DERIVCHECK_CENTRAL          2

Specifies whether to use forward or central finite differencing for the derivative checker when it is enabled.

  • 1 (forward) Use forward finite differencing for the derivative checker.

  • 2 (central) Use central finite differencing for the derivative checker.

Default value: 1

type gradopt
type KN_PARAM_GRADOPT
#define KN_PARAM_GRADOPT              1007
#  define KN_GRADOPT_EXACT               1
#  define KN_GRADOPT_FORWARD             2
#  define KN_GRADOPT_CENTRAL             3

Specifies how to compute the gradients of the objective and constraint functions.

  • 1 (exact) User provides a routine for computing the exact gradients.

  • 2 (forward) Knitro computes gradients by forward finite differences.

  • 3 (central) Knitro computes gradients by central finite differences.

Default value: 1

Note

It is highly recommended to provide exact gradients if at all possible as this greatly impacts the performance of the code.

type hessian_no_f
type KN_PARAM_HESSIAN_NO_F
#define KN_PARAM_HESSIAN_NO_F         1062
#  define KN_HESSIAN_NO_F_FORBID         0
#  define KN_HESSIAN_NO_F_ALLOW          1

Determines whether or not to allow Knitro to request Hessian (or Hessian-vector product) evaluations without the objective component included. If hessian_no_f=0, Knitro will only ask the user for the standard Hessian and will internally approximate the Hessian without the objective component when it is needed. When hessian_no_f=1, Knitro will provide a flag to the user EVALH_NO_F (or EVALHV_NO_F) when it wants an evaluation of the Hessian (or Hessian-vector product) without the objective component. Using hessian_no_f=1 (and providing the appropriate Hessian) may improve Knitro performance on some problems.

This option only has an effect when hessopt=1 (i.e. user-provided exact Hessians), or hessopt=5 (i.e. user-provided exact Hessians-vector products).

  • 0 (forbid) Knitro will not ask for Hessian evaluations without the objective component.

  • 1 (allow) Knitro may ask for Hessian evaluations without the objective component.

Default value: 0

type hessopt
type KN_PARAM_HESSOPT
#define KN_PARAM_HESSOPT              1008
#  define KN_HESSOPT_EXACT               1
#  define KN_HESSOPT_BFGS                2
#  define KN_HESSOPT_SR1                 3
#  define KN_HESSOPT_PRODUCT_FINDIFF     4
#  define KN_HESSOPT_PRODUCT             5
#  define KN_HESSOPT_LBFGS               6
#  define KN_HESSOPT_GAUSS_NEWTON        7

Specifies how to compute the (approximate) Hessian of the Lagrangian.

  • 1 (exact) User provides a routine for computing the exact Hessian.

  • 2 (bfgs) Knitro computes a (dense) quasi-Newton BFGS Hessian.

  • 3 (sr1) Knitro computes a (dense) quasi-Newton SR1 Hessian.

  • 4 (product_findiff) Knitro computes Hessian-vector products using finite-differences.

  • 5 (product) User provides a routine to compute the Hessian-vector products.

  • 6 (lbfgs) Knitro computes a limited-memory quasi-Newton BFGS Hessian (its size is determined by the option lmsize).

  • 7 (gauss_newton) Knitro computes a Gauss-Newton approximation of the hessian (available for least-squares only, and default value for least-squares)

Default value: 1

Note

Options hessopt = 4 and hessopt = 5 are not available with the Interior/Direct or SQP algorithms.

Knitro usually performs best when the user provides exact Hessians (hessopt = 1) or exact Hessian-vector products (hessopt = 5). If neither can be provided but exact gradients are available (i.e., gradopt = 1), then hessopt = 4 may be a good option. This option is comparable in terms of robustness to the exact Hessian option and typically not much slower in terms of time, provided that gradient evaluations are not a dominant cost. However, this option is only available for some algorithms. If exact gradients cannot be provided, then one of the quasi-Newton options is preferred. Options hessopt = 2 and hessopt = 3 are only recommended for small problems (say, n < 1000) since they require working with a dense Hessian approximation. Note that with these last two options, the Hessian pattern will be ignored since Knitro computes a dense approximation. Option hessopt = 6 should be used for large problems.

type lmsize
type KN_PARAM_LMSIZE
#define KN_PARAM_LMSIZE               1038

Specifies the number of limited memory pairs stored when approximating the Hessian using the limited-memory quasi-Newton BFGS option. The value must be between 1 and 100 and is only used with hessopt = 6.

Larger values may give a more accurate, but more expensive, Hessian approximation. Smaller values may give a less accurate, but faster, Hessian approximation. When using the limited memory BFGS approach it is recommended to experiment with different values of this parameter.

Default value: 10

Termination options

type feastol
type KN_PARAM_FEASTOL
#define KN_PARAM_FEASTOL              1022

Specifies the final relative stopping tolerance for the feasibility error.

Smaller values of feastol result in a higher degree of accuracy in the solution with respect to feasibility.

Default value: 1.0e-6

type feastol_abs
type KN_PARAM_FEASTOLABS
#define KN_PARAM_FEASTOLABS           1023

Specifies the final absolute stopping tolerance for the feasibility error. Smaller values of feastol_abs result in a higher degree of accuracy in the solution with respect to feasibility.

Default value: 1.0e-3

type findiff_relstepsize
type KN_PARAM_FINDIFF_RELSTEPSIZE
#define KN_PARAM_FINDIFF_RELSTEPSIZE  1123

Specifies the relative stepsize used for finite-difference gradients. This option sets the stepsize for all variables. The API function KN_set_cb_relstepsizes() can be used to customize the settings for individual variables.

Default value: sqrt(eps) (forward-difference), eps^(1/3) (central difference)

type findiff_terminate
type KN_PARAM_FINDIFF_TERMINATE
#define KN_PARAM_FINDIFF_TERMINATE    1119
#  define KN_FINDIFF_TERMINATE_NONE      0
#  define KN_FINDIFF_TERMINATE_ERREST    1

This option specifies the termination criteria when using finite-difference gradients. The optimality (or KKT) conditions for nonlinear optimization depend on gradient values of the nonlinear objective and constraint functions (see Termination criteria). When using finite-difference gradients (e.g. gradopt > 1), there will typically be small errors in the computed gradients that will limit the precision in the solution (and the ability to satisfy the optimality conditions). By default, Knitro will try to estimate these finite-difference gradient errors and terminate when it seems that no more accuracy in the solution is possible. The solution will be treated as optimal as long as it is feasible and the optimality conditions are satisfied either by the optimality tolerances (opttol and opttol_abs) or the error estimates. On some problems, the error estimates may result in extra function evaluations on some iterations, but will often prevent extra iterations that produce no significant improvement in the solution. This special termination can be disabled by setting findiff_terminate = 0 (none).

  • 0 (none) No special criteria; use the standard stopping conditions.

  • 1 (errest) Allow termination based on estimates of the finite-difference error (when no more significant progress is likely).

Default value: 1

type fstopval
type KN_PARAM_FSTOPVAL
#define KN_PARAM_FSTOPVAL             1086

Used to implement a custom stopping condition based on the objective function value. Knitro will stop and declare that a satisfactory solution was found if a feasible objective function value at least as good as the value specified by fstopval is achieved. This stopping condition is only active when the absolute value of fstopval is less than objrange.

Default value: KN_INFINITY

type ftol
type KN_PARAM_FTOL
#define KN_PARAM_FTOL                 1090

The optimization process will terminate if the relative change in the objective function is less than ftol for ftol_iters consecutive feasible iterations.

Default value: 1.0e-15

type ftol_iters
type KN_PARAM_FTOL_ITERS
#define KN_PARAM_FTOL_ITERS           1091

The optimization process will terminate if the relative change in the objective function is less than ftol for ftol_iters consecutive feasible iterations.

Default value: 5

type infeastol
type KN_PARAM_INFEASTOL
#define KN_PARAM_INFEASTOL            1056

Specifies the (relative) tolerance used for declaring infeasibility of a model.

Smaller values of infeastol make it more difficult to satisfy the conditions Knitro uses for detecting infeasible models. If you believe Knitro incorrectly declares a model to be infeasible, then you should try a smaller value for infeastol.

Default value: 1.0e-8

type infeastol_iters
type KN_PARAM_INFEASTOL_ITERS
#define KN_PARAM_INFEASTOL_ITERS      1124

The optimization process will terminate if the relative change in the feasibility error is less than infeastol for infeastol_iters consecutive infeasible iterations.

Default value: 50

type maxfevals
type KN_PARAM_MAXFEVALS
#define KN_PARAM_MAXFEVALS            1085

Specifies the maximum number of function evaluations before termination. Values less than zero imply no limit.

Default value: -1 (unlimited)

type maxit
type KN_PARAM_MAXIT
#define KN_PARAM_MAXIT                1014

Specifies the maximum number of iterations before termination.

  • 0 Let Knitro automatically choose a value based on the problem type. Currently Knitro sets this value to 10000 for LPs/NLPs and 3000 for MIP problems.

  • n At most n>0 iterations may be performed before terminating.

Default value: 0

type maxtime_cpu
type KN_PARAM_MAXTIMECPU
#define KN_PARAM_MAXTIMECPU           1024

Specifies, in seconds, the maximum allowable CPU time before termination.

Default value: 1.0e8

type maxtime_real
type KN_PARAM_MAXTIMEREAL
#define KN_PARAM_MAXTIMEREAL          1040

Specifies, in seconds, the maximum allowable real time before termination.

Default value: 1.0e8

type opttol
type KN_PARAM_OPTTOL
#define KN_PARAM_OPTTOL               1027

Specifies the final relative stopping tolerance for the KKT (optimality) error.

Smaller values of opttol result in a higher degree of accuracy in the solution with respect to optimality.

Default value: 1.0e-6

type opttol_abs
type KN_PARAM_OPTTOLABS
#define KN_PARAM_OPTTOLABS            1028

Specifies the final absolute stopping tolerance for the KKT (optimality) error.

Smaller values of opttol_abs result in a higher degree of accuracy in the solution with respect to optimality.

Default value: 1.0e-3

type xtol
type KN_PARAM_XTOL
#define KN_PARAM_XTOL                 1030

The optimization process will terminate if the relative change in all components of the solution point estimate is less than xtol for xtol_iters. consecutive iterations. If using the Interior/Direct or Interior/CG algorithm and the barrier parameter is still large, Knitro will first try decreasing the barrier parameter before terminating.

Default value: 1.0e-12

type xtol_iters
type KN_PARAM_XTOL_ITERS
#define KN_PARAM_XTOL_ITERS           1094

The optimization process will terminate if the relative change in the solution estimate is less than xtol for xtol_iters consecutive iterations. If set to 0, Knitro chooses this value based on the solver and context. Currently Knitro sets this value to 3 unless the MISQP algorithm is being used, in which case the value is set to 1 by default.

Default value: 0

Barrier options

type bar_conic_enable
type KN_PARAM_BAR_CONIC_ENABLE
#define KN_PARAM_BAR_CONIC_ENABLE     1113
#  define KN_BAR_CONIC_ENABLE_AUTO      -1
#  define KN_BAR_CONIC_ENABLE_NONE       0
#  define KN_BAR_CONIC_ENABLE_SOC        1

Enable special treatments for conic constraints when using the Interior/Direct algorithm (has no affect when using the Interior/CG algorithm).

  • -1 (auto) Let Knitro automatically choose the strategy.

  • 0 (none) Do not apply any special treatment for conic constraints.

  • 1 (soc) Apply special treatments for any Second Order Cone (SOC) constraints identified in the model.

Default value: -1

type bar_directinterval
type KN_PARAM_BAR_DIRECTINTERVAL
#define KN_PARAM_BAR_DIRECTINTERVAL   1058

Controls the maximum number of consecutive conjugate gradient (CG) steps before Knitro will try to enforce that a step is taken using direct linear algebra.

This option is only valid for the Interior/Direct algorithm and may be useful on problems where Knitro appears to be taking lots of conjugate gradient steps. Setting bar_directinterval to 0 will try to enforce that only direct steps are taken which may produce better results on some problems.

Default value: 10

type bar_feasible
type KN_PARAM_BAR_FEASIBLE
#define KN_PARAM_BAR_FEASIBLE         1006
#  define KN_BAR_FEASIBLE_NO             0
#  define KN_BAR_FEASIBLE_STAY           1
#  define KN_BAR_FEASIBLE_GET            2
#  define KN_BAR_FEASIBLE_GET_STAY       3

Specifies whether special emphasis is placed on getting and staying feasible in the interior-point algorithms.

  • 0 (no) No special emphasis on feasibility.

  • 1 (stay) Iterates must satisfy inequality constraints once they become sufficiently feasible.

  • 2 (get) Special emphasis is placed on getting feasible before trying to optimize.

  • 3 (get_stay) Implement both options 1 and 2 above.

Default value: 0

Note

This option can only be used with the Interior/Direct and Interior/CG algorithms.

If bar_feasible = stay or bar_feasible = get_stay, this will activate the feasible version of Knitro. The feasible version of Knitro will force iterates to strictly satisfy inequalities, but does not require satisfaction of equality constraints at intermediate iterates. This option and the honorbnds option may be useful in applications where functions are undefined outside the region defined by inequalities. The initial point must satisfy inequalities to a sufficient degree; if not, Knitro may generate infeasible iterates and does not switch to the feasible version until a sufficiently feasible point is found. Sufficient satisfaction occurs at a point x if it is true for all inequalities that

cl + tol \leq c(x) \leq cu - tol

The constant tol is determined by the option bar_feasmodetol.

If bar_feasible = get or bar_feasible = get_stay, Knitro will place special emphasis on first trying to get feasible before trying to optimize.

type bar_feasmodetol
type KN_PARAM_BAR_FEASMODETOL
#define KN_PARAM_BAR_FEASMODETOL      1021

Specifies the tolerance in equation that determines whether Knitro will force subsequent iterates to remain feasible.

The tolerance applies to all inequality constraints in the problem. This option only has an effect if option bar_feasible = stay or bar_feasible = get_stay.

Default value: 1.0e-4

type bar_initmu
type KN_PARAM_BAR_INITMU
#define KN_PARAM_BAR_INITMU           1025

Specifies the initial value for the barrier parameter \mu used with the barrier algorithms.

This option has no effect on the Active Set algorithm.

Default value: 1.0e-1

type bar_initpi_mpec
type KN_PARAM_BAR_INITPI_MPEC
#define KN_PARAM_BAR_INITPI_MPEC      1093

Specifies the initial value for the MPEC penalty parameter \pi used when solving problems with complementarity constraints using the barrier algorithms. If this value is non-positive, then Knitro uses an internal formula to initialize the MPEC penalty parameter.

Default value: 0.0

type bar_initpt
type KN_PARAM_BAR_INITPT
#define KN_PARAM_BAR_INITPT           1009
#  define KN_BAR_INITPT_AUTO             0
#  define KN_BAR_INITPT_CONVEX           1
#  define KN_BAR_INITPT_NEARBND          2
#  define KN_BAR_INITPT_CENTRAL          3

Indicates initial point strategy for x, slacks and multipliers when using a barrier algorithm. Note, this option only alters the initial x values if the user does not specify an initial x.

This option has no effect on the Active Set algorithm.

  • 0 (auto) Let Knitro automatically choose the strategy.

  • 1 (convex) Initialization designed for convex models.

  • 2 (nearbnd) Initialization strategy that stays closer to the bounds.

  • 3 (central) Initialization strategy that is more central on double-bounded variables.

Default value: 0

type bar_linsys
type KN_PARAM_BAR_LINSYS
#define KN_PARAM_BAR_LINSYS           1126
#  define KN_BAR_LINSYS_AUTO            -1
#  define KN_BAR_LINSYS_FULL             0
#  define KN_BAR_LINSYS_ELIMINATE_SLACKS 1
#  define KN_BAR_LINSYS_ELIMINATE_BOUNDS 2
#  define KN_BAR_LINSYS_ELIMINATE_INEQS  3

Indicates which linear system form is used inside the Interior/Direct algorithm for computing primal-dual steps. Eliminating more elements results in a smaller dimensional linear system (but also one that is, perhaps, less numerically stable). The bounds option may be preferable for very large problems with many bounded variables. The ineq option may generate significant speedups on models where the number of variables is small, but the number of inequality constraints is large.

  • -1 (auto) Let Knitro automatically choose the linear system form.

  • 0 (full) Use the full linear system.

  • 1 (slacks) Eliminate the slack variables.

  • 2 (bounds) Eliminate the slack variables and bounds.

  • 3 (ineqs) Eliminate the slack variables, bounds, and some inequalities.

Default value: -1

type bar_linsys_storage
type KN_PARAM_BAR_LINSYS_STORAGE
#define KN_PARAM_BAR_LINSYS_STORAGE   1129
#  define KN_BAR_LINSYS_STORAGE_AUTO    -1
#  define KN_BAR_LINSYS_STORAGE_LOWMEM   1
#  define KN_BAR_LINSYS_STORAGE_NORMAL   2

Indicates how to store in memory the linear systems used inside the Interior/Direct algorithm for computing primal-dual steps. The lowmem option uses one storage location for multiple linear systems. As a result it may use much less memory, but also may be less efficient when the Interior/Direct algorithm takes a lot of CG steps. The normal option uses separate storage for different linear systems.

  • -1 (auto) Let Knitro automatically choose the linear system storage approach.

  • 1 (lowmem) Use common storage for multiple linear systems.

  • 2 (normal) Use separate storage for different linear systems.

Default value: -1

type bar_maxcorrectors
type KN_PARAM_BAR_MAXCORRECTORS
#define KN_PARAM_BAR_MAXCORRECTORS    1117

Specifies the maximum number of corrector steps allowed for primal-dual steps.

If the value is positive and the algorithm used is Interior/Direct, then Knitro may add at most bar_maxcorrectors corrector steps to the primal-dual step to try to stay closer to the central path. This may speedup convergence on some models (although it may make the cost per iteration a little more expensive). If the value is negative, Knitro automatically determines the maximum number of corrector steps to apply.

Default value: -1

type bar_maxcrossit
type KN_PARAM_BAR_MAXCROSSIT
#define KN_PARAM_BAR_MAXCROSSIT       1039

Specifies the maximum number of crossover iterations before termination.

If the value is positive and the algorithm in operation is Interior/Direct or Interior/CG, then Knitro will crossover to the Active Set algorithm near the solution. The Active Set algorithm will then perform at most bar_maxcrossit iterations to get a more exact solution. If the value is 0, no Active Set crossover occurs and the interior-point solution is the final result.

If Active Set crossover is unable to improve the approximate interior-point solution, then Knitro will restore the interior-point solution. In some cases (especially on large-scale problems or difficult degenerate problems) the cost of the crossover procedure may be significant – for this reason, crossover is disabled by default. Enabling crossover generally provides a more accurate solution than Interior/Direct or Interior/CG.

Default value: 0

type bar_maxrefactor
type KN_PARAM_BAR_MAXREFACTOR
#define KN_PARAM_BAR_MAXREFACTOR      1043

Indicates the maximum number of refactorizations of the KKT system per iteration of the Interior/Direct algorithm before reverting to a CG step. If this value is set to -1, it will use a dynamic strategy.

These refactorizations are performed if negative curvature is detected in the model. Rather than reverting to a CG step, the Hessian matrix is modified in an attempt to make the subproblem convex and then the KKT system is refactorized. Increasing this value will make the Interior/Direct algorithm less likely to take CG steps. If the Interior/Direct algorithm is taking a large number of CG steps (as indicated by a positive value for “CGits” in the output), this may improve performance. This option has no effect on the Active Set algorithm.

Default value: -1

type bar_murule
type KN_PARAM_BAR_MURULE
#define KN_PARAM_BAR_MURULE           1004
#  define KN_BAR_MURULE_AUTOMATIC        0
#  define KN_BAR_MURULE_AUTO             0
#  define KN_BAR_MURULE_MONOTONE         1
#  define KN_BAR_MURULE_ADAPTIVE         2
#  define KN_BAR_MURULE_PROBING          3
#  define KN_BAR_MURULE_DAMPMPC          4
#  define KN_BAR_MURULE_FULLMPC          5
#  define KN_BAR_MURULE_QUALITY          6

Indicates which strategy to use for modifying the barrier parameter mu in the barrier algorithms.

Not all strategies are available for both barrier algorithms, as described below. This option has no effect on the Active Set algorithm.

  • 0 (auto) Let Knitro automatically choose the strategy.

  • 1 (monotone) Monotonically decrease the barrier parameter. Available for both barrier algorithms.

  • 2 (adaptive) Use an adaptive rule based on the complementarity gap to determine the value of the barrier parameter. Available for both barrier algorithms.

  • 3 (probing) Use a probing (affine-scaling) step to dynamically determine the barrier parameter. Available only for the Interior/Direct algorithm.

  • 4 (dampmpc) Use a Mehrotra predictor-corrector type rule to determine the barrier parameter, with safeguards on the corrector step. Available only for the Interior/Direct algorithm.

  • 5 (fullmpc) Use a Mehrotra predictor-corrector type rule to determine the barrier parameter, without safeguards on the corrector step. Available only for the Interior/Direct algorithm.

  • 6 (quality) Minimize a quality function at each iteration to determine the barrier parameter. Available only for the Interior/Direct algorithm.

Default value: 0

type bar_penaltycons
type KN_PARAM_BAR_PENCONS
#define KN_PARAM_BAR_PENCONS          1050
#  define KN_BAR_PENCONS_AUTO           -1
#  define KN_BAR_PENCONS_NONE            0
#  define KN_BAR_PENCONS_ALL             2
#  define KN_BAR_PENCONS_EQUALITIES      3

Indicates whether a penalty approach is applied to the constraints.

Using a penalty approach may be helpful when the problem has degenerate or difficult constraints. It may also help to more quickly identify infeasible problems, or achieve feasibility in problems with difficult constraints.

This option has no effect on the Active Set algorithm.

  • -1 (auto) Let Knitro automatically choose the strategy.

  • 0 (none) No constraints are penalized.

  • 2 (all) A penalty approach is applied to all general constraints.

  • 3 (equalities) Apply a penalty approach to equality constraints only.

Default value: -1

type bar_penaltyrule
type KN_PARAM_BAR_PENRULE
#define KN_PARAM_BAR_PENRULE          1049
#  define KN_BAR_PENRULE_AUTO            0
#  define KN_BAR_PENRULE_SINGLE          1
#  define KN_BAR_PENRULE_FLEX            2

Indicates which penalty parameter strategy to use for determining whether or not to accept a trial iterate. This option has no effect on the Active Set algorithm.

  • 0 (auto) Let Knitro automatically choose the strategy.

  • 1 (single) Use a single penalty parameter in the merit function to weight feasibility versus optimality.

  • 2 (flex) Use a more tolerant and flexible step acceptance procedure based on a range of penalty parameter values.

Default value: 0

type bar_refinement
type KN_PARAM_BAR_REFINEMENT
#define KN_PARAM_BAR_REFINEMENT       1079
#  define KN_BAR_REFINEMENT_NO           0
#  define KN_BAR_REFINEMENT_YES          1

Specifies whether to try to refine the barrier solution for better precision. If enabled, once the optimality conditions are satisfied, Knitro will apply an additional refinement/postsolve phase to try to obtain more precision in the barrier solution. The effect is similar to the effect of enabling bar_maxcrossit, but it is usually much more efficient since it does not involve switching to the Active Set algorithm.

Default value: 0

type bar_relaxcons
type KN_PARAM_BAR_RELAXCONS
#define KN_PARAM_BAR_RELAXCONS        1077
#  define KN_BAR_RELAXCONS_NONE          0
#  define KN_BAR_RELAXCONS_EQS           1
#  define KN_BAR_RELAXCONS_INEQS         2
#  define KN_BAR_RELAXCONS_ALL           3

Indicates whether a relaxation approach is applied to the constraints.

Using a relaxation approach may be helpful when the problem has degenerate or difficult constraints.

This option has no effect on the Active Set algorithm.

  • 0 (none) No constraints are relaxed.

  • 1 (eqs) A relaxation approach is applied to general equality constraints.

  • 2 (ineqs) A relaxation approach is applied to general inequality constraints.

  • 3 (all) A relaxation approach is applied to all general constraints.

Default value: 2

type bar_slackboundpush
type KN_PARAM_BAR_SLACKBOUNDPUSH
#define KN_PARAM_BAR_SLACKBOUNDPUSH   1102

Specifies the amount by which the barrier slack variables are initially pushed inside the bounds. A smaller value may be preferable when warm-starting from a point close to the solution.

Default value: 1.0e-1

type bar_switchobj
type KN_PARAM_BAR_SWITCHOBJ
#define KN_PARAM_BAR_SWITCHOBJ        1104
#  define KN_BAR_SWITCHOBJ_NONE          0
#  define KN_BAR_SWITCHOBJ_SCALARPROX    1
#  define KN_BAR_SWITCHOBJ_DIAGPROX      2

Indicates which objective function to use when the barrier algorithms switch to a pure feasibility phase.

  • 0 (none) No (or zero) objective.

  • 1 (scalarprox) Proximal point objective with scalar weighting.

  • 2 (diagprox) Proximal point objective with diagonal weighting.

Default value: 1

type bar_switchrule
type KN_PARAM_BAR_SWITCHRULE
#define KN_PARAM_BAR_SWITCHRULE       1061
#  define KN_BAR_SWITCHRULE_AUTO        -1
#  define KN_BAR_SWITCHRULE_NEVER        0
#  define KN_BAR_SWITCHRULE_MODERATE     2
#  define KN_BAR_SWITCHRULE_AGGRESSIVE   3

Indicates whether or not the barrier algorithms will allow switching from an optimality phase to a pure feasibility phase. This option has no effect on the Active Set algorithm.

  • -1 (auto) Let Knitro determine the switching procedure.

  • 0 (never) Never switch to feasibility phase.

  • 2 (moderate) Allow switches to feasibility phase.

  • 3 (aggressive) Use a more aggressive switching rule.

Default value: -1

type bar_watchdog
type KN_PARAM_BAR_WATCHDOG
#define KN_PARAM_BAR_WATCHDOG         1089
#  define KN_BAR_WATCHDOG_NO             0
#  define KN_BAR_WATCHDOG_YES            1

Specifies whether to enable watchdog heuristic for barrier algorithms. In general, enabling the watchdog heuristic makes the barrier algorithms more likely to accept trial points. Specifically, the watchdog heuristic may occasionally accept trial points that increase the merit function, provided that subsequent iterates decrease the merit function.

Default value: 0

Active-set options

type act_lpalg
type KN_PARAM_ACT_LPALG
#define KN_PARAM_ACT_LPALG            1109
#  define KN_ACT_LPALG_DEFAULT           0
#  define KN_ACT_LPALG_PRIMAL            1
#  define KN_ACT_LPALG_DUAL              2
#  define KN_ACT_LPALG_BARRIER           3

Indicates which algorithm to use to solve linear programming (LP) subproblems when using the Knitro Active Set or SQP algorithms.

This option is currently only active when using the CPLEX(R) or Xpress(R) LP solvers chosen via act_lpsolver.

This option has no effect on the Interior/Direct and Interior/CG algorithms.

  • 0 (default) use the default algorithm for the chosen LP solver.

  • 1 (primal) use a primal simplex algorithm.

  • 2 (dual) use a dual simplex algorithm.

  • 3 (barrier) use a barrier/interior-point algorithm.

Default value: 0

type act_lpfeastol
type KN_PARAM_ACT_LPFEASTOL
#define KN_PARAM_ACT_LPFEASTOL        1098

Specifies the feasibility tolerance used for linear programming subproblems solved when using the Active Set or SQP algorithms.

Default value: 1.0e-8

type act_lppenalty
type KN_PARAM_ACT_LPPENALTY
#define KN_PARAM_ACT_LPPENALTY        1111
#  define KN_ACT_LPPENALTY_ALL           1
#  define KN_ACT_LPPENALTY_NONLINEAR     2
#  define KN_ACT_LPPENALTY_DYNAMIC       3

Indicates whether to use a penalty formulation for linear programming subproblems in the Knitro Active Set or SQP algorithms.

  • 1 (all) penalize all constraints.

  • 2 (nonlinear) penalize only nonlinear constraints.

  • 3 (dynamic) dynamically choose which constraints to penalize.

Default value: 1

type act_lppresolve
type KN_PARAM_ACT_LPPRESOLVE
#define KN_PARAM_ACT_LPPRESOLVE       1110
#  define KN_ACT_LPPRESOLVE_OFF          0
#  define KN_ACT_LPPRESOLVE_ON           1

Indicates whether to apply a presolve for linear programming subproblems in the Knitro Active Set or SQP algorithms.

  • 0 (off) presolve turned off for LP subproblems.

  • 1 (on) presolve turned on for LP subproblems.

Default value: 0

type act_lpsolver
type KN_PARAM_ACT_LPSOLVER
#define KN_PARAM_ACT_LPSOLVER         1012
#  define KN_ACT_LPSOLVER_INTERNAL       1
#  define KN_ACT_LPSOLVER_CPLEX          2
#  define KN_ACT_LPSOLVER_XPRESS         3

Indicates which linear programming simplex solver the Knitro Active Set or SQP algorithms use when solving internal LP subproblems.

This option has no effect on the Interior/Direct and Interior/CG algorithms.

  • 1 (internal) Knitro uses its default LP solver.

  • 2 (cplex) Knitro uses IBM ILOG-CPLEX(R), provided the user has a valid CPLEX license. The CPLEX library is loaded dynamically after KN_solve() is called.

  • 3 (xpress) Knitro uses the FICO Xpress(R) solver, provided the user has a valid Xpress license. The Xpress library is loaded dynamically after KN_solve() is called.

Default value: 1

If act_lpsolver = cplex then the CPLEX shared object library or DLL must reside in the operating system’s load path. If this option is selected, Knitro will automatically look for standard CPLEX library names in the system’s load path (in order of most recent releases starting with CPLEX 12.10).

To override the automatic search and load a particular CPLEX library, set its name with the character type user option cplexlibname. Either supply the full path name in this option, or make sure the library resides in a directory that is listed in the operating system’s load path. For example, to specifically load the Windows CPLEX library cplex123.dll, make sure the directory containing the library is part of the PATH environment variable, and call the following (also be sure to check the return status of this call):

KN_set_char_param_by_name (kc, "cplexlibname", "cplex123.dll");

If act_lpsolver = xpress then the Xpress shared object library or DLL must reside in the operating system’s load path. If this option is selected, Knitro will automatically look for the standard Xpress dll/shared library name.

To override the automatic search and load a particular Xpress library, set its name with the character type user option xpresslibname. Either supply the full path name in this option, or make sure the library resides in a directory that is listed in the operating system’s load path.

type act_parametric
type KN_PARAM_ACT_PARAMETRIC
#define KN_PARAM_ACT_PARAMETRIC       1107
#  define KN_ACT_PARAMETRIC_NO           0
#  define KN_ACT_PARAMETRIC_MAYBE        1
#  define KN_ACT_PARAMETRIC_YES          2

Indicates whether to use a parametric approach when solving linear programming (LP) subproblems when using the Knitro Active Set or SQP algorithms. A parametric approach will solve a sequence of closely related LPs instead of one LP. It may increase the cost of an active-set iteration, but perhaps lead to convergence in fewer iterations.

  • 0 (no) do not use a parametric solve (i.e. solve a single LP).

  • 1 (maybe) use a parametric solve sometimes.

  • 2 (yes) always try a parametric solve.

Default value: 1

type act_qpalg
type KN_PARAM_ACT_QPALG
#define KN_PARAM_ACT_QPALG            1092
#  define KN_ACT_QPALG_AUTO              0
#  define KN_ACT_QPALG_BAR_DIRECT        1
#  define KN_ACT_QPALG_BAR_CG            2
#  define KN_ACT_QPALG_ACT_CG            3

Indicates which algorithm to use to solve quadratic programming (QP) subproblems when using the Knitro Active Set or SQP algorithms.

This option has no effect on the Interior/Direct and Interior/CG algorithms.

  • 0 (auto) let Knitro automatically choose an algorithm, based on the problem characteristics.

  • 1 (direct) use the Interior/Direct algorithm.

  • 2 (cg) use the Interior/CG algorithm.

  • 3 (active) use the Active Set algorithm.

Default value: 0

type act_qppenalty
type KN_PARAM_ACT_QPPENALTY
#define KN_PARAM_ACT_QPPENALTY        1128
#  define KN_ACT_QPPENALTY_AUTO         -1
#  define KN_ACT_QPPENALTY_NONE          0
#  define KN_ACT_QPPENALTY_ALL           1

Indicates whether to use a penalty formulation for quadratic programming subproblems in the Knitro SQP algorithm.

  • -1 (auto) let Knitro automatically decide.

  • 0 (none) do not penalize constraints in QP subproblems.

  • 1 (all) penalize all constraints in QP subproblems.

Default value: -1

type cplexlibname
type KN_PARAM_CPLEXLIB
#define KN_PARAM_CPLEXLIB             1048

See option act_lpsolver.

type xpresslibname
type KN_PARAM_XPRESSLIB
#define KN_PARAM_XPRESSLIB            1069

See option act_lpsolver.

MIP options

type mip_branchrule
type KN_PARAM_MIP_BRANCHRULE
#define KN_PARAM_MIP_BRANCHRULE       2002
#  define KN_MIP_BRANCH_AUTO             0
#  define KN_MIP_BRANCH_MOSTFRAC         1
#  define KN_MIP_BRANCH_PSEUDOCOST       2
#  define KN_MIP_BRANCH_STRONG           3

Specifies which branching rule to use for MIP branch and bound procedure.

  • 0 (auto) Let Knitro automatically choose the branching rule.

  • 1 (most_frac) Use most fractional (most infeasible) branching.

  • 2 (pseudcost) Use pseudo-cost branching.

  • 3 (strong) Use strong branching (see options mip_strong_candlim, mip_strong_level and mip_strong_maxit for further control of strong branching procedure).

Default value: 0

type mip_clique
type KN_PARAM_MIP_CLIQUE
#define KN_PARAM_MIP_CLIQUE           2038
#  define KN_MIP_CLIQUE_NONE             0
#  define KN_MIP_CLIQUE_ROOT             1
#  define KN_MIP_CLIQUE_TREE             2
#  define KN_MIP_CLIQUE_ALL              3

Specifies rules for adding clique cuts.

  • 0 (none) Do not add clique cuts.

  • 1 (root) Add cuts derived from equalities at the root node only.

  • 2 (tree) Add cuts derived from equalities at every node depending on the solution of the relaxation and the cut generation strategy.

  • 3 (all) Add cuts derived from equalities combining both root and tree cut generations.

Default value: 0

type mip_cutfactor
type KN_PARAM_MIP_CUTFACTOR
#define KN_PARAM_MIP_CUTFACTOR        2035

This value specifies a limit on the number of cuts added to a node subproblem. If non-negative, a maximum of mip_cutfactor times the number of constraints is possibly appended.

Default value: 1.0

type mip_cutoff
type KN_PARAM_MIP_CUTOFF
#define KN_PARAM_MIP_CUTOFF           2044

This value specifies the objective cutoff value for MIP.

Default value: KN_INFINITY

type mip_cutting_plane
type KN_PARAM_MIP_CUTTINGPLANE
#define KN_PARAM_MIP_CUTTINGPLANE     2043  /*-- Cutting Plane */
#  define KN_MIP_CUTTINGPLANE_NONE       0  /*--   NONE */
#  define KN_MIP_CUTTINGPLANE_ROOT       1  /*--   IN THE ROOT */

Specifies when to apply the cutting plane procedure.

  • 0 (none) No cutting plane procedure enabled.

  • 1 (root) Perform cutting plane procedure at the root node only.

Default value: 0

type mip_debug
type KN_PARAM_MIP_DEBUG
#define KN_PARAM_MIP_DEBUG            2013
#  define KN_MIP_DEBUG_NONE              0
#  define KN_MIP_DEBUG_ALL               1

Specifies debugging level for MIP solution.

  • 0 (none) No MIP debugging output created.

  • 1 (all) Write MIP debugging output to the file kdbg_mip.log.

Default value: 0

type mip_gub_branch
type KN_PARAM_MIP_GUB_BRANCH
#define KN_PARAM_MIP_GUB_BRANCH       2015  /*-- BRANCH ON GENERALIZED BOUNDS */
#  define KN_MIP_GUB_BRANCH_NO           0
#  define KN_MIP_GUB_BRANCH_YES          1

Specifies whether or not to branch on generalized upper bounds (GUBs).

  • 0 (no) Do not branch on GUBs.

  • 1 (yes) Allow branching on GUBs.

Default value: 0

type mip_heuristic_diving
type KN_PARAM_MIP_HEUR_DIVING
#define KN_PARAM_MIP_HEUR_DIVING      2042

Specifies whether or not to enable the MIP diving heuristic. This option is a bit-valued option where various diving heuristics can be enabled by activating the corresponding bit value as described below. Setting this option to -1 will use an automatic setting and setting the value to 0 will disable all diving heuristics. Otherwise, set this parameter value to the sum of the values for the individual diving heuristics you wish to enable. For example, to enable only the “fractional” and “linesearch” diving heuristics, you would set this option value to 9 (summing 1 for fractional and 8 for linesearch).

  • -1 (auto) Let Knitro determine automaticallyfrom mip_heuristic_strategy.

  • 1 (bit 0) Enable fractional diving heuristic.

  • 2 (bit 1) Enable vector length diving heuristic.

  • 4 (bit 2) Enable coefficient diving heuristic.

  • 8 (bit 3) Enable linesearch diving heuristic.

  • 16 (bit 4) Enable guided diving heuristic.

Default value: -1

type mip_heuristic_feaspump
type KN_PARAM_MIP_HEUR_FEASPUMP
#define KN_PARAM_MIP_HEUR_FEASPUMP    2040
#  define KN_MIP_HEUR_FEASPUMP_AUTO     -1
#  define KN_MIP_HEUR_FEASPUMP_OFF       0
#  define KN_MIP_HEUR_FEASPUMP_ON        1

Specifies whether or not to enable the MIP feasibility pump heuristic.

  • -1 (auto) Let Knitro determine automatically from mip_heuristic_strategy.

  • 0 (off) Feasibility pump heuristic is not applied.

  • 1 (on) Feasibility pump heuristic is enabled.

Default value: -1

type mip_heuristic_lns
type KN_PARAM_MIP_HEUR_LNS
#define KN_PARAM_MIP_HEUR_LNS         2045

Specifies whether or not to enable the MIP large neighborhood search (LNS) heuristics. This option is a bit-valued option where various LNS heuristics can be enabled by activating the corresponding bit value as described below. Setting this option to -1 will use an automatic setting and setting the value to 0 will disable all LNS heuristics. Otherwise, set this parameter value to the sum of the values for the individual LNS heuristics you wish to enable. For example, to enable both the “RENS” and “RINS” LNS heuristics, you would set this option value to 3 (summing 1 for RENS and 2 for RINS).

  • -1 (auto) Let Knitro determine automatically from mip_heuristic_strategy.

  • 1 (bit 0) Enable relaxation enforced neighborhood search (RENS) heuristic.

  • 2 (bit 1) Enable relaxation induced neighborhood search (RINS) heuristic.

Default value: -1

type mip_heuristic_maxit
type KN_PARAM_MIP_HEURISTIC_MAXIT
#define KN_PARAM_MIP_HEUR_MAXIT       2023

Specifies the maximum number of iterations to allow for MIP heuristic, if one is enabled.

Default value: 100

type mip_heuristic_mpec
type KN_PARAM_MIP_HEUR_MPEC
#define KN_PARAM_MIP_HEUR_MPEC        2041
#  define KN_MIP_HEUR_MPEC_AUTO         -1
#  define KN_MIP_HEUR_MPEC_OFF           0
#  define KN_MIP_HEUR_MPEC_ON            1

Specifies whether or not to enable the MIP MPEC heuristic.

  • -1 (auto) Let Knitro determine automatically from mip_heuristic_strategy.

  • 0 (off) MPEC heuristic is not applied.

  • 1 (on) MPEC heuristic is enabled.

Default value: -1

type mip_heuristic_strategy
type KN_PARAM_MIP_HEUR_STRATEGY
#define KN_PARAM_MIP_HEUR_STRATEGY    2039
#  define KN_MIP_HEUR_STRATEGY_AUTO     -1
#  define KN_MIP_HEUR_STRATEGY_NONE      0
#  define KN_MIP_HEUR_STRATEGY_BASIC     1
#  define KN_MIP_HEUR_STRATEGY_ADVANCED  2
#  define KN_MIP_HEUR_STRATEGY_EXTENSIVE 3

Specifies the level of effort applied for the MIP heuristic search used to try to find an initial integer feasible point.

  • -1 (auto) Let Knitro choose the heuristic strategy to apply (if any).

  • 0 (none) No heuristic search applied.

  • 1 (basic) Apply basic heuristics.

  • 2 (advanced) Apply more advanced heuristics.

  • 3 (extensive) Apply most extensive heuristics.

Default value: -1

type mip_heuristic_terminate
type KN_PARAM_MIP_HEUR_TERMINATE
#define KN_PARAM_MIP_HEUR_TERMINATE   2033
#  define KN_MIP_HEUR_TERMINATE_FEASIBLE 1
#  define KN_MIP_HEUR_TERMINATE_LIMIT    2

Specifies the condition for terminating the MIP heuristic.

  • 1 (feasible) Terminate at first feasible point or iteration limit (whichever comes first).

  • 2 (limit) Always run to the iteration limit.

Default value: 1

type mip_implications
type KN_PARAM_MIP_IMPLICATIONS
#define KN_PARAM_MIP_IMPLICATIONS     2014  /*-- USE LOGICAL IMPLICATIONS */
#  define KN_MIP_IMPLICATIONS_NO         0
#  define KN_MIP_IMPLICATIONS_YES        1

Specifies whether or not to add constraints to the MIP derived from logical implications.

  • 0 (no) Do not add constraints from logical implications.

  • 1 (yes) Knitro adds constraints from logical implications.

Default value: 1

type mip_integer_tol
type KN_PARAM_MIP_INTEGERTOL
#define KN_PARAM_MIP_INTEGERTOL       2009

This value specifies the threshold for deciding whether or not a variable is determined to be an integer.

Default value: 1.0e-8

type mip_intvar_strategy
type KN_PARAM_MIP_INTVAR_STRATEGY
#define KN_PARAM_MIP_INTVAR_STRATEGY  2030
#  define KN_MIP_INTVAR_STRATEGY_NONE    0
#  define KN_MIP_INTVAR_STRATEGY_RELAX   1
#  define KN_MIP_INTVAR_STRATEGY_MPEC    2

Specifies how to handle integer variables.

  • 0 (none) No special treatment applied.

  • 1 (relax) Relax all integer variables.

  • 2 (mpec) Convert all binary variables to complementarity constraints.

Default value: 0

type mip_knapsack
type KN_PARAM_MIP_KNAPSACK
#define KN_PARAM_MIP_KNAPSACK         2016  /*-- KNAPSACK CUTS */
#  define KN_MIP_KNAPSACK_NO             0  /*--   NONE */
#  define KN_MIP_KNAPSACK_INEQ           1  /*--   INEQUALITIES */
#  define KN_MIP_KNAPSACK_LIFTED         2  /*--   LIFTED */
#  define KN_MIP_KNAPSACK_ALL            3  /*--   INEQUALITIES + LIFTED */

Specifies rules for adding MIP knapsack cuts.

  • 0 (none) Do not add knapsack cuts.

  • 1 (ineqs) Add knapsack cuts derived from linear (in)equalities at the root node only.

  • 2 (lifted) Add lifted separation cuts derived at every node from the solution of the relaxation.

  • 3 (all) Add both root node cuts from (in)equalities and lifted separation cuts.

Default value: 1

type mip_liftproject
type KN_PARAM_MIP_LIFTPROJECT
#define KN_PARAM_MIP_LIFTPROJECT      2047  /*-- LIFT&PROJECT CUTS */
#  define KN_MIP_LIFTPROJECT_NONE        0  /*--   NONE */
#  define KN_MIP_LIFTPROJECT_ROOT        1  /*--   IN THE ROOT */

Specifies rules for adding lift and project cuts.

  • 0 (none) Do not add lift and project cuts.

  • 1 (root) Add lift and project cuts at the root node only.

Default value: 0

type mip_lpalg
type KN_PARAM_MIP_LPALG
#define KN_PARAM_MIP_LPALG            2019
#  define KN_MIP_LPALG_AUTO              0
#  define KN_MIP_LPALG_BAR_DIRECT        1
#  define KN_MIP_LPALG_BAR_CG            2
#  define KN_MIP_LPALG_ACT_CG            3

Specifies which algorithm to use for any linear programming (LP) subproblem solves that may occur in the MIP branch and bound procedure.

LP subproblems may arise if the problem is a mixed integer linear program (MILP), or if using mip_method = HQG. (Nonlinear programming subproblems use the algorithm specified by the algorithm option.)

  • 0 (auto) Let Knitro automatically choose an algorithm, based on the problem characteristics.

  • 1 (direct) Use the Interior/Direct (barrier) algorithm.

  • 2 (cg) Use the Interior/CG (barrier) algorithm.

  • 3 (active) Use the Active Set (simplex) algorithm.

Default value: 0

type mip_maxnodes
type KN_PARAM_MIP_MAXNODES
#define KN_PARAM_MIP_MAXNODES         2021

Specifies the maximum number of nodes explored (0 means no limit).

Default value: 0

type mip_maxsolves
type KN_PARAM_MIP_MAXSOLVES
#define KN_PARAM_MIP_MAXSOLVES        2008

Specifies the maximum number of subproblem solves allowed (0 means no limit).

Default value: 0

type mip_maxtime_cpu
type KN_PARAM_MIP_MAXTIMECPU
#define KN_PARAM_MIP_MAXTIMECPU       2006

Specifies the maximum allowable CPU time in seconds for the complete MIP solution.

Use maxtime_cpu to additionally limit time spent per subproblem solve.

Default value: 1.0e8

type mip_maxtime_real
type KN_PARAM_MIP_MAXTIMEREAL
#define KN_PARAM_MIP_MAXTIMEREAL      2007

Specifies the maximum allowable real time in seconds for the complete MIP solution.

Use maxtime_real to additionally limit time spent per subproblem solve.

Default value: 1.0e8

type mip_method
type KN_PARAM_MIP_METHOD
#define KN_PARAM_MIP_METHOD           2001
#  define KN_MIP_METHOD_AUTO             0
#  define KN_MIP_METHOD_BB               1
#  define KN_MIP_METHOD_HQG              2
#  define KN_MIP_METHOD_MISQP            3

Specifies which MIP method to use.

  • 0 (auto) Let Knitro automatically choose the method.

  • 1 (BB) Use the standard branch and bound method.

  • 2 (HQG) Use the hybrid Quesada-Grossman method (for convex, nonlinear problems only).

  • 3 (MISQP) Use mixed-integer SQP method (allows for non-relaxable integer variables).

Default value: 0

type mip_mir
type KN_PARAM_MIP_MIR
#define KN_PARAM_MIP_MIR             2037
#  define KN_MIP_MIR_AUTO               -1
#  define KN_MIP_MIR_NONE                0
#  define KN_MIP_MIR_TREE                1
#  define KN_MIP_MIR_NLP                 2

Specifies rules for adding mixed integer rounding cuts.

  • -1 (auto) Let Knitro decide whether to add mixed integer rounding cuts.

  • 0 (none) Do not add mixed integer rounding cuts.

  • 1 (all) Add cuts derived from equalities at every node depending on the solution of the relaxation and the cut generation strategy.

  • 2 (nlp) Add mixed-integer rounding cuts at the root node and for nonlinear constraints.

Default value: -1

type mip_multistart
type KN_PARAM_MIP_MULTISTART
#define KN_PARAM_MIP_MULTISTART       2046
#  define KN_MIP_MULTISTART_OFF          0
#  define KN_MIP_MULTISTART_ON           1

Use to enable MIP multi-start at the branch-and-bound level.

  • 0 (off) Do not enable MIP multi-start for branch-and-bound.

  • 1 (on) Enable MIP multi-start for branch-and-bound.

Default value: 0

type mip_nodealg
type KN_PARAM_MIP_NODEALG
#define KN_PARAM_MIP_NODEALG          2032
#  define KN_MIP_NODEALG_AUTO            0
#  define KN_MIP_NODEALG_BAR_DIRECT      1
#  define KN_MIP_NODEALG_BAR_CG          2
#  define KN_MIP_NODEALG_ACT_CG          3
#  define KN_MIP_NODEALG_ACT_SQP         4
#  define KN_MIP_NODEALG_MULTI           5

Specifies which algorithm to use for standard node subproblem solves in MIP (same options as algorithm user option).

Default value: 0

type mip_numthreads
type KN_PARAM_MIP_NUMTHREADS
#define KN_PARAM_MIP_NUMTHREADS       2048

Specify the number of threads to use for MIP branch and bound (when mip_method = 1).

  • 0 Let Knitro choose the number of threads.

  • n>0 Use n threads for the MIP branch and bound.

Default value: 0

type mip_opt_gap_abs
type KN_PARAM_MIP_OPTGAPABS
#define KN_PARAM_MIP_OPTGAPABS        2004

The absolute optimality gap stop tolerance for MIP.

Default value: 1.0e-6

type mip_opt_gap_rel
type KN_PARAM_MIP_OPTGAPREL
#define KN_PARAM_MIP_OPTGAPREL        2005

The relative optimality gap stop tolerance for MIP.

Default value: 1.0e-4

type mip_outinterval
type KN_PARAM_MIP_OUTINTERVAL
#define KN_PARAM_MIP_OUTINTERVAL      2011

Specifies node printing interval for mip_outlevel when mip_outlevel > 0.

  • 1 Print output every node.

  • 2 Print output every 2nd node.

  • N Print output every Nth node.

Default value: 10

type mip_outlevel
type KN_PARAM_MIP_OUTLEVEL
#define KN_PARAM_MIP_OUTLEVEL         2010
#  define KN_MIP_OUTLEVEL_NONE           0
#  define KN_MIP_OUTLEVEL_ITERS          1
#  define KN_MIP_OUTLEVEL_ITERSTIME      2
#  define KN_MIP_OUTLEVEL_ROOT           3

Specifies how much MIP information to print.

  • 0 (none) Do not print any MIP node information.

  • 1 (iters) Print one line of output for every node.

  • 2 (iterstime) Also print accumulated time for every node.

  • 3 (root) Also print detailed log from root node solve.

Default value: 2

type mip_outsub
type KN_PARAM_MIP_OUTSUB
#define KN_PARAM_MIP_OUTSUB           2012
#  define KN_MIP_OUTSUB_NONE             0
#  define KN_MIP_OUTSUB_YES              1
#  define KN_MIP_OUTSUB_YESPROB          2

Specifies MIP subproblem solve debug output control. This output is only produced if mip_debug = 1 and appears in the file kdbg_mip.log.

  • 0 Do not print any debug output from subproblem solves.

  • 1 Subproblem debug output enabled, controlled by option outlev.

  • 2 Subproblem debug output enabled and print problem characteristics.

Default value: 0

type mip_pseudoinit
type KN_PARAM_MIP_PSEUDOINIT
#define KN_PARAM_MIP_PSEUDOINIT       2026
#  define KN_MIP_PSEUDOINIT_AUTO         0
#  define KN_MIP_PSEUDOINIT_AVE          1
#  define KN_MIP_PSEUDOINIT_STRONG       2

Specifies the method used to initialize pseudo-costs corresponding to variables that have not yet been branched on in the MIP method.

  • 0 Let Knitro automatically choose the method.

  • 1 Initialize using the average value of computed pseudo-costs.

  • 2 Initialize using strong branching.

Default value: 0

type mip_relaxable
type KN_PARAM_MIP_RELAXABLE
#define KN_PARAM_MIP_RELAXABLE        2031
#  define KN_MIP_RELAXABLE_NONE          0
#  define KN_MIP_RELAXABLE_ALL           1

Specifies Whether integer variables are relaxable.

  • 0 (none) Integer variables are not relaxable.

  • 1 (all) All integer variables are relaxable.

Default value: 1

type mip_rootalg
type KN_PARAM_MIP_ROOTALG
#define KN_PARAM_MIP_ROOTALG          2018
#  define KN_MIP_ROOTALG_AUTO            0
#  define KN_MIP_ROOTALG_BAR_DIRECT      1
#  define KN_MIP_ROOTALG_BAR_CG          2
#  define KN_MIP_ROOTALG_ACT_CG          3
#  define KN_MIP_ROOTALG_ACT_SQP         4
#  define KN_MIP_ROOTALG_MULTI           5

Specifies which algorithm to use for the root node solve in MIP (same options as algorithm user option).

Default value: 0

type mip_rounding
type KN_PARAM_MIP_ROUNDING
#define KN_PARAM_MIP_ROUNDING         2017
#  define KN_MIP_ROUND_AUTO             -1
#  define KN_MIP_ROUND_NONE              0  /*-- DO NOT ATTEMPT ROUNDING */
#  define KN_MIP_ROUND_HEURISTIC         2  /*-- USE FAST HEURISTIC */
#  define KN_MIP_ROUND_NLP_SOME          3  /*-- SOLVE NLP IF LIKELY TO WORK */
#  define KN_MIP_ROUND_NLP_ALWAYS        4  /*-- SOLVE NLP ALWAYS */

Specifies the MIP rounding rule to apply.

  • -1 (auto) Let Knitro choose the rounding rule.

  • 0 (none) No rounding heuristic is used.

  • 2 (heur_only) Round using a fast heuristic only.

  • 3 (nlp_sometimes) Round and solve a subproblem if likely to succeed.

  • 4 (nlp_always) Always round and solve a subproblem.

Default value: -1

type mip_selectdir
type KN_PARAM_MIP_SELECTDIR
#define KN_PARAM_MIP_SELECTDIR        2034
#  define KN_MIP_SELECTDIR_DOWN          0
#  define KN_MIP_SELECTDIR_UP            1

Specifies the MIP node selection direction rule (for tiebreakers) for choosing the next node in the branch and bound tree.

  • 0 (down) Choose the down (i.e. <=) node first.

  • 1 (up) Choose the up (i.e. >=) node first.

Default value: 0

type mip_selectrule
type KN_PARAM_MIP_SELECTRULE
#define KN_PARAM_MIP_SELECTRULE       2003
#  define KN_MIP_SEL_AUTO                0
#  define KN_MIP_SEL_DEPTHFIRST          1
#  define KN_MIP_SEL_BESTBOUND           2
#  define KN_MIP_SEL_COMBO_1             3

Specifies the MIP select rule for choosing the next node in the branch and bound tree.

  • 0 (auto) Let Knitro choose the node selection rule.

  • 1 (depth_first) Search the tree using a depth first procedure.

  • 2 (best_bound) Select the node with the best relaxation bound.

  • 3 (combo_1) Use depth first unless pruned, then best bound.

Default value: 0

type mip_strong_candlim
type KN_PARAM_MIP_STRONG_CANDLIM
#define KN_PARAM_MIP_STRONG_CANDLIM   2028

Specifies the maximum number of candidates to explore for MIP strong branching.

Default value: 10

type mip_strong_level
type KN_PARAM_MIP_STRONG_LEVEL
#define KN_PARAM_MIP_STRONG_LEVEL     2029

Specifies the maximum number of tree levels on which to perform MIP strong branching.

Default value: 10

type mip_strong_maxit
type KN_PARAM_MIP_STRONG_MAXIT
#define KN_PARAM_MIP_STRONG_MAXIT     2027

Specifies the maximum number of iterations to allow for MIP strong branching solves.

Default value: 1000

type mip_terminate
type KN_PARAM_MIP_TERMINATE
#define KN_PARAM_MIP_TERMINATE        2020
#  define KN_MIP_TERMINATE_OPTIMAL       0
#  define KN_MIP_TERMINATE_FEASIBLE      1

Specifies conditions for terminating the MIP algorithm.

  • 0 (optimal) Terminate at optimum.

  • 1 (feasible) Terminate at first integer feasible point.

Default value: 0

type mip_zerohalf
type KN_PARAM_MIP_ZEROHALF
#define KN_PARAM_MIP_ZEROHALF           2036
#  define KN_MIP_ZEROHALF_NONE             0
#  define KN_MIP_ZEROHALF_ROOT             1
#  define KN_MIP_ZEROHALF_TREE             2
#  define KN_MIP_ZEROHALF_ALL              3

Specifies rules for adding zero-half cuts.

  • 0 (none) Do not add zero-half cuts.

  • 1 (root) Add cuts derived from equalities at the root node only.

  • 2 (tree) Add cuts derived from equalities at every node depending on the solution of the relaxation and the cut generation strategy.

  • 3 (all) Add cuts derived from equalities combining both root and tree cut generations.

Default value: 0

Multi-algorithm options

type ma_maxtime_cpu
type KN_PARAM_MA_MAXTIMECPU
#define KN_PARAM_MA_MAXTIMECPU        1064

Specifies, in seconds, the maximum allowable CPU time before termination for the multi-algorithm (“MA”) procedure (alg=5).

Default value: 1.0e8

type ma_maxtime_real
type KN_PARAM_MA_MAXTIMEREAL
#define KN_PARAM_MA_MAXTIMEREAL       1065

Specifies, in seconds, the maximum allowable real time before termination for the multi-algorithm (“MA”) procedure (alg=5).

Default value: 1.0e8

Note

When using the multi-algorithm procedure, the options maxtime_cpu and maxtime_real control time limits for the individual algorithms, while ma_maxtime_cpu and ma_maxtime_real impose time limits for the overall procedure.

type ma_outsub
type KN_PARAM_MA_OUTSUB
#define KN_PARAM_MA_OUTSUB            1067
#  define KN_MA_OUTSUB_NONE              0
#  define KN_MA_OUTSUB_YES               1

Enable writing algorithm output to files for the multi-algorithm (alg=5) procedure.

  • 0 Do not write detailed algorithm output to files.

  • 1 Write detailed algorithm output to files named knitro_ma_*.log.

Default value: 0

type ma_terminate
type KN_PARAM_MA_TERMINATE
#define KN_PARAM_MA_TERMINATE         1063
#  define KN_MA_TERMINATE_ALL            0
#  define KN_MA_TERMINATE_OPTIMAL        1
#  define KN_MA_TERMINATE_FEASIBLE       2
#  define KN_MA_TERMINATE_ANY            3

Define the termination condition for the multi-algorithm (alg=5) procedure.

  • 0 Terminate after all algorithms have completed.

  • 1 Terminate at first locally optimal solution.

  • 2 Terminate at first feasible solution estimate.

  • 3 Terminate at first solution estimate of any type.

Default value: 1

Multi-Start options

type ms_deterministic
type KN_PARAM_MS_DETERMINISTIC
#define KN_PARAM_MS_DETERMINISTIC     1078
#  define KN_MS_DETERMINISTIC_NO         0
#  define KN_MS_DETERMINISTIC_YES        1

Indicates whether Knitro multi-start procedure will be deterministic (when ms_terminate = 0).

  • 0 (no) multithreaded multi-start is non-deterministic.

  • 1 (yes) multithreaded multi-start is deterministic (when ms_terminate = 0).

Default value: 1

type ms_enable
type KN_PARAM_MS_ENABLE
#define KN_PARAM_MULTISTART           1033
#define KN_PARAM_MS_ENABLE            1033
#  define KN_MS_ENABLE_NO                0
#  define KN_MS_ENABLE_YES               1

Indicates whether Knitro will solve from multiple start points to find a better local minimum.

  • 0 (no) Knitro solves from a single initial point.

  • 1 (yes) Knitro solves using multiple start points.

Default value: 0

type ms_maxbndrange
type KN_PARAM_MS_MAXBNDRANGE
#define KN_PARAM_MS_MAXBNDRANGE       1035

Specifies the maximum range that an unbounded variable can take when determining new start points.

If a variable is unbounded in one or both directions, then new start point values are restricted by the option. If x_i is such a variable, then all initial values satisfy

\max \{ b^L_i, x_i^0 - {\tt ms\_maxbndrange}/2 \}
\leq x_i
\leq \min \{ b^U_i, x_i^0 + {\tt ms\_maxbndrange}/2 \},

where x_i^0 is the initial value of x_i provided by the user, and b^L_i and b^U_i are the variable bounds (possibly infinite) on x_i. This option has no effect unless ms_enable = yes.

Default value: 1000.0

type ms_maxsolves
type KN_PARAM_MS_MAXSOLVES
#define KN_PARAM_MS_MAXSOLVES         1034

Specifies how many start points to try in multi-start. This option has no effect unless ms_enable = yes.

  • 0 Let Knitro automatically choose a value based on the problem size. The value is min(200, 10 N), where N is the number of variables in the problem.

  • n Try n>0 start points.

Default value: 0

type ms_maxtime_cpu
type KN_PARAM_MS_MAXTIMECPU
#define KN_PARAM_MS_MAXTIMECPU        1036

Specifies, in seconds, the maximum allowable CPU time before termination.

The limit applies to the operation of Knitro since multi-start began; in contrast, the value of maxtime_cpu limits how long Knitro iterates from a single start point. Therefore, ms_maxtime_cpu should be greater than maxtime_cpu. This option has no effect unless ms_enable = yes.

Default value: 1.0e8

type ms_maxtime_real
type KN_PARAM_MS_MAXTIMEREAL
#define KN_PARAM_MS_MAXTIMEREAL       1037

Specifies, in seconds, the maximum allowable real time before termination.

The limit applies to the operation of Knitro since multi-start began; in contrast, the value of maxtime_real limits how long Knitro iterates from a single start point. Therefore, ms_maxtime_real should be greater than maxtime_real. This option has no effect unless ms_enable = yes.

Default value: 1.0e8

type ms_num_to_save
type KN_PARAM_MS_NUMTOSAVE
#define KN_PARAM_MS_NUMTOSAVE         1051

Specifies the number of distinct feasible points to save in a file named knitro_mspoints.log.

Each point results from a Knitro solve from a different starting point, and must satisfy the absolute and relative feasibility tolerances. The file stores points in order from best objective to worst. Points are distinct if they differ in objective value or some component by the value of ms_savetol using a relative tolerance test. This option has no effect unless ms_enable = yes.

Default value: 0

type ms_numthreads
type KN_PARAM_MS_NUMTHREADS
#define KN_PARAM_MS_NUMTHREADS        1137

Specify the number of threads to use for multi-start (when ms_enable = 1).

  • 0 Let Knitro choose the number of threads (currently sets ms_numthreads based on numthreads).

  • n>0 Use n threads for the multi-start (solve n problems in parallel).

Default value: 0

type ms_outsub
type KN_PARAM_MS_OUTSUB
#define KN_PARAM_MS_OUTSUB            1068
#  define KN_MS_OUTSUB_NONE              0
#  define KN_MS_OUTSUB_YES               1

Enable writing algorithm output to files for the parallel multi-start procedure.

  • 0 Do not write detailed algorithm output to files.

  • 1 Write detailed algorithm output to files named knitro_ms_*.log.

Default value: 0

type ms_savetol
type KN_PARAM_MS_SAVETOL
#define KN_PARAM_MS_SAVETOL           1052

Specifies the tolerance for deciding if two feasible points are distinct.

Points are distinct if they differ in objective value or some component by the value of ms_savetol using a relative tolerance test. A large value can cause the saved feasible points in the file knitro_mspoints.log to cluster around more widely separated points. This option has no effect unless ms_enable = yes. and ms_num_to_save is positive.

Default value: 1.0e-6

type ms_seed
type KN_PARAM_MS_SEED
#define KN_PARAM_MS_SEED              1066

Seed value used to generate random initial points in multi-start; should be a non-negative integer.

Default value: 0

type ms_startptrange
type KN_PARAM_MS_STARTPTRANGE
#define KN_PARAM_MS_STARTPTRANGE      1055

Specifies the maximum range that each variable can take when determining new start points.

If a variable has upper and lower bounds and the difference between them is less than or equal to ms_startptrange, then new start point values for the variable can be any number between its upper and lower bounds.

If the variable is unbounded in one or both directions, or the difference between bounds is greater than ms_startptrange, then new start point values are restricted by the option. If x_i is such a variable, then all initial values satisfy

\max \{ b^L_i, x_i^0 - \tau \}
\leq x_i
\leq \min \{ b^U_i, x_i^0 + \tau \},

\tau = \min \{ {\tt ms\_startptrange}/2, {\tt ms\_maxbndrange}/2 \}

where x_i^0 is the initial value of x_i provided by the user, and b^L_i and b^U_i are the variable bounds (possibly infinite) on x_i. This option has no effect unless ms_enable = yes.

Default value: 1.0e20

type ms_terminate
type KN_PARAM_MS_TERMINATE
#define KN_PARAM_MS_TERMINATE         1054
#  define KN_MS_TERMINATE_MAXSOLVES      0
#  define KN_MS_TERMINATE_OPTIMAL        1
#  define KN_MS_TERMINATE_FEASIBLE       2
#  define KN_MS_TERMINATE_ANY            3

Specifies the condition for terminating multi-start.

This option has no effect unless ms_enable = yes.

  • 0 Terminate after ms_maxsolves.

  • 1 Terminate after the first local optimal solution is found or ms_maxsolves, whichever comes first.

  • 2 Terminate after the first feasible solution estimate is found or ms_maxsolves, whichever comes first.

  • 3 Terminate after the first solution estimate of any type is found or ms_maxsolves, whichever comes first.

Default value: 0

Parallelism options

type blas_numthreads
type KN_PARAM_BLAS_NUMTHREADS
#define KN_PARAM_BLAS_NUMTHREADS      1135

Specify the number of threads to use for BLAS operations when blasoption = 1 (see Parallelism).

Default value: 0 (Knitro will automatically set blas_numthreads based on numthreads)

type concurrent_evals
type KN_PARAM_CONCURRENT_EVALS
#define KN_PARAM_CONCURRENT_EVALS     1134
#  define KN_CONCURRENT_EVALS_NO         0
#  define KN_CONCURRENT_EVALS_YES        1

Determines whether or not the user provided callback functions used for function and derivative evaluations can take place concurrently in parallel (for possibly different values of “x”). If it is not safe to have concurrent evaluations, then setting concurrent_evals=0, will put these evaluations in a critical region so that only one evaluation can take place at a time. If concurrent_evals=1 then concurrent evaluations are allowed when Knitro is run in parallel, and it is the responsibility of the user to ensure that these evaluations are stable. See Parallelism.

  • 0 (no) Do not allow concurrent callback evaluations.

  • 1 (yes) Allow concurrent callback evaluations.

Default value: 1

type conic_numthreads
type KN_PARAM_CONIC_NUMTHREADS
#define KN_PARAM_CONIC_NUMTHREADS     1138

Specify the number of threads to use for operations in the conic algorithm (when bar_conic_enable = 1).

  • 0 Let Knitro choose the number of threads (currently sets conic_numthreads based on numthreads).

  • n>0 Use n threads for the conic solver.

Default value: 0

type linsolver_numthreads
type KN_PARAM_LINSOLVER_NUMTHREADS
#define KN_PARAM_LINSOLVER_NUMTHREADS 1136

Specify the number of threads to use for linear system solve operations when linsolver = 6 (see Parallelism).

Default value: 0 (Knitro will automatically set linsolver_numthreads based on numthreads)

type numthreads
type KN_PARAM_NUMTHREADS
#define KN_PARAM_NUMTHREADS           1133

Specify the number of threads to use for parallel computing features (see Parallelism).

Default value: -1 (Knitro will automatically determine the number of threads to use and how to distribute them)

Output options

type debug
type KN_PARAM_DEBUG
#define KN_PARAM_DEBUG                1031
#  define KN_DEBUG_NONE                  0
#  define KN_DEBUG_PROBLEM               1
#  define KN_DEBUG_EXECUTION             2

Controls the level of debugging output.

Debugging output can slow execution of Knitro and should not be used in a production setting. All debugging output is suppressed if option outlev = 0.

  • 0 (none) No debugging output.

  • 1 (problem) Print algorithm information to kdbg*.log

    output files.

  • 2 (execution) Print program execution information.

Default value: 0

type newpoint
type KN_PARAM_NEWPOINT
#define KN_PARAM_NEWPOINT             1001
#  define KN_NEWPOINT_NONE               0
#  define KN_NEWPOINT_SAVEONE            1
#  define KN_NEWPOINT_SAVEALL            2

Specifies additional action to take after every iteration in a solve of a continuous problem.

An iteration of Knitro results in a new point that is closer to a solution. The new point includes values of x and Lagrange multipliers lambda. The “newpoint” feature in Knitro is currently only available for continuous problems (solved via KN_solve()).

  • 0 (none) Knitro takes no additional action.

  • 1 (saveone) Knitro writes x and lambda to the file knitro_newpoint.log. Previous contents of the file are overwritten.

  • 2 (saveall) Knitro appends x and lambda to the file knitro_newpoint.log. Warning: this option can generate a very large file. All iterates, including the start point, crossover points, and the final solution are saved. Each iterate also prints the objective value at the new point, except the initial start point.

Default value: 0

type out_csvinfo
type KN_PARAM_OUT_CSVINFO
#define KN_PARAM_OUT_CSVINFO          1096
#  define KN_OUT_CSVINFO_NO              0
#  define KN_OUT_CSVINFO_YES             1

Controls whether or not to generates a file knitro_solve.csv containing solve information in comma separated format.

  • 0 (no) No solution information file is generated.

  • 1 (yes) The knitro_solve.csv solution file is generated.

Default value: 0

type out_csvname
type KN_PARAM_OUT_CSVNAME
#define KN_PARAM_OUT_CSVNAME          1106

Use to specify a custom csv filename when using out_csvinfo.

Default value: knitro_solve.csv

type out_hints
type KN_PARAM_OUT_HINTS
#define KN_PARAM_OUT_HINTS            1115
#  define KN_OUT_HINTS_NO                0
#  define KN_OUT_HINTS_YES               1

Specifies whether to print diagnostic hints (e.g. about user option settings) after solving.

  • 0 (no) Do not print any hints.

  • 1 (yes) Print diagnostic hints on occasion.

Default value: 1

type outappend
type KN_PARAM_OUTAPPEND
#define KN_PARAM_OUTAPPEND            1046
#  define KN_OUTAPPEND_NO                0
#  define KN_OUTAPPEND_YES               1

Specifies whether output should be started in a new file, or appended to existing files.

The option affects knitro.log and files produced when debug = 1. It does not affect knitro_newpoint.log, which is controlled by option newpoint.

  • 0 (no) Erase any existing files when opening for output.

  • 1 (yes) Append output to any existing files.

Default value: 0

type outdir
type KN_PARAM_OUTDIR
#define KN_PARAM_OUTDIR               1047

Specifies a single directory as the location to write all output files.

The option should be a full pathname to the directory, and the directory must already exist.

type outlev
type KN_PARAM_OUTLEV
#define KN_PARAM_OUTLEV               1015
#  define KN_OUTLEV_NONE                 0
#  define KN_OUTLEV_SUMMARY              1
#  define KN_OUTLEV_ITER_10              2
#  define KN_OUTLEV_ITER                 3
#  define KN_OUTLEV_ITER_VERBOSE         4
#  define KN_OUTLEV_ITER_X               5
#  define KN_OUTLEV_ALL                  6

Controls the level of output produced by Knitro.

  • 0 (none) Printing of all output is suppressed.

  • 1 (summary) Print only summary information.

  • 2 (iter_10) Print basic information every 10 iterations.

  • 3 (iter) Print basic information at each iteration.

  • 4 (iter_verbose) Print basic information and the function count at each iteration.

  • 5 (iter_x) Print all the above, and the values of the solution vector x.

  • 6 (all) Print all the above, and the values of the constraints c at x and the Lagrange multipliers lambda.

Default value: 2

type outmode
type KN_PARAM_OUTMODE
#define KN_PARAM_OUTMODE              1016
#  define KN_OUTMODE_SCREEN              0
#  define KN_OUTMODE_FILE                1
#  define KN_OUTMODE_BOTH                2

Specifies where to direct the output from Knitro.

  • 0 (screen) Output is directed to standard out (e.g., screen).

  • 1 (file) Output is sent to a file named knitro.log.

  • 2 (both) Output is directed to both the screen and file knitro.log.

Default value: 0

type outname
type KN_PARAM_OUTNAME
#define KN_PARAM_OUTNAME              1105

Use to specify a custom filename when output is written to a file using outmode.

Default value: knitro.log

Tuner options

type tuner
type KN_PARAM_TUNER
#define KN_PARAM_TUNER                1070
    #  define KN_TUNER_OFF                   0
    #  define KN_TUNER_ON                    1

Indicates whether to invoke the Knitro-Tuner (see The Knitro-Tuner).

  • 0 (off) Do not invoke the Knitro-Tuner.

  • 1 (on) Invoke the Knitro-Tuner.

Default value: 0

type tuner_maxtime_cpu
type KN_PARAM_TUNER_MAXTIMECPU
#define KN_PARAM_TUNER_MAXTIMECPU     1072

Specifies, in seconds, the maximum allowable CPU time before terminating the Knitro-Tuner.

The limit applies to the operation of Knitro since the Knitro-Tuner began. In contrast, the value of maxtime_cpu places a time limit on each individual Knitro-Tuner solve for a particular option setting. Therefore, tuner_maxtime_cpu should be greater than maxtime_cpu. This option has no effect unless tuner = on.

Default value: 1.0e8

type tuner_maxtime_real
type KN_PARAM_TUNER_MAXTIMEREAL
#define KN_PARAM_TUNER_MAXTIMEREAL    1073

Specifies, in seconds, the maximum allowable real time before terminating the Knitro-Tuner.

The limit applies to the operation of Knitro since the Knitro-Tuner began. In contrast, the value of maxtime_real places a time limit on each individual Knitro-Tuner solve for a particular option setting. Therefore, tuner_maxtime_real should be greater than maxtime_real. This option has no effect unless tuner = on.

Default value: 1.0e8

type tuner_optionsfile
type KN_PARAM_TUNER_OPTIONSFILE
#define KN_PARAM_TUNER_OPTIONSFILE    1071

Can be used to specify the location of a Tuner options file (see The Knitro-Tuner).

Default value: NULL

type tuner_outsub
type KN_PARAM_TUNER_OUTSUB
#define KN_PARAM_TUNER_OUTSUB         1074
#  define KN_TUNER_OUTSUB_NONE           0
#  define KN_TUNER_OUTSUB_SUMMARY        1
#  define KN_TUNER_OUTSUB_ALL            2

Enable writing additional Tuner subproblem solve output to files for the Knitro-Tuner procedure (tuner=1).

  • 0 Do not write detailed solve output to files.

  • 1 Write summary solve output to a file named knitro_tuner_summary.log.

  • 2 Write detailed individual solve output to files named knitro_tuner_*.log.

Default value: 0

type tuner_terminate
type KN_PARAM_TUNER_TERMINATE
#define KN_PARAM_TUNER_TERMINATE      1075
#  define KN_TUNER_TERMINATE_ALL         0
#  define KN_TUNER_TERMINATE_OPTIMAL     1
#  define KN_TUNER_TERMINATE_FEASIBLE    2
#  define KN_TUNER_TERMINATE_ANY         3

Define the termination condition for the Knitro-Tuner procedure (tuner=1).

  • 0 Terminate after all solves have completed.

  • 1 Terminate at first locally optimal solution.

  • 2 Terminate at first feasible solution estimate.

  • 3 Terminate at first solution estimate of any type.

Default value: 0