Artelys releases Knitro 15.1, bringing a new wave of performance upgrades and usability improvements to help you solve large-scale optimization problems faster than ever. 

More Accessible Interfaces

• New Command-Line Interface:

The new command-line interface is available through an executable named “knitro“, which accepts MPS and LP formats as well as any Knitro user options, to easily test Knitro on your instances.

• New API Functions:

The new API functions allow to manipulate model characteristics simplifying updates and KPI generation.

• Support of Linux ARM64 architectures:

All supported platforms are available on our website, check the detailed list on our dedicated page.

More Accessible Interfaces

• New Command-Line Interface :

The new command-line interface is available through an executable named “knitro“, which accepts MPS and LP formats as well as any Knitro user options, to easily test Knitro on your instances.

• New API Functions:

The new API functions allow to manipulare model characteristics simplifying updates and KPI generation.

• Support of Linux ARM64 architectures:

All supported platforms are available on our website, check the detailed list on our dedicated page.

To learn more about Knitro 15.1 and its capabilities, visit the official documentation or review the detailed release notes.

Use-cases

Discover the use-cases that transform your ideas into practical realities, building a future where technology meets real needs in an exceptional way with Knitro.

Artelys is pleased to announce the release of Knitro 15.0, which provides new algorithms and performance improvements to solve your large-scale optimisation problems, whether linear or non-linear, more quickly

Improved Performance

Knitro 15.0 enhances the efficiency and robustness of existing optimisation methods:

• Faster Resolution of MILP and MINLP Problems:

The addition of new presolve operations allows for faster resolution of your mixed-integer programming models, particularly for non-linear problems (MINLP) with linear structures.

• Faster Hessian Approximation:

This version offers significant performance improvements for Hessian approximation with the limited-memory Broyden-Fletcher-Goldfarb-Shanno method, especially for large-scale problems.

• Use of Apple Accelerate Sparse Solvers Library:

Users of Mac-ARM platforms can now use Apple Accelerate’s specific solvers for sparse matrix equation systems, thereby improving performance on large-scale models.

Thanks to these improvements, Knitro 15.0 reduces solution times on continuous problems on our pooling and LP datasets by 46% and 14% respectively, compared with version 14.2. Our evaluation datasets are composed from academic and customer cases instances, with application in water networks, oil and gas, refinery processing, etc.

Knitro 15.0 is also more robust on discrete problems, with new instances solved on our MINLP convex dataset. On the latter, the primal-dual integral gap is reduced by more than 10%, demonstrating increased convergence speed and solution quality.

More Accessible Interfaces

• Python Interface Available on PyPI:

The installation process for Knitro’s Python interface has been completely revamped. It is now possible to install Knitro and its Python interface directly from PyPI, the Python package manager, greatly facilitating the use of our non-linear solver from this language.

• Java Interface Compatible with Mac ARM:

With this new version, Knitro’s Java interface is now available for users of Mac ARM platforms.

To learn more about Knitro 15.0 and its capabilities, visit the official documentation or review the detailed release notes.

Improved Performance

• Knitro 15.0 enhances the efficiency and robustness of existing optimisation methods:

  • Faster Resolution of MILP and MINLP Problems: The addition of new presolve operations allows for faster resolution of your mixed-integer programming models, particularly for non-linear problems (MINLP) with linear structures.
  • Faster Hessian Approximation: This version offers significant performance improvements for Hessian approximation with the limited-memory Broyden-Fletcher-Goldfarb-Shanno method, especially for large-scale problems.
  • Use of Apple Accelerate Sparse Solvers Library: Users of Mac-ARM platforms can now use Apple Accelerate’s specific solvers for sparse matrix equation systems, thereby improving performance on large-scale models.

  Thanks to these improvements, Knitro 15.0 reduces solution times on continuous problems on our pooling and LP datasets by 46% and 14% respectively, compared with version 14.2. Our evaluation datasets are composed from academic and customer cases instances, with application in water networks, oil and gas, refinery processing, etc.

  Knitro 15.0 is also more robust on discrete problems, with new instances solved on our MINLP convex dataset. On the latter, the primal-dual integral gap is reduced by more than 10%, demonstrating increased convergence speed and solution quality.

More Accessible Interfaces

• Python Interface Available on PyPI: The installation process for Knitro’s Python interface has been completely revamped. It is now possible to install Knitro and its Python interface directly from PyPI, the Python package manager, greatly facilitating the use of our non-linear solver from this language.

• Java Interface Compatible with Mac ARM: With this new version, Knitro’s Java interface is now available for users of Mac ARM platforms.

To learn more about Knitro 15.0 and its capabilities, visit the official documentation or review the detailed release notes.

Use-cases

Discover the use-cases that transform your ideas into practical realities, building a future where technology meets real needs in an exceptional way with Knitro.

This new release brings several enhancements designed to improve efficiency, robustness, and user control. This version includes both significant speed ups on non-convex models and several new features to ease the modeling and tuning of Artelys Knitro.

Read our Artelys Knitro use cases to find out more!

New Tools for solver settings and information retrieval

• Best Feasible Iterate Retrieval:
  Users can now retrieve the best feasible iterate found during optimization with dedicated API functions or via user options.
• File-Based Initialization:
  A new option allows users to specify a file for initializing optimization algorithms, simplifying setup for complex models.
• Enhanced Time Management
  The maximum time option now enables users to set a global time limit across single, multi-start, multi-algorithm, tuner, or MIP solves. Additional options offer precise time control over subproblem solves.

To learn more about Knitro 14.2 and its capabilities, visit the official documentation or review the detailed release notes.

Use-cases

Discover the use-cases that transform your ideas into practical realities, building a future where technology meets real needs in an exceptional way with Knitro.

We are excited to announce the release of Artelys Knitro version 14.1, our solver for nonlinear optimization, providing greater efficiency and precision across all problem classes from large linear models (LP) to numerically difficult mixed-integer nonlinear problems (MINLP).

This new version provides specific improvements on quadratic models (QP) and quadratically constrained quadratic models (QCQP), both convex and non-convex. Quadratic models arise in many business applications such as portfolio optimization in finance, model predictive control in chemistry or robotics, least square regressions or pooling problem in the Oil & Gas industry or water networks. Check our Artelys Knitro use cases to learn more!

Interfaces updates

Several interfaces provide new features to improve user friendliness and flexibility:

• AMPL interface now provides a better handling of partially specified initial points.
• Mixed MATLAB interface has been optimized to allow re-using expensive evaluations common to both the objective function and constraint functions.
• Julia interface now supports newer versions of Julia 1.10.2+.

For a detailed breakdown of Artelys Knitro 14.1 features and improvements, please refer to our release notes.

Use-cases

Discover the use-cases that transform your ideas into practical realities, building a future where technology meets real needs in an exceptional way with Knitro.

— We are pleased to announce that Artelys Knitro 14 is now available!

Artelys, a global leader in optimization solutions, is thrilled to announce the release of Artelys Knitro 14, the latest version of its flagship optimization software.

Artelys Knitro 14 marks a significant milestone in empowering organizations to solve complex nonlinear optimization challenges with unprecedented efficiency and accuracy.

Key Features of Artelys Knitro 14:

 1. Enhanced Performance: Artelys Knitro 14 is 26% faster on convex mixed-integer nonlinear models (MINLP) thanks to the latest presolve, cut and parallel heuristics. With increased problem size limits and improved handling of non-linear constraints, users can address a wider range of real-world challenges across industries such as finance, energy, logistics, and robotics.

 2. Improved User Interface: Artelys Knitro 14 features an enhanced user interface, providing a more intuitive and user-friendly experience. The software now boasts improved documentation, tutorials, and use cases, making it easier for both new and experienced users to leverage its powerful capabilities.

 3. Compatibility Upgrades: In response to user feedback, Artelys Knitro 14 includes updates for compatibility with the latest operating systems and programming languages. This ensures seamless integration into diverse computing environments, allowing users to leverage the software across various platforms. In particular, Apple Silicon users may now access Artelys Knitro through a native Matlab interface.

For a detailed breakdown of Artelys Knitro 14 features and improvements, please refer to our release notes.

Use-cases

Discover the use-cases that transform your ideas into practical realities, building a future where technology meets real needs in an exceptional way with Knitro.

What is CasADi?

CasADi initially focused on using a syntax akin to Computer Algebra Systems (CAS) and has now encapsulated this expertise in a general-purpose tool for gradient-based numerical optimization. The user can set up, solve, and perform forward and adjoint sensitivity analysis for systems of Ordinary Differential Equations (ODE) or Differential-Algebraic Equations (DAE) as well as formulate and solve Non-Linear Programs (NLP, MINLP) and Optimal Control Problems (OCP) using the derivative information generated by AD.

It facilitates rapid and efficient implementation of various methods for numerical optimal control, both in an offline context and for Non-linear Model Predictive Control (NMPC).

CasADi’s backbone is a symbolic framework implementing forward and reverse mode of ADon expression graphs to construct gradients, large-and-sparse Jacobians and Hessians.

Interface with Artelys Knitro

For each problem mentioned above, CasADi is able to call relevant solvers, such as Artelys Knitro, a high-performance tool dedicated to NLP/MINLP. This is especially useful to solve high-dimensional nonlinear problems that occur in robotics, for example when performing trajectory planning.

For instance, in a previous news we mentioned an article from the Biomimetic Robotics Lab of the Massachusetts Institute of Technology (MIT) where the authors use CasADi and Knitro for real-time control of their legged robots: the MIT Mini Cheetah and the MIT Humanoid.

The great news is: versions v3.6.2 and higher of CasADI are now compatible with the latest version of Knitro (v13.2)!

Version 13.2 of Artelys Knitro offers many improvements compared to the last version supported in CasADi, some of which are especially relevant in this context:

Applications

The applications of CasADi in science and engineering involve system design, parameter estimation, Model Predictive Control (MPC) or Moving Horizon Estimation (MHE) in many fields, either directly or embedded in software packages.

In The MIT Mini Cheetah use case mentioned earlier, the authors implemented a NMPC to find optimal landing postures, requiring nonlinear trajectory optimization with contact complementary constraints. Because real-time performance is so important in the short duration of a fall, Knitro was used for its speed, reliable convergence and ability to handle complementarity constraints.

In this article, the authors tackle the topical problem of trajectory generation for autonomous Micro-Aerial-Vehicles. A new formulation is proposed for multirotors trajectory generation that, unlike previous formulations, takes into account gravity, aerodynamic drag as well as obstacles and path constraints. The result is an NLP modeled in CasADi and solved with Knitro. Thanks to their approach and to Knitro, the authors are now able to cold start their optimization and converge to an optimal solution.

This use case also resorts to CasADi to plan multi-contact motions for legged robots to traverse uneven surfaces. Due to perturbations that may arise in real world (e.g. environment changes), online motion planning becomes essential. To this end, the authors use Receding Horizon Planning (RHP) which aims at constantly updating the motion plan. RHP requires the resolution of trajectory optimization problems, which are high-dimensional nonlinear problems, hence why Knitro was chosen as solver.

In this thesis, the race strategy of FIA Formula E is optimized. In this series, energy management is crucial. Defining the best trajectory using only GPS data of the track boundaries is a highly nonlinear problem. Therefore, CasADi and Knitro have been used to compute the optimal energy distribution in a single lap first, and then over the entire race.

Discover more Artelys Knitro use cases on this page and more applications of CasADi in this article.

If you want to know more about Artelys Knitro, check out our dedicated webpage.

Mixed-integer nonlinear programs offer a very natural way to model many engineering problems where nonlinear functions can capture the complex behavior of physical processes and integer variables can be used to model discrete decisions. Several examples can be found in our extensive library of Artelys Knitro use-case, including MINLP formulations covering applications such as dimensioning EV charging infrastructures, efficient fault detection and isolation (FDI) on complex systems and many more!

Artelys Knitro 13.2 is more robust and consistently converging in shorter time to optimality on MINLP thanks to several developments:

In particular, Artelys Knitro 13.2 solves 12 additional instances on the MINLPLib benchmark compared to Artelys Knitro 13.1. In addition, this new version converges to a global optimum on 9% additional non-convex instances from MINLPLib2.

If you want to know more about Artelys Knitro, you can contact us or visit the dedicated web page.

Mixed-integer nonlinear problems offer a very natural way to model many engineering problems where nonlinear functions can capture the complex behavior of physical processes and integer variables can be used to model discrete decisions.

Artelys Knitro 13.0 introduced a new parallel branch and bound to solve our customers’ most complex MINLP. Artelys Knitro 13.1 pushes it further by adding several key features:

Relying on these new features dedicated to MINLPs, Artelys Knitro 13.1 is 3 times faster on average than version 13.0 on the MINLP benchmarks such as MINLPLib2.

Artelys Knitro 13.1 also introduces a new noise estimation routine for nonlinear objective functions to adapt the derivatives approximation. This is particularly useful when solving black-box optimization problems and relying on an external simulator (network model, finite element methods, chemical reaction, etc.) in which numerical inaccuracies can lead to noisy black-box optimization models.

Additional features of Artelys Knitro 13.1:

If you want to know more about Artelys Knitro, you can contact us or visit the dedicated web page.

Artelys Knitro is the leading optimization solver for difficult large-scale nonlinear problems. Hundreds of customers worldwide rely on Artelys Knitro’s efficiency and robustness in numerous business and industry applications to solve their nonlinear optimization problems.

Mixed Integer Nonlinear Problems (MINLP) arise in applications in a wide range of fields including chemical engineering, finance, power networks, and manufacturing. Some industry analysts also point to the offshore casino sector as a growing area for sophisticated risk assessment and operational optimization, underscoring the versatility of MINLP solutions. Artelys Knitro 13 introduces a brand-new parallel branch-and-bound algorithm to tackle the most complex MINLP problems, taking advantage of customers’ increasingly important computing capabilities. Using 4 threads on hard MINLP instances (requiring more than 100 seconds to solve), Artelys Knitro 13 is 5 times faster than Artelys Knitro 12.4.

Simultaneously, Artelys Knitro 13 delivers an 18% performance improvement on average for hard continuous nonlinear problems.

In addition, Artelys Knitro 13 brings:

If you want to know more about Artelys Knitro, you can contact us or visit the dedicated web page.

Trajectory optimization

The Biomimetic Robotics Lab at the Massachusetts Institute of Technology (MIT) has been using Artelys Knitro for real-time control of their legged robots: the MIT Mini Cheetah and the MIT Humanoid. Trajectory optimization is an essential process for real-time control of legged robots. Optimal control for legged systems is a challenging problem that must contend with nonlinear, hybrid dynamics and complicated, high degree-of-freedom kinematics. In the case of the Mini Cheetah, this optimization process is used to plan complicated motions such as jumps, aerial spins, backflips, and barrel rolls.

The ability to perform these motions greatly expands the range of environments the robot is capable of traversing, which is crucial as these robots are meant to be deployed operationally. Since incorporating Artelys Knitro into the software, the lab has been able to implement a number of new, real-time controllers on their robots using nonlinear optimization. Even after testing other state-of-the-art nonlinear optimization solvers, Artelys Knitro was the only one capable of performing fast enough to be deployed onboard their robot.

Model predictive controllers

For the Mini Cheetah, a nonlinear model predictive controller (MPC) was implemented for landing. The optimization was formulated with complementarity constraints, which make the nonlinear program (NLP) challenging for many solvers, but Artelys Knitro was able to find solutions at roughly 5-10 Hz (i.e., between 5 and 10 problems are solved every second). The team used a nonlinear trajectory optimization including contact complementary constraints to find optimal landing postures. Because real-time performance is so important in the short duration of a fall, Artelys Knitro was used over other solvers for its speed and reliable convergence.

For the MIT Humanoid, a nonlinear predictive controller was implemented that leverages the MIT Humanoid’s arms to improve balance and locomotion. The complex nonlinear optimization can solve complex arm motions in response to large disturbances. Artelys Knitro solved these motions at 40 Hz and helped demonstrate successful landings in both simulation and hardware.

If you want to know more about Artelys Knitro applications, you can contact us or visit the dedicated web page.