Artelys Knitro is an especially powerful nonlinear solver, offering a range of state-of-the-art algorithms and options for working with smooth objective and constraint functions in continuous and integer variables. It is designed for local optimization of large-scale problems with up to hundreds of thousands of variables.
Unconstrained, bound-constrained, and nonlinear equation systems; linear and nonlinear least squares; LPs, QPs (convex and nonconvex), QCQPs, and SOCPs; complementarity constraints (MPCCs); general nonlinear (NLP), mixed-integer linear (MILP), and mixed-integer nonlinear (MINLP) problems of moderate size; and derivative-free (DFO) or black-box optimization.
Complementarity and equilibrium constraints using the AMPL “complements” operator.
Knitro utilizes four advanced optimization algorithms: direct and conjugate-gradient interior point methods, active set, and sequential quadratic programming (SQP). Mixed-integer nonlinear programs are solved via nonlinear branch-and-bound (NLPBB) or mixed-integer SQP (MISQP), with MISQP tailored for costly function evaluations and non-relaxable integer variables.
Knitro leverages shared-memory multi-core computing for concurrent optimization, parallel multi-start for non-convex problems, and parallel linear algebra and gradient computations. It includes options to maintain feasibility, along with specialized handling of quadratic objectives and constraints for improved efficiency.
Knitro for AMPL is a specialized version of the solver, designed exclusively for use within the AMPL environment. It includes key enhancements that make the solver even more powerful, allowing it to handle larger, more complex nonlinear optimization problems with greater efficiency and speed.
Knitro for AMPL is more than just a solver—it’s an enhanced nonlinear optimization experience. AMPL provides a purpose-built solver interface that allows Knitro to handle large-scale nonlinear and nonconvex problems with greater efficiency and scalability. By leveraging AMPL’s modeling language, users gain faster insights, improved workflow efficiency, and a better return on their solver investment, all while maintaining the flexibility and power of Knitro.
AMPL’s solver interface optimizes problem structures before they reach the solver, reducing computation time and improving efficiency. By automatically restructuring nonlinear and nonconvex models, AMPL enables Knitro to solve complex problems faster and more effectively, making large-scale decision-making more practical and scalable across industries.
One of AMPL’s key advantages is its ability to automatically reformulate nonlinear problems into solver-friendly formats. This ensures that even highly complex models, including those with complementarity constraints, large-scale derivatives, and second-order methods, are structured in a way that Knitro can process more efficiently, improving solver stability and convergence speed.
To help users get the most out of Knitro, AMPL offers consulting services and dedicated support plans for solver tuning and model optimization. Our experts provide solver diagnostics, advanced tuning, and optimization strategies to improve computation times and solver performance. Whether refining a problem formulation or troubleshooting solver behavior, AMPL’s specialized services ensure users achieve the best possible results with Knitro.
Access this world-class solver in the AMPL License Portal, available on Windows, Linux, and macOS
Knitro can be used with AMPL from various programming languages using our dedicated APIs
For Python enthusiasts, Xpress is also accessible as a module with amplpy, blending seamlessly with your Python projects.
How to install using amplpy:
# Install Python API for AMPL: $ python -m pip install amplpy --upgrade # Install AMPL & solver modules: $ python -m amplpy.modules install knitro # install Knitro # Activate your license (e.g., free ampl.com/ce or ampl.com/courses licenses): $ python -m amplpy.modules activate <your-license-uuid>
How to use:
from amplpy import AMPL
ampl = AMPL()
...
ampl.solve(solver="knitro", knitro_options="option1=value1 option2=value2")For learning optimization and benchmarking solvers – a one-time 1 month free trial of Knitro is available with a free Community Edition license.
For building and testing optimization models with commercial solver integration and full support, ensuring a smooth path to enterprise deployment.
AMPL starting at
$300 / month
Billed annually, includes updates and basic support.
Knitro starting at
$165 / month
Billed annually, includes updates and basic support.
Build a custom plan – designed for large teams, multiple processes, high computational demands, or unique workflows.
All custom licenses built to your specific needs.
AMPL empowers businesses across diverse industries to make smarter decisions, improve efficiency, and maximize performance through optimization. From supply chain logistics to financial modeling, our expertise helps organizations tackle real-world challenges with precision and speed.
Knitro and AMPL enable pharmaceutical companies to optimize drug formulations, dosage regimens, and clinical trial designs. By leveraging advanced nonlinear solvers, researchers can improve efficiency in R&D, reduce costs, and accelerate drug approval processes.
AMPL and Knitro provide powerful nonlinear optimization tools for power grid stability, renewable energy integration, and network flow optimization. Energy providers use these solutions to minimize costs, balance energy loads, and ensure sustainable operations.
Knitro’s ability to solve large-scale nonlinear and nonconvex problems makes it ideal for hyperparameter tuning, deep learning optimization, and AI model training. AMPL’s interface ensures efficient problem structuring, reducing computation time and improving model accuracy.
Get the most out of Knitro with these essential resources—documentation, tutorials, and expert guides to help you optimize efficiently.
Knitro for AMPL Documentation
Your go-to reference for using Knitro, covering everything from installation to advanced solver techniques.
Knitro Parameters & Options
Fine-tune Knitro’s performance with a full list of solver parameters and configuration options.
MP Modeling Guide
Learn how to use the AMPL MP library to create flexible, solver-agnostic optimization models that work seamlessly with Knitro.
Knitro for AMPL on Google Colab
Run Knitro with AMPL directly in your browser using pre-configured Google Colab notebooks—no installation required.
Knitro for AMPL Change Log
Stay up to date with the latest improvements, bug fixes, and feature updates for the Knitro-AMPL interface.
How to Use Knitro for AMPL
Best practices for setting up Knitro with AMPL, running models, and optimizing solver performance.
Knitro is specifically designed for solving large-scale, complex non-linear optimization problems. It excels in areas like mixed-integer, quadratic, and nonlinear programming, making it ideal for applications in finance, energy management, engineering design, and more.
Knitro seamlessly integrates with AMPL, allowing users to define their optimization models in AMPL’s user-friendly language, which Knitro then solves using its advanced algorithms. This integration provides an efficient workflow for formulating, solving, and analyzing optimization problems.
Yes, Knitro is renowned for its ability to efficiently solve large-scale optimization problems. Its advanced algorithms and techniques are specifically designed to handle the complexities and size of large-scale, real-world problems.
Knitro provides comprehensive documentation, including user guides, example problems, and technical references. Additionally, users have access to a dedicated support team for technical assistance and guidance in both using Knitro and integrating it with AMPL.
Yes, a trial version of Knitro is available for evaluation purposes. Prospective users can request a trial to assess Knitro’s capabilities and determine how it meets their specific optimization needs.
Knitro uses rigorously tested algorithms and numerical methods to ensure the accuracy and reliability of its solutions. It employs advanced techniques to verify solution quality and provides detailed diagnostic information to aid in model evaluation and troubleshooting.
Knitro stands out for its specialized focus on non-linear problems, its ability to handle a wide range of problem types, and its seamless integration with modeling languages like AMPL. Its robustness, speed, and advanced features make it a preferred choice for complex optimization tasks.
Yes, Knitro is widely used in academic research for solving complex optimization problems across various disciplines. Special licensing options are available for academic institutions, supporting research and educational use.
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