Artelys Knitro

Last updated
Artelys Knitro
Designed by
  • Richard Waltz
  • Jorge Nocedal
  • Todd Plantenga
  • Richard Byrd
Developer Artelys
First appeared2001 (2001)
Stable release
14.0 / January 30, 2024;2 months ago (2024-01-30)
OS Cross-platform
License Proprietary
Website Artelys Knitro

Artelys Knitro is a commercial software package for solving large scale nonlinear mathematical optimization problems.

Contents

KNITRO – (the original solver name) short for "Nonlinear Interior point Trust Region Optimization" (the "K" is silent) – was co-created by Richard Waltz, Jorge Nocedal, Todd Plantenga and Richard Byrd. It was first introduced in 2001, as a derivative of academic research at Northwestern University (through Ziena Optimization LLC), and has since been continually improved by developers at Artelys.

Optimization problems must be presented to Knitro in mathematical form, and should provide a way of computing function derivatives using sparse matrices (Knitro can compute derivatives approximation but in most cases providing the exact derivatives is beneficial). An often easier approach is to develop the optimization problem in an algebraic modeling language. The modeling environment computes function derivatives, and Knitro is called as a "solver" from within the environment.

Problem classes solved by Artelys Knitro

Knitro is specialized for nonlinear optimization but also solves a wide range of optimization problems:

Algorithms

Artelys Knitro contains a wide range of optimization algorithms.

NonLinear Programming (NLP) solver

Knitro offers four different optimization algorithms for solving optimization problems. [1] Two algorithms are of the interior point type, and two are of the active set type. These algorithms are known to have fundamentally different characteristics; for example, interior point methods follow a path through the interior of the feasible region while active set methods tend to stay at the boundaries. Knitro provides both types of algorithm for greater flexibility in solving problems, and allows crossover during the solution process from one algorithm to another. The code also provides a multistart option for promoting the computation of the global minimum.

Mixed-Integer NonLinear Programming (MINLP) solver

Knitro provides tools for solving optimization models (both linear and nonlinear) with binary or integer variables. The Knitro mixed integer programming (MIP) code offers three algorithms for mixed-integer nonlinear programming (MINLP): [2]

Features

Artelys Knitro supports a variety of programming and modeling languages including. [3]

Artelys Knitro also includes a number of key features:

Supported platforms

Artelys Knitro is available on the following platforms:

Related Research Articles

Quadratic programming (QP) is the process of solving certain mathematical optimization problems involving quadratic functions. Specifically, one seeks to optimize a multivariate quadratic function subject to linear constraints on the variables. Quadratic programming is a type of nonlinear programming.

<span class="mw-page-title-main">Mathematical optimization</span> Study of mathematical algorithms for optimization problems

Mathematical optimization or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

In mathematics, nonlinear programming (NLP) is the process of solving an optimization problem where some of the constraints or the objective function are nonlinear. An optimization problem is one of calculation of the extrema of an objective function over a set of unknown real variables and conditional to the satisfaction of a system of equalities and inequalities, collectively termed constraints. It is the sub-field of mathematical optimization that deals with problems that are not linear.

<span class="mw-page-title-main">AMPL</span>

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Sequential quadratic programming (SQP) is an iterative method for constrained nonlinear optimization which may be considered a quasi-Newton method. SQP methods are used on mathematical problems for which the objective function and the constraints are twice continuously differentiable, but not necessarily convex.

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MOSEK is a software package for the solution of linear, mixed-integer linear, quadratic, mixed-integer quadratic, quadratically constrained, conic and convex nonlinear mathematical optimization problems. The applicability of the solver varies widely and is commonly used for solving problems in areas such as engineering, finance and computer science.

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Jorge Nocedal is an applied mathematician, computer scientist and the Walter P. Murphy professor at Northwestern University who in 2017 received the John Von Neumann Theory Prize. He was elected a member of the National Academy of Engineering in 2020.

ANTIGONE, is a deterministic global optimization solver for general Mixed-Integer Nonlinear Programs (MINLP).

References