List of optimization software

Given a transformation between input and output values, described by a mathematical function f, optimization deals with generating and selecting a best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function, and recording the best output values found during the process. Many real-world problems can be modeled in this way. For example, the inputs can be design parameters of a motor, the output can be the power consumption, or the inputs can be business choices and the output can be the obtained profit.

An optimization problem, in this case a minimization problem, can be represented in the following way

Given: a function f : A \to R from some set A to the real numbers
Search for: an element x0 in A such that f(x0) ≤ f(x) for all x in A.

In continuous optimization, A is some subset of the Euclidean space Rn, often specified by a set of constraints, equalities or inequalities that the members of A have to satisfy. In combinatorial optimization, A is some subset of a discrete space, like binary strings, permutations, or sets of integers.

The use of optimization software requires that the function f is defined in a suitable programming language and connected at compile or run time to the optimization software. The optimization software will deliver input values in A, the software module realizing f will deliver the computed value f(x) and, in some cases, additional information about the function like derivatives.

In this manner, a clear separation of concerns is obtained: different optimization software modules can be easily tested on the same function f, or a given optimization software can be used for different functions f.

The following tables provide a list of optimization software organized according to license and business model type.

Free and Open Source software

Name License Brief info
ADMB BSD nonlinear optimization framework, using automatic differentiation
ALGENCAN GPL Fortran code for general nonlinear programming. Interfaces with AMPL, C/C++, CUTEr, Matlab, Python, Octave and R.
APMonitor BSD MATLAB Toolbox and Python APIs to Mixed Integer Nonlinear Programming Solvers
ASCEND GPL mathematical modelling system
BOBYQA LGPLAn algorithm that seeks the least value of a nonlinear function subject to bound constraints, without using derivatives of the objective function. By Professor Michael J. D. Powell. Source code is available at CCPForge or here.
CMA-ES BSDCovariance Matrix Adaptation Evolution Strategy. Source code available at
COBYLA LGPLAn algorithm that seeks the least value of a nonlinear function subject to nonlinear inequality constraints, without using derivatives of the objective function or the constraints. By Professor Michael J. D. Powell. Source code is available at CCPForge or here.
CONDOR GPL Non-linear Continuous Objective Function for small dimension (n<20) with linear and non-linear constraints. Only the value of the objective function is used. Stand-Alone C++ code.
COIN-OR SYMPHONY Eclipse v.1 integer programming
CUTEr GPL testing environment for optimization and linear algebra solvers
dlib Boost A stand-alone C++ library with a variety of linear and non-linear solvers for small and large scale problems
EvA2 GPL Evolutionary algorithms framework written in Java
GLPK GPL GNU Linear Programming Kit
IPOPTCPL large scale nonlinear optimization for continuous system (requires gradient)
JOptimizer Apache License Java library for convex optimization
JuliaOpt MIT, BSD and MPL 2.0 A collection of optimization libraries and environment written in Julia
L-BFGS BSD limited-memory quasi-Newton method optimization; for large scale optimization
Liger LGPL Liger is an open source integrated optimization environment for single and multi-objective nonconvex problems
LINCOA LGPLAn algorithm that seeks the least value of a nonlinear function subject to linear inequality constraints, without using derivatives of the objective function. By Professor Michael J. D. Powell. Source code is available at CCPForge or here.
MIDACO BY-NC-ND Global optimization software, Limited Version, MINLP, Parallelization (Excel, Java, Matlab, Python, C/C++, R and Fortran)
MINUIT/MINUIT2 (L)GPL multivariate function minimizer for real-valued functions with analytic or numerical gradients
MLPACK BSD mixed convex L1/L2 LARS/Lasso optimization, clustering, PCA, ridge regression (C++)
NEWUOA LGPLAn algorithm that solves unconstrained optimization problems without using derivatives. By Professor Michael J. D. Powell. Source code is available at CCPForge or here.
NLopt LGPL, MIT many algos, many language bindings, global and local optimizers, derivative-free and gradient-driven
NOMADLGPL generic black-box (no gradients required) optimization package
OpenMDAOASL Multidisciplinary Design, Analysis, and Optimization (MDAO) framework, written in the Python programming language. Developed by NASA Glenn Research Center, with support from the NASA Langley Research Center.
OpenOptBSD free numerical optimization framework in Python language for solving NLP, LP, MIP, QP, etc. with automatic differentiation features.
Opt4JMIT Java-based framework for evolutionary computation.
OptaPlannerASL OptaPlanner is a lightweight, embeddable planning engine written in Java™. It helps normal Java™ programmers solve constraint satisfaction problems efficiently. Under the hood, it combines optimization heuristics and metaheuristics with very efficient score calculation.
PPL GPLv3 integer programming problems, polyhedra
ScilabCeCILL cross-platform numerical computational package and a high-level, numerically oriented programming language with free numerical optimization framework.
TAO BSD large-scale optimization, focus on parallel algos.
TOLMIN LGPLAn algorithm that minimizes a general differentiable nonlinear function subject to linear constraints. By Professor Michael J. D. Powell.Source code is available at CCPForge or here.
UOBYQA LGPLAn algorithm that solves unconstrained optimization problems without using derivatives (for general usage, NEWUOA is recommended to replace UOBYQA). By Professor Michael J. D. Powell. Source code is available at CCPForge or here.

Proprietary software

Freeware \ Free for academic use

See also

This article is issued from Wikipedia - version of the Friday, April 29, 2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.