Comparison of optimization software
||This article's lead section may not adequately summarize key points of its contents. (October 2010)|
Given a system transforming a set of inputs to output values, described by a mathematical function f, optimization refers to the generation and selection of a best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the value of the function, and recording the best value found during the process. Many real-world and theoretical problems may be modeled in this general framework. 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, or the inputs can describe the configuration of a physical system and the output can be its energy.
An optimization problem can be represented in the following way
- Given: a function f : A 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 ("minimization").
Typically, 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. Maximization can be reduced to minimization by multiplying the function by minus one.
The use of optimization software requires that the function f is defined in a suitable programming language and linked 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). 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 comparison of optimization software libraries, either specialized or general purpose libraries with significant optimization coverage.
|Language||Latest stable version||Academic/noncommercial
use is free
|Can be used in
|ALGLIB||C++, C#, FreePascal, VBA||3.8.0 / August 2013||Yes||Yes||Dual (Commercial, GPL)||General purpose library, includes optimization package.|
|AMPL||C||October 2013||Yes||Yes||Dual (Commercial, academic)||A popular algebraic modeling language for linear, mixed-integer and nonlinear optimization. Student and AMPL for courses versions are available for free.|
|APMonitor||Fortran, C++, Python, Matlab, Julia||0.6.2 / March 2016||Yes||Yes||Dual (Commercial, academic)||A differential and algebraic modeling language for mixed-integer and nonlinear optimization. Freely available interfaces for Matlab, Python, and Julia.|
|GNU Scientific Library||C||1.16 / July 2013||Yes||No||GPL||Free library provided by GNU project.|
|GNU Linear Programming Kit||C||4.52 / July 2013||Yes||No||GPL||Free library for linear programming (LP) and mixed integer programming (MIP).|
|IMSL Numerical Libraries||C, Java, C#, Fortran, Python||many components||No||Yes||Proprietary|
|LIONsolver||C++, Java||2.0.198 / October 2011||Yes||Yes||Proprietary||Support for interactive and learning optimization,
according to RSO principles .
|MKL||C++, Fortran||11.1 / October 2013||No||Yes||Proprietary||Numerical library from Intel. MKL is specialized on linear algebra,
but contains some optimization-related functionality.
|MIDACO||C++, Python, Matlab, C#, Fortran, R, Java, Excel, VBA||5.0 / April 2016||Yes||Yes||Dual (Commercial, BY-NC-ND)||General purpose global optimization solver, single- and multi-objective MINLP problems, supporting parallelization and large-scale. Free limited version available under BY-NC-ND.|
|NAG Numerical Libraries||C, Fortran||Mark 24 / October 2013||No||Yes||Proprietary|
|NMath||C#||5.3 / May 2013||No||Yes||Proprietary||C# numerical library built on top of MKL.|
|OptaPlanner||Java||6.0.1.Final / Dec 2013||Yes||Yes||ASL||Lightweight optimization solver in Java|
|SciPy||Python||0.13.1 / November 2013||Yes||Yes||BSD||General purpose numerical and scientific computing library for Python.|
- OR/MS Today: 2013 Linear Programming Software Survey
- OR/MS Today: 1998 Nonlinear Programming Software Survey