TOMLAB
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| TOMLAB | |
|---|---|
| [[Programmer|DeveloperTemplate:Pluralize from text]] | Tomlab Optimization Inc. |
| Written in | MATLAB, C, Fortran |
| Engine | Page Template:Plainlist/styles.css has no content.Template:EditAtWikidata |
| Operating system | Windows 32/64-bit, Linux 32/64-bit and Mac OS X (Intel) |
| Size | 89 MB (Windows 32-bit) |
| Type | Technical computing |
| License | Proprietary |
| Website | TOMLAB product page |
| Repository | Page Template:Plainlist/styles.css has no content.
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The TOMLAB[1][2][3] Optimization Environment is a modeling platform for solving applied optimization problems in MATLAB.
Description
TOMLAB is a general purpose development and modeling environment[4] in MATLAB for research, teaching and practical solution of optimization problems. It enables a wider range of problems to be solved in MATLAB and provides many additional solvers.
Optimization problems supported
- TOMLAB handles a wide range of problem types, among them:
- Linear programming
- Quadratic programming
- Nonlinear programming
- Mixed-integer programming
- Mixed-integer quadratic programming with or without convex quadratic constraints
- Mixed-integer nonlinear programming
- Linear and nonlinear least squares with L1, L2 and infinity norm
- Exponential data fitting
- Global optimization
- Semi-definite programming problem with bilinear matrix inequalities
- Constrained goal attainment
- Geometric programming
- Genetic programming
- Costly or expensive black-box global optimization[5]
- Nonlinear complementarity problems
Additional features
- TOMLAB supports more areas than general optimization, for example:
- Optimal control with PROPT using Gauss and Chebyshev collocation.[6]
- Automatic differentiation with MAD[7]
- Interface to AMPL
Further details
TOMLAB supports solvers like CPLEX, SNOPT, KNITRO and MIDACO. Each such solver can be called to solve one single model formulation. The supported solvers are appropriate for many problems, including linear programming, integer programming, and global optimization.
An interface to AMPL makes it possible to formulate the problem in an algebraic format. The MATLAB Compiler enables the user to build stand-alone solutions. Sister products are available for LabVIEW and Microsoft .NET.
Modeling is mainly facilitated by the TomSym class.
References
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- ^ Page Module:Citation/CS1/styles.css has no content.Holmström, Kenneth; Quttineh, Nils-Hassan; Edvall, Marcus M. (7 February 2008). An adaptive radial basis algorithm {(ARBF)} for expensive black-box mixed-integer constrained global optimization. Journal of Optimization and Engineering. doi:10.1007/s11081-008-9037-3. ISSN 1389-4420.
- ^ Page Module:Citation/CS1/styles.css has no content.Kallrath, Josef; Holmström, Kenneth; Edvall, Marcus M. (29 February 2004). Modeling Languages in Mathematical Optimization (Applied Optimization). Springer. ISBN 1-4020-7547-2.
- ^ Page Module:Citation/CS1/styles.css has no content.Holmström, Kenneth; Edvall, Marcus M.; Göran Anders O. (21 October 2003). "TOMLAB - for Large-Scale Robust Optimization" (PDF). Nordic MATLAB Conference 2003.
{{cite journal}}: Cite journal requires|journal=(help) - ^ "TOMLAB OPTIMIZATION", TOMOPT Home Page Juli, 2014.
- ^ Page Module:Citation/CS1/styles.css has no content.Holmström, Kenneth (7 November 2007). An adaptive radial basis algorithm {(ARBF)} for expensive black-box global optimization. Journal of Global Optimization (JOGO). doi:10.1007/s10898-007-9256-8. ISSN 0925-5001.
- ^ "PROPT - Matlab Optimal Control Software (DAE, ODE)", PROPT Home Page April, 2009.
- ^ "Matlab Automatic Differentiation (MAD) - matlabAD", MAD Home Page June, 2008.
External links
- TOMLAB
- MAD (MATLAB Automatic Differentiation)
- PROPT - MATLAB Optimal Control Software
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