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TOMLAB

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TOMLAB
Developer(s)Tomlab Optimization Inc.
Stable release
8.7 / 17 September 2020
Written inMATLAB, C, Fortran
Operating systemWindows 32/64-bit, Linux 32/64-bit and Mac OS X (Intel)
Size89 MB (Windows 32-bit)
TypeTechnical computing
LicenseProprietary
WebsiteTOMLAB product page

The TOMLAB[1][2][3] Optimization Environment is a modeling platform for solving applied optimization problems in MATLAB.

Description

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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

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Additional features

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Further details

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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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  1. ^ 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.
  2. ^ Kallrath, Josef; Holmström, Kenneth; Edvall, Marcus M. (29 February 2004). Modeling Languages in Mathematical Optimization (Applied Optimization). Springer. ISBN 1-4020-7547-2.
  3. ^ 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)
  4. ^ "TOMLAB OPTIMIZATION", TOMOPT Home Page Juli, 2014.
  5. ^ 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.
  6. ^ "PROPT - Matlab Optimal Control Software (DAE, ODE)", PROPT Home Page April, 2009.
  7. ^ "Matlab Automatic Differentiation (MAD) - matlabAD", MAD Home Page June, 2008.
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