TOMLAB

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TOMLAB
Developer(s) Tomlab Optimization Inc.
Stable release 7.9 / 23 August 2012
Development status Active
Written in MATLAB, C, Fortran
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

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

Description[edit]

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[edit]

Additional features[edit]

Further details[edit]

TOMLAB supports solvers like Gurobi, CPLEX, SNOPT and KNITRO. 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[edit]

  1. ^ Holmström, Kenneth; Quttineh, Nils-Hassan, Edvall, Marcus M. (2008-02-07). 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. (2004-02-29). Modeling Languages in Mathematical Optimization (Applied Optimization). Springer. ISBN 1-4020-7547-2. 
  3. ^ Holmström, Kenneth; Edvall, Marcus M., Göran Anders O. (2003-10-21). TOMLAB - for Large-Scale Robust Optimization (PDF). Nordic MATLAB Conference 2003. 
  4. ^ "TOMlab SYMbolic modeling", TOMSYM Home Page April, 2009.
  5. ^ Holmström, Kenneth (2007-11-07). 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.

External links[edit]