TOMLAB is a general purpose development and modeling environment in Matlab for research, teaching and practical solution of optimization problems. The TOMLAB optimization environment is flexible, easy-to-use, robust and reliable for the solution of all types of applied optimization problems. TOMLAB has grown out of a need for advanced, robust and reliable tools to be used in the development of algorithms and software for the solution of applied optimization problems. TOMLAB supplies Matlab solver algorithms, as well as well-known state-of-the-art optimization software packages in the areas that TOMLAB covers. The external solvers are distributed as compiled binary MEX DLLs on PC-systems, and compiled MEX library files on Unix and other systems. All TOMLAB packages include a license for the solver.

References in zbMATH (referenced in 73 articles )

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  1. Corveleyn, Samuel; Vandewalle, Stefan: Computation of the output of a function with fuzzy inputs based on a low-rank tensor approximation (2017)
  2. Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)
  3. Liu, Qunfeng; Yang, Guang; Zhang, Zhongzhi; Zeng, Jinping: Improving the convergence rate of the DIRECT global optimization algorithm (2017)
  4. Torgovitsky, Alexander: Minimum distance from independence estimation of nonseparable instrumental variables models (2017)
  5. Zhao, Xiao; Noack, Stephan; Wiechert, Wolfgang; von Lieres, Eric: Dynamic flux balance analysis with nonlinear objective function (2017)
  6. Alazki, Hussain; Poznyak, Alexander: Robust output stabilization for a class of nonlinear uncertain stochastic systems under multiplicative and additive noises: the attractive ellipsoid method (2016)
  7. Linker, Raphael; Ioslovich, Ilya: Optimal irrigation scheduling for wheat production in the Canadian prairies: a modelling study (2016)
  8. Zheng, Haiyan; Jian, Jinbao; Yang, Linfeng; Quan, Ran: A deterministic method for the unit commitment problem in power systems (2016)
  9. Bruni, Renato; Cesarone, Francesco; Scozzari, Andrea; Tardella, Fabio: A linear risk-return model for enhanced indexation in portfolio optimization (2015)
  10. Cesarone, Francesco; Scozzari, Andrea; Tardella, Fabio: Linear vs. quadratic portfolio selection models with hard real-world constraints (2015)
  11. Gopalakrishnan, Hariharan; Kosanovic, Dragoljub: Operational planning of combined heat and power plants through genetic algorithms for mixed 0-1 nonlinear programming (2015)
  12. Ko, Sangho; Weyer, Erik; Campi, Marco Claudio: Non-asymptotic model quality assessment of transfer functions at multiple frequency points (2015)
  13. Liu, Qunfeng; Zeng, Jinping; Yang, Gang: MrDIRECT: a multilevel robust DIRECT algorithm for global optimization problems (2015)
  14. Ma, Ding; Saunders, Michael A.: Solving multiscale linear programs using the simplex method in quadruple precision (2015)
  15. Conde, Eduardo: A MIP formulation for the minmax regret total completion time in scheduling with unrelated parallel machines (2014)
  16. Cesarone, Francesco; Scozzari, Andrea; Tardella, Fabio: A new method for mean-variance portfolio optimization with cardinality constraints (2013)
  17. Fasano, Giorgio; Pintér, János D.: Model development and optimization for space engineering: concepts, tools, applications, and perspectives (2013)
  18. Jia, Gaofeng; Taflanidis, Alexandros A.: Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment (2013)
  19. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  20. Zhao, Hengjun; Zhan, Naijun; Kapur, Deepak: Synthesizing switching controllers for hybrid systems by generating invariants (2013)

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