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 86 articles )

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  1. Gehlot, Hemant; Honnappa, Harsha; Ukkusuri, Satish V.: An optimal control approach to day-to-day congestion pricing for stochastic transportation networks (2020)
  2. Song, Liqiang; Yang, Wei Hong: A block Lanczos method for the extended trust-region subproblem (2019)
  3. Sun, Chuangchuang; Dai, Ran: An iterative rank penalty method for nonconvex quadratically constrained quadratic programs (2019)
  4. Taflanidis, A. A.; Giaralis, Agathoklis; Patsialis, D.: Multi-objective optimal design of inerter-based vibration absorbers for earthquake protection of multi-storey building structures (2019)
  5. Nedělková, Zuzana; Lindroth, Peter; Patriksson, Michael; Strömberg, Ann-Brith: Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space (2018)
  6. Putkaradze, Vakhtang; Rogers, Stuart: Constraint control of nonholonomic mechanical systems (2018)
  7. Torrisi, Giampaolo; Grammatico, Sergio; Smith, Roy S.; Morari, Manfred: A projected gradient and constraint linearization method for nonlinear model predictive control (2018)
  8. Boukouvala, Fani; Faruque Hasan, M. M.; Floudas, Christodoulos A.: Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption (2017)
  9. Bruni, Renato; Cesarone, Francesco; Scozzari, Andrea; Tardella, Fabio: On exact and approximate stochastic dominance strategies for portfolio selection (2017)
  10. Chen, Jingnan; Flood, Mark D.; Sowers, Richard B.: Measuring the unmeasurable: an application of uncertainty quantification to treasury bond portfolios (2017)
  11. Corveleyn, Samuel; Vandewalle, Stefan: Computation of the output of a function with fuzzy inputs based on a low-rank tensor approximation (2017)
  12. 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)
  13. Liu, Qunfeng; Yang, Guang; Zhang, Zhongzhi; Zeng, Jinping: Improving the convergence rate of the DIRECT global optimization algorithm (2017)
  14. Tan, Tunzi; Gao, Suixiang; Mesa, Juan A.: An exact algorithm for min-max hyperstructure equipartition with a connected constraint (2017)
  15. Torgovitsky, Alexander: Minimum distance from independence estimation of nonseparable instrumental variables models (2017)
  16. Zhao, Xiao; Noack, Stephan; Wiechert, Wolfgang; von Lieres, Eric: Dynamic flux balance analysis with nonlinear objective function (2017)
  17. 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)
  18. Linker, Raphael; Ioslovich, Ilya: Optimal irrigation scheduling for wheat production in the Canadian prairies: a modelling study (2016)
  19. Zheng, Haiyan; Jian, Jinbao; Yang, Linfeng; Quan, Ran: A deterministic method for the unit commitment problem in power systems (2016)
  20. Bruni, Renato; Cesarone, Francesco; Scozzari, Andrea; Tardella, Fabio: A linear risk-return model for enhanced indexation in portfolio optimization (2015)

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