The TOMLAB OPERA toolbox for linear and discrete optimization. The Matlab toolbox OPERA TB is a set of Matlab mfiles, which solves basic linear and discrete optimization problems in operations research and mathematical programming. Included are routines for linear programming (LP), network programming (NP), integer programming (IP) and dynamic programming (DP). OPERA TB, like the nonlinear programming toolbox NLPLIB TB, is a part of TOMLAB; an environment in Matlab for research and teaching in optimization. Linear programs are solved either by direct call to a solver routine or to a multisolver driver routine, or interactively, using the Graphical User Interface (GUI) or a menu system. From OPERA TB it is possible to call solvers in the Math Works Optimization Toolbox and, using a MEXfile interface, generalpurpose solvers implemented in Fortran or C. The focus is on dense problems, but sparse linear programs may be solved using the commercial solver MINOS. Presently, OPERA TB implements about thirty algorithms and includes a set of test examples and demonstration files. This paper gives an overview of OPERA TB and presents test results for medium size LP problems. The tests show that the OPERA TB solver converges as fast as commercial Fortran solvers and is at least five times faster than the simplex LP solver in the Optimization Toolbox 2.0 and twice as fast as the primaldual interiorpoint LP solver in the same toolbox. Running the commercial Fortran solvers using MEXfile interfaces gives a speed- up factor of five to thirtyfive.

References in zbMATH (referenced in 30 articles , 1 standard article )

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  1. Manno, Andrea; Amaldi, Edoardo; Casella, Francesco; Martelli, Emanuele: A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement (2020)
  2. Sun, Chuangchuang; Dai, Ran: An iterative rank penalty method for nonconvex quadratically constrained quadratic programs (2019)
  3. Torrisi, Giampaolo; Grammatico, Sergio; Smith, Roy S.; Morari, Manfred: A projected gradient and constraint linearization method for nonlinear model predictive control (2018)
  4. Tan, Tunzi; Gao, Suixiang; Mesa, Juan A.: An exact algorithm for min-max hyperstructure equipartition with a connected constraint (2017)
  5. Zhao, Xiao; Noack, Stephan; Wiechert, Wolfgang; von Lieres, Eric: Dynamic flux balance analysis with nonlinear objective function (2017)
  6. Ma, Ding; Saunders, Michael A.: Solving multiscale linear programs using the simplex method in quadruple precision (2015)
  7. Conde, Eduardo: A MIP formulation for the minmax regret total completion time in scheduling with unrelated parallel machines (2014)
  8. Ahn, Mihye; Zhang, Hao Helen; Lu, Wenbin: Moment-based method for random effects selection in linear mixed models (2012)
  9. Berend, Daniel; Korach, Ephraim; Zucker, Shira: Tabu search for the BWC problem (2012)
  10. Ingolfsson, Armann; Campello, Fernanda; Wu, Xudong; Cabral, Edgar: Combining integer programming and the randomization method to schedule employees (2010)
  11. Jakobsson, Stefan; Saif-Ul-Hasnain, Muhammad; Rundqvist, Robert; Edelvik, Fredrik; Andersson, Björn; Patriksson, Michael; Ljungqvist, Mattias; Lortet, Dimitri; Wallesten, Johan: Combustion engine optimization: a multiobjective approach (2010)
  12. Pisică, Ioana; Postolache, Petru; Edvall, Marcus M.: Optimal planning of distributed generation via nonlinear optimization and genetic algorithms (2010)
  13. Chiu, Nan-Chieh; Fang, Shu-Cherng; Lavery, John E.; Lin, Jen-Yen; Wang, Yong: Approximating term structure of interest rates using cubic (L_1) splines (2008)
  14. Holmström, Kenneth; Quttineh, Nils-Hassan; Edvall, Marcus M.: An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization (2008)
  15. Regis, Rommel G.; Shoemaker, Christine A.: Improved strategies for radial basis function methods for global optimization (2007)
  16. Murray, Walter; Shanbhag, Uday V.: A local relaxation approach for the siting of electrical substations (2006)
  17. Zörnig, Peter: Systematic construction of examples for cycling in the simplex method (2006)
  18. Regis, Rommel G.; Shoemaker, Christine A.: Constrained global optimization of expensive black box functions using radial basis functions (2005)
  19. Banga, Julio R.; Moles, Carmen G.; Alonso, Antonio A.: Global optimization of bioprocesses using stochastic and hybrid methods (2004)
  20. Berbyuk, V. E.: Control and optimization of semi-passively actuated multibody systems (2003)

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