Benchmarks for Optimization Software
Benchmarks for Optimization Software: Here we provide information on testruns comparing different solution methods on standardized sets of testproblems, running on the same or on different computer systems. Benchmarking is a difficult area for nonlinear problems, since different codes use different criteria for termination. Although much effort has been invested in making results comparable, in a critical situation you should try the candidates of your choice on your specific application. Many benchmark results can be found in the literature, ..
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References in zbMATH (referenced in 132 articles )
Showing results 1 to 20 of 132.
Sorted by year (- Bruno, Hugo; Barros, Guilherme; Menezes, Ivan F. M.; Martha, Luiz Fernando: Return-mapping algorithms for associative isotropic hardening plasticity using conic optimization (2020)
- Eltved, Anders; Dahl, Joachim; Andersen, Martin S.: On the robustness and scalability of semidefinite relaxation for optimal power flow problems (2020)
- Galabova, I. L.; Hall, J. A. J.: The `Idiot’ crash quadratic penalty algorithm for linear programming and its application to linearizations of quadratic assignment problems (2020)
- Kobayashi, Ken; Takano, Yuich: A branch-and-cut algorithm for solving mixed-integer semidefinite optimization problems (2020)
- Averkov, Gennadiy: Optimal size of linear matrix inequalities in semidefinite approaches to polynomial optimization (2019)
- Kuhlmann, Renke: Learning to steer nonlinear interior-point methods (2019)
- Pereira Coutinho, Walton; Fliege, Jörg; Battarra, Maria: Glider routing and trajectory optimisation in disaster assessment (2019)
- Van Bulck, David; Goossens, Dries R.; Spieksma, Frits C. R.: Scheduling a non-professional indoor football league: a tabu search based approach (2019)
- Waki, Hayato; Sebe, Noboru: Application of facial reduction to (H_\infty) state feedback control problem (2019)
- Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
- Huangfu, Q.; Hall, J. A. J.: Parallelizing the dual revised simplex method (2018)
- Permenter, Frank; Parrilo, Pablo: Partial facial reduction: simplified, equivalent SDPs via approximations of the PSD cone (2018)
- Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
- Cheung, Kevin K. H.; Gleixner, Ambros; Steffy, Daniel E.: Verifying integer programming results (2017)
- Gamrath, Gerald; Koch, Thorsten; Maher, Stephen J.; Rehfeldt, Daniel; Shinano, Yuji: SCIP-Jack -- a solver for STP and variants with parallelization extensions (2017)
- Hijazi, Hassan; Coffrin, Carleton; Van Hentenryck, Pascal: Convex quadratic relaxations for mixed-integer nonlinear programs in power systems (2017)
- Mohammad-Nezhad, Ali; Terlaky, Tamás: A polynomial primal-dual affine scaling algorithm for symmetric conic optimization (2017)
- Permenter, Frank; Friberg, Henrik A.; Andersen, Erling D.: Solving conic optimization problems via self-dual embedding and facial reduction: A unified approach (2017)
- Puranik, Yash; Sahinidis, Nikolaos V.: Domain reduction techniques for global NLP and MINLP optimization (2017)
- Vinkó, Tamás; Gelle, Kitti: Basin hopping networks of continuous global optimization problems (2017)
Further publications can be found at: http://plato.asu.edu/sub/tutorials.html