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 141 articles )
Showing results 1 to 20 of 141.
Sorted by year (- Polyak, B. T.; Khlebnikov, M. V.; Shcherbakov, P. S.: Linear matrix inequalities in control systems with uncertainty (2021)
- Scheiderer, Claus: Second-order cone representation for convex sets in the plane (2021)
- Şeker, Oylum; Ekim, Tınaz; Taşkın, Z. Caner: An exact cutting plane algorithm to solve the selective graph coloring problem in perfect graphs (2021)
- Zhang, Richard Y.; Lavaei, Javad: Sparse semidefinite programs with guaranteed near-linear time complexity via dualized clique tree conversion (2021)
- 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)
- Mittelmann, Hans D.: Benchmarking optimization software -- a (Hi)story (2020)
- Stellato, Bartolomeo; Banjac, Goran; Goulart, Paul; Bemporad, Alberto; Boyd, Stephen: OSQP: an operator splitting solver for quadratic programs (2020)
- Walteros, Jose L.; Buchanan, Austin: Why is maximum clique often easy in practice? (2020)
- Averkov, Gennadiy: Optimal size of linear matrix inequalities in semidefinite approaches to polynomial optimization (2019)
- Fukasawa, Ricardo; Poirrier, Laurent: Permutations in the factorization of simplex bases (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)
Further publications can be found at: http://plato.asu.edu/sub/tutorials.html