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, ..

References in zbMATH (referenced in 139 articles )

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  1. Polyak, B. T.; Khlebnikov, M. V.; Shcherbakov, P. S.: Linear matrix inequalities in control systems with uncertainty (2021)
  2. Scheiderer, Claus: Second-order cone representation for convex sets in the plane (2021)
  3. Bruno, Hugo; Barros, Guilherme; Menezes, Ivan F. M.; Martha, Luiz Fernando: Return-mapping algorithms for associative isotropic hardening plasticity using conic optimization (2020)
  4. Eltved, Anders; Dahl, Joachim; Andersen, Martin S.: On the robustness and scalability of semidefinite relaxation for optimal power flow problems (2020)
  5. 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)
  6. Kobayashi, Ken; Takano, Yuich: A branch-and-cut algorithm for solving mixed-integer semidefinite optimization problems (2020)
  7. Mittelmann, Hans D.: Benchmarking optimization software -- a (Hi)story (2020)
  8. Stellato, Bartolomeo; Banjac, Goran; Goulart, Paul; Bemporad, Alberto; Boyd, Stephen: OSQP: an operator splitting solver for quadratic programs (2020)
  9. Walteros, Jose L.; Buchanan, Austin: Why is maximum clique often easy in practice? (2020)
  10. Averkov, Gennadiy: Optimal size of linear matrix inequalities in semidefinite approaches to polynomial optimization (2019)
  11. Fukasawa, Ricardo; Poirrier, Laurent: Permutations in the factorization of simplex bases (2019)
  12. Kuhlmann, Renke: Learning to steer nonlinear interior-point methods (2019)
  13. Pereira Coutinho, Walton; Fliege, Jörg; Battarra, Maria: Glider routing and trajectory optimisation in disaster assessment (2019)
  14. Van Bulck, David; Goossens, Dries R.; Spieksma, Frits C. R.: Scheduling a non-professional indoor football league: a tabu search based approach (2019)
  15. Waki, Hayato; Sebe, Noboru: Application of facial reduction to (H_\infty) state feedback control problem (2019)
  16. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  17. Huangfu, Q.; Hall, J. A. J.: Parallelizing the dual revised simplex method (2018)
  18. Permenter, Frank; Parrilo, Pablo: Partial facial reduction: simplified, equivalent SDPs via approximations of the PSD cone (2018)
  19. Shinano, Yuji; Heinz, Stefan; Vigerske, Stefan; Winkler, Michael: FiberSCIP -- a shared memory parallelization of SCIP (2018)
  20. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)

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