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 149 articles )

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  1. De Marchi, Alberto: On a primal-dual Newton proximal method for convex quadratic programs (2022)
  2. Li, Keke; Yu, Tiantang; Bui, Tinh Quoc: Adaptive XIGA shakedown analysis for problems with holes (2022)
  3. Lundell, Andreas; Kronqvist, Jan: Polyhedral approximation strategies for nonconvex mixed-integer nonlinear programming in SHOT (2022)
  4. Garstka, Michael; Cannon, Mark; Goulart, Paul: COSMO: a conic operator splitting method for convex conic problems (2021)
  5. Gleixner, Ambros; Hendel, Gregor; Gamrath, Gerald; Achterberg, Tobias; Bastubbe, Michael; Berthold, Timo; Christophel, Philipp; Jarck, Kati; Koch, Thorsten; Linderoth, Jeff; Lübbecke, Marco; Mittelmann, Hans D.; Ozyurt, Derya; Ralphs, Ted K.; Salvagnin, Domenico; Shinano, Yuji: MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library (2021)
  6. Kobayashi, Ken; Takano, Yuichi; Nakata, Kazuhide: Bilevel cutting-plane algorithm for cardinality-constrained mean-CVaR portfolio optimization (2021)
  7. Mihić, Krešimir; Zhu, Mingxi; Ye, Yinyu: Managing randomization in the multi-block alternating direction method of multipliers for quadratic optimization (2021)
  8. Polyak, B. T.; Khlebnikov, M. V.; Shcherbakov, P. S.: Linear matrix inequalities in control systems with uncertainty (2021)
  9. Scheiderer, Claus: Second-order cone representation for convex sets in the plane (2021)
  10. Şeker, Oylum; Ekim, Tınaz; Taşkın, Z. Caner: An exact cutting plane algorithm to solve the selective graph coloring problem in perfect graphs (2021)
  11. Zhang, Richard Y.; Lavaei, Javad: Sparse semidefinite programs with guaranteed near-linear time complexity via dualized clique tree conversion (2021)
  12. Bruno, Hugo; Barros, Guilherme; Menezes, Ivan F. M.; Martha, Luiz Fernando: Return-mapping algorithms for associative isotropic hardening plasticity using conic optimization (2020)
  13. Eltved, Anders; Dahl, Joachim; Andersen, Martin S.: On the robustness and scalability of semidefinite relaxation for optimal power flow problems (2020)
  14. 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)
  15. Kobayashi, Ken; Takano, Yuich: A branch-and-cut algorithm for solving mixed-integer semidefinite optimization problems (2020)
  16. Mittelmann, Hans D.: Benchmarking optimization software -- a (Hi)story (2020)
  17. Stellato, Bartolomeo; Banjac, Goran; Goulart, Paul; Bemporad, Alberto; Boyd, Stephen: OSQP: an operator splitting solver for quadratic programs (2020)
  18. Walteros, Jose L.; Buchanan, Austin: Why is maximum clique often easy in practice? (2020)
  19. Averkov, Gennadiy: Optimal size of linear matrix inequalities in semidefinite approaches to polynomial optimization (2019)
  20. Fukasawa, Ricardo; Poirrier, Laurent: Permutations in the factorization of simplex bases (2019)

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