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 122 articles )

Showing results 1 to 20 of 122.
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  1. Berthold, Timo: A computational study of primal heuristics inside an MI(NL)P solver (2018)
  2. Huangfu, Q.; Hall, J. A. J.: Parallelizing the dual revised simplex method (2018)
  3. Permenter, Frank; Parrilo, Pablo: Partial facial reduction: simplified, equivalent SDPs via approximations of the PSD cone (2018)
  4. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  5. Cheung, Kevin K. H.; Gleixner, Ambros; Steffy, Daniel E.: Verifying integer programming results (2017)
  6. Gamrath, Gerald; Koch, Thorsten; Maher, Stephen J.; Rehfeldt, Daniel; Shinano, Yuji: SCIP-Jack -- a solver for STP and variants with parallelization extensions (2017)
  7. Hijazi, Hassan; Coffrin, Carleton; Van Hentenryck, Pascal: Convex quadratic relaxations for mixed-integer nonlinear programs in power systems (2017)
  8. Mohammad-Nezhad, Ali; Terlaky, Tamás: A polynomial primal-dual affine scaling algorithm for symmetric conic optimization (2017)
  9. Permenter, Frank; Friberg, Henrik A.; Andersen, Erling D.: Solving conic optimization problems via self-dual embedding and facial reduction: A unified approach (2017)
  10. Vinkó, Tamás; Gelle, Kitti: Basin hopping networks of continuous global optimization problems (2017)
  11. Kevin K. H. Cheung, Ambros Gleixner, Daniel E. Steffy: Verifying Integer Programming Results (2016) arXiv
  12. Klep, Igor; Povh, Janez: Constrained trace-optimization of polynomials in freely noncommuting variables (2016)
  13. Ku, Wen-Yang; Beck, J. Christopher: Mixed integer programming models for job shop scheduling: A computational analysis (2016)
  14. O’Donoghue, Brendan; Chu, Eric; Parikh, Neal; Boyd, Stephen: Conic optimization via operator splitting and homogeneous self-dual embedding (2016)
  15. Santos, Haroldo G.; Toffolo, Túlio A. M.; Gomes, Rafael A. M.; Ribas, Sabir: Integer programming techniques for the nurse rostering problem (2016)
  16. Lubin, Miles; Dunning, Iain: Computing in operations research using Julia (2015)
  17. Mamalis, Basilis; Pantziou, Grammati: Advances in the parallelization of the simplex method (2015)
  18. Berthold, Timo: RENS. The optimal rounding (2014)
  19. Bussieck, Michael R.; Dirkse, Steven P.; Vigerske, Stefan: PAVER 2.0: an open source environment for automated performance analysis of benchmarking data (2014)
  20. Carvajal, R.; Ahmed, S.; Nemhauser, G.; Furman, K.; Goel, V.; Shao, Y.: Using diversification, communication and parallelism to solve mixed-integer linear programs (2014)

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Further publications can be found at: http://plato.asu.edu/sub/tutorials.html