COPS

Benchmarking optimization software with COPS. We are continuing the development of COPS, a large-scale Constrained Optimization Problem Set. The primary purpose of this collection is to provide difficult test cases for optimization software. Problems in the current version of the collection come from fluid dynamics, population dynamics, optimal design, mesh smoothing, and optimal control. For each problem we provide a short description of the problem, notes on the formulation of the problem, and results of computational experiments with general optimization solvers. Each problem has been implemented in AMPL. The models from COPS 2.0 are also available in GAMS, courtesy of GAMS Development Corporation.


References in zbMATH (referenced in 36 articles )

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  1. García-Palomares, Ubaldo M.: Non-monotone derivative-free algorithm for solving optimization models with linear constraints: extensions for solving nonlinearly constrained models via exact penalty methods (2020)
  2. Gill, Philip E.; Kungurtsev, Vyacheslav; Robinson, Daniel P.: A shifted primal-dual penalty-barrier method for nonlinear optimization (2020)
  3. Griffin, Joshua; Omheni, Riadh: A primal-dual modified log-barrier method for inequality constrained nonlinear optimization (2020)
  4. Moriconi, Riccardo; Deisenroth, Marc Peter; Sesh Kumar, K. S.: High-dimensional Bayesian optimization using low-dimensional feature spaces (2020)
  5. Yuan, Gonglin; Li, Tingting; Hu, Wujie: A conjugate gradient algorithm for large-scale nonlinear equations and image restoration problems (2020)
  6. Pintér, János D.: How difficult is nonlinear optimization? A practical solver tuning approach, with illustrative results (2018)
  7. Armand, Paul; Omheni, Riadh: A mixed logarithmic barrier-augmented Lagrangian method for nonlinear optimization (2017)
  8. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  9. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  10. Puranik, Yash; Sahinidis, Nikolaos V.: Bounds tightening based on optimality conditions for nonconvex box-constrained optimization (2017)
  11. Arreckx, Sylvain; Lambe, Andrew; Martins, Joaquim R. R. A.; Orban, Dominique: A matrix-free augmented Lagrangian algorithm with application to large-scale structural design optimization (2016)
  12. Janka, Dennis; Kirches, Christian; Sager, Sebastian; Wächter, Andreas: An SR1/BFGS SQP algorithm for nonconvex nonlinear programs with block-diagonal Hessian matrix (2016)
  13. Qiu, Songqiang; Chen, Zhongwen: A globally convergent penalty-free method for optimization with equality constraints and simple bounds (2016)
  14. Curtis, Frank E.; Jiang, Hao; Robinson, Daniel P.: An adaptive augmented Lagrangian method for large-scale constrained optimization (2015)
  15. Gill, Philip E.; Wong, Elizabeth: Methods for convex and general quadratic programming (2015)
  16. Armand, Paul; Benoist, Joël; Omheni, Riadh; Pateloup, Vincent: Study of a primal-dual algorithm for equality constrained minimization (2014)
  17. Bussieck, Michael R.; Dirkse, Steven P.; Vigerske, Stefan: PAVER 2.0: an open source environment for automated performance analysis of benchmarking data (2014)
  18. Domes, Ferenc; Fuchs, Martin; Schichl, Hermann; Neumaier, Arnold: The optimization test environment (2014)
  19. Jan Fiala, Michal Kocvara, Michael Stingl: PENLAB: A MATLAB solver for nonlinear semidefinite optimization (2013) arXiv
  20. Kirches, Christian; Leyffer, Sven: TACO: a toolkit for AMPL control optimization (2013)

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