NEOS Server: State-of-the-Art Solvers for Numerical Optimization. The NEOS Server is a free internet-based service for solving numerical optimization problems. Hosted by the Wisconsin Institutes for Discovery at the University of Wisconsin in Madison, the NEOS Server provides access to more than 60 state-of-the-art solvers in more than a dozen optimization categories. The NEOS Server offers a variety of interfaces for accessing the solvers. Solvers hosted by the University of Wisconsin in Madison run on distributed high-performance machines enabled by the HTCondor software; remote solvers run on machines at Argonne National Laboratory, Arizona State University, the University of Klagenfurt in Austria, and the University of Minho in Portugal. The NEOS Guide web site complements the NEOS Server, showcasing optimization case studies, presenting optimization information and resources, and providing background information on the NEOS Server

References in zbMATH (referenced in 92 articles , 1 standard article )

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  1. Hartillo-Hermoso, María Isabel; Jiménez-Tafur, Haydee; Ucha-Enríquez, José María: An exact algebraic (\epsilon)-constraint method for bi-objective linear integer programming based on test sets (2020)
  2. Mittelmann, Hans D.: Benchmarking optimization software -- a (Hi)story (2020)
  3. Baumgartner, János; Süle, Zoltán; Bertók, Botond; Abonyi, János: Test-sequence optimisation by survival analysis (2019)
  4. Kancharla, Surendra Reddy; Ramadurai, Gitakrishnan: Multi-depot two-echelon fuel minimizing routing problem with heterogeneous fleets: model and heuristic (2019)
  5. Maniezzo, Vittorio; Boschetti, Marco A.; Carbonaro, Antonella; Marzolla, Moreno; Strappaveccia, Francesco: Client-side computational optimization (2019)
  6. Saygı, Elif: On the domination number and the total domination number of Fibonacci cubes (2019)
  7. Anyenya, Gladys A.; Braun, Robert J.; Lee, Kyung Jae; Sullivan, Neal P.; Newman, Alexandra M.: Design and dispatch optimization of a solid-oxide fuel cell assembly for unconventional oil and gas production (2018)
  8. Arpón, Sebastián; Homem-de-Mello, Tito; Pagnoncelli, Bernardo: Scenario reduction for stochastic programs with conditional value-at-risk (2018)
  9. Dufossé, Fanny; Kaya, Kamer; Panagiotas, Ioannis; Uçar, Bora: Further notes on Birkhoff-von Neumann decomposition of doubly stochastic matrices (2018)
  10. Bertsimas, Dimitris; King, Angela: Logistic regression: from art to science (2017)
  11. Hanks, Robert W.; Weir, Jeffery D.; Lunday, Brian J.: Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets (2017)
  12. Le Thi, Hoai An; Pham Dinh, Tao: Difference of convex functions algorithms (DCA) for image restoration via a Markov random field model (2017)
  13. Newby, Eric; Ali, M. M.: Linear transformation based solution methods for non-convex mixed integer quadratic programs (2017)
  14. Bugarin, Florian; Henrion, Didier; Lasserre, Jean Bernard: Minimizing the sum of many rational functions (2016)
  15. Gassmann, Horand; Ma, Jun; Martin, Kipp: Communication protocols for options and results in a distributed optimization environment (2016)
  16. Hu, T. C.; Kahng, Andrew B.: Linear and integer programming made easy (2016)
  17. Mezzadri, Francesco; Galligani, Emanuele: A Chebyshev technique for the solution of optimal control problems with nonlinear programming methods (2016)
  18. Yoda, Kunikazu; Prékopa, András: Convexity and solutions of stochastic multidimensional 0-1 knapsack problems with probabilistic constraints (2016)
  19. Zhang, Jiaxiang; Qiao, Wei; Wen, John T.; Julius, Agung: Light-based circadian rhythm control: entrainment and optimization (2016)
  20. Cai, Yongyang; Judd, Kenneth L.: Dynamic programming with Hermite approximation (2015)

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