XPRESS

FICO Xpress is the premier mathematical modeling and optimization software suite in the world, with the best tools available to aid the development and deployment of optimization applications that solve real-world challenges. FICO Xpress helps organizations solve bigger problems, design applications faster and make even better decisions in virtually any business scenario. Xpress Optimization Suite includes two types of tools: model building and development tools, and solver engines.


References in zbMATH (referenced in 198 articles , 1 standard article )

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  1. Castro, Jordi; González, José A.: A linear optimization-based method for data privacy in statistical tabular data (2019)
  2. Horváth, Markó; Kis, Tamás: Computing strong lower and upper bounds for the integrated multiple-depot vehicle and crew scheduling problem with branch-and-price (2019)
  3. Molnár-Szipai, Richárd; Varga, Anita: Integrating combinatorial algorithms into a linear programming solver (2019)
  4. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2018)
  5. Berthold, Timo; Hendel, Gregor; Koch, Thorsten: From feasibility to improvement to proof: three phases of solving mixed-integer programs (2018)
  6. Berthold, Timo; Perregaard, Michael; Mészáros, Csaba: Four good reasons to use an interior point solver within a MIP solver (2018)
  7. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
  8. Huangfu, Q.; Hall, J. A. J.: Parallelizing the dual revised simplex method (2018)
  9. Lancia, Giuseppe; Serafini, Paolo: Compact extended linear programming models (2018)
  10. Lubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pablo: Polyhedral approximation in mixed-integer convex optimization (2018)
  11. Pavlikov, Konstantin; Uryasev, Stan: CVaR distance between univariate probability distributions and approximation problems (2018)
  12. Pinto, Bruno Q.; Ribeiro, Celso C.; Rosseti, Isabel; Plastino, Alexandre: A biased random-key genetic algorithm for the maximum quasi-clique problem (2018)
  13. Sáez-Aguado, Jesús; Trandafir, Paula Camelia: Variants of the (\varepsilon)-constraint method for biobjective integer programming problems: application to (p)-median-cover problems (2018)
  14. Sawik, Tadeusz: Supply chain disruption management using stochastic mixed integer programming (2018)
  15. Shinano, Yuji; Berthold, Timo; Heinz, Stefan: ParaXpress: an experimental extension of the FICO Xpress-Optimizer to solve hard MIPs on supercomputers (2018)
  16. Zhou, Kai; Kılınç, Mustafa R.; Chen, Xi; Sahinidis, Nikolaos V.: An efficient strategy for the activation of MIP relaxations in a multicore global MINLP solver (2018)
  17. Agra, Agostinho; Cerdeira, Jorge Orestes; Requejo, Cristina: A decomposition approach for the (p)-median problem on disconnected graphs (2017)
  18. Balasubramaniam, Chitra; Butenko, Sergiy: On robust clusters of minimum cardinality in networks (2017)
  19. Fernández, Pascual; Pelegrín, Blas; Lančinskas, Algirdas; Žilinskas, Julius: New heuristic algorithms for discrete competitive location problems with binary and partially binary customer behavior (2017)
  20. Fortz, Bernard; Oliveira, Olga; Requejo, Cristina: Compact mixed integer linear programming models to the minimum weighted tree reconstruction problem (2017)

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