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 230 articles , 1 standard article )

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  1. Salles da Cunha, Alexandre; Lucena, Abilio: Modeling and solving the angular constrained minimum spanning tree problem (2019)
  2. Veremyev, Alexander; Pavlikov, Konstantin; Pasiliao, Eduardo L.; Thai, My T.; Boginski, Vladimir: Critical nodes in interdependent networks with deterministic and probabilistic cascading failures (2019)
  3. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2018)
  4. Berthold, Timo; Hendel, Gregor; Koch, Thorsten: From feasibility to improvement to proof: three phases of solving mixed-integer programs (2018)
  5. Berthold, Timo; Perregaard, Michael; Mészáros, Csaba: Four good reasons to use an interior point solver within a MIP solver (2018)
  6. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
  7. Huangfu, Q.; Hall, J. A. J.: Parallelizing the dual revised simplex method (2018)
  8. Lancia, Giuseppe; Serafini, Paolo: Compact extended linear programming models (2018)
  9. Lubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pablo: Polyhedral approximation in mixed-integer convex optimization (2018)
  10. Pavlikov, Konstantin; Uryasev, Stan: CVaR distance between univariate probability distributions and approximation problems (2018)
  11. Pelegrín, Blas; Fernández, Pascual; García, María Dolores: Computation of multi-facility location Nash equilibria on a network under quantity competition (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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