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 224 articles , 1 standard article )

Showing results 1 to 20 of 224.
Sorted by year (citations)

1 2 3 ... 10 11 12 next

  1. Fernández, Pascual; Pelegrín, Blas; Lančinskas, Algirdas; Žilinskas, Julius: Exact and heuristic solutions of a discrete competitive location model with Pareto-Huff customer choice rule (2021)
  2. Maher, Stephen J.: Implementing the branch-and-cut approach for a general purpose Benders’ decomposition framework (2021)
  3. Rodríguez, Jesús A.; Anjos, Miguel F.; Côté, Pascal; Desaulniers, Guy: Accelerating Benders decomposition for short-term hydropower maintenance scheduling (2021)
  4. Wolsey, Laurence A.: Integer programming (2021)
  5. Basso, S.; Ceselli, Alberto; Tettamanzi, Andrea: Random sampling and machine learning to understand good decompositions (2020)
  6. Cappanera, Paola; Requejo, Cristina; Scutellà, Maria Grazia: Temporal constraints and device management for the skill VRP: mathematical model and lower bounding techniques (2020)
  7. Melo, Wendel; Fampa, Marcia; Raupp, Fernanda: An overview of MINLP algorithms and their implementation in Muriqui optimizer (2020)
  8. van der Linden, Wim J.; Jiang, Bingnan: A shadow-test approach to adaptive item calibration (2020)
  9. Agra, Agostinho; Cerdeira, Jorge Orestes; Requejo, Cristina: A computational comparison of compact MILP formulations for the zero forcing number (2019)
  10. Andrade, Tiago; Oliveira, Fabricio; Hamacher, Silvio; Eberhard, Andrew: Enhancing the normalized multiparametric disaggregation technique for mixed-integer quadratic programming (2019)
  11. Castro, Jordi; González, José A.: A linear optimization-based method for data privacy in statistical tabular data (2019)
  12. Furini, Fabio; Traversi, Emiliano; Belotti, Pietro; Frangioni, Antonio; Gleixner, Ambros; Gould, Nick; Liberti, Leo; Lodi, Andrea; Misener, Ruth; Mittelmann, Hans; Sahinidis, Nikolaos V.; Vigerske, Stefan; Wiegele, Angelika: QPLIB: a library of quadratic programming instances (2019)
  13. 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)
  14. Molnár-Szipai, Richárd; Varga, Anita: Integrating combinatorial algorithms into a linear programming solver (2019)
  15. Salles da Cunha, Alexandre; Lucena, Abilio: Modeling and solving the angular constrained minimum spanning tree problem (2019)
  16. Veremyev, Alexander; Pavlikov, Konstantin; Pasiliao, Eduardo L.; Thai, My T.; Boginski, Vladimir: Critical nodes in interdependent networks with deterministic and probabilistic cascading failures (2019)
  17. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2018)
  18. Berthold, Timo; Hendel, Gregor; Koch, Thorsten: From feasibility to improvement to proof: three phases of solving mixed-integer programs (2018)
  19. Berthold, Timo; Perregaard, Michael; Mészáros, Csaba: Four good reasons to use an interior point solver within a MIP solver (2018)
  20. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)

1 2 3 ... 10 11 12 next