CPLEX

IBM® ILOG® CPLEX® offers C, C++, Java, .NET, and Python libraries that solve linear programming (LP) and related problems. Specifically, it solves linearly or quadratically constrained optimization problems where the objective to be optimized can be expressed as a linear function or a convex quadratic function. The variables in the model may be declared as continuous or further constrained to take only integer values.


References in zbMATH (referenced in 1806 articles , 1 standard article )

Showing results 1 to 20 of 1806.
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  1. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
  2. Albrecht, Wolfgang; Steinrücke, Martin: Continuous-time production, distribution and financial planning with periodic liquidity balancing (2017)
  3. Al-Dhaheri, Noura; Diabat, Ali: A Lagrangian relaxation-based heuristic for the multi-ship quay crane scheduling problem with ship stability constraints (2017)
  4. Amir Ali Ahmadi, Anirudha Majumdar: DSOS and SDSOS Optimization: More Tractable Alternatives to Sum of Squares and Semidefinite Optimization (2017) arXiv
  5. Assarf, Benjamin; Gawrilow, Ewgenij; Herr, Katrin; Joswig, Michael; Lorenz, Benjamin; Paffenholz, Andreas; Rehn, Thomas: Computing convex hulls and counting integer points with polymake (2017)
  6. Atakan, Semih; Bülbül, Kerem; Noyan, Nilay: Minimizing value-at-risk in single-machine scheduling (2017)
  7. Bekos, Michael A.; Kaufmann, Michael; Kobourov, Stephen G.; Stavropoulos, Konstantinos; Veeramoni, Sankar: The maximum $k$-differential coloring problem (2017)
  8. Billionnet, Alain; Elloumi, Sourour; Lambert, Amélie; Wiegele, Angelika: Using a conic bundle method to accelerate both phases of a quadratic convex reformulation (2017)
  9. Boyland, Peter; Pintér, Gabriella; Laukó, István; Roth, Ivan; Schoenfield, Jon E.; Wasielewski, Stephen: On the maximum number of non-intersecting diagonals in an array (2017)
  10. Bruni, M.E.; Beraldi, P.; Conforti, D.: Water distribution networks design under uncertainty (2017)
  11. Bülbül, Kerem; Şen, Halil: An exact extended formulation for the unrelated parallel machine total weighted completion time problem (2017)
  12. Cheung, Kevin K.H.; Gleixner, Ambros; Steffy, Daniel E.: Verifying integer programming results (2017)
  13. Czibula, Oliver; Gu, Hanyu; Russell, Aaron; Zinder, Yakov: A multi-stage IP-based heuristic for class timetabling and trainer rostering (2017)
  14. Edirisinghe, Chanaka; Jeong, Jaehwan: Tight bounds on indefinite separable singly-constrained quadratic programs in linear-time (2017)
  15. Ernestus, Maximilian; Friedrichs, Stephan; Hemmer, Michael; Kokemüller, Jan; Kröller, Alexander; Moeini, Mahdi; Schmidt, Christiane: Algorithms for art gallery illumination (2017)
  16. Glenn, Paul; Menasco, William W.; Morrell, Kayla; Morse, Matthew J.: MICC: a tool for computing short distances in the curve complex (2017)
  17. Göttlich, Simone; Potschka, Andreas; Ziegler, Ute: Partial outer convexification for traffic light optimization in road networks (2017)
  18. Gould, Nicholas I.M.; Robinson, Daniel P.: A dual gradient-projection method for large-scale strictly convex quadratic problems (2017)
  19. Hamdi, Imen; Loukil, Taïcir: The permutation flowshop scheduling problem with exact time lags to minimise the total earliness and tardiness (2017)
  20. Hart, William E.; Laird, Carl D.; Watson, Jean-Paul; Woodruff, David L.; Hackebeil, Gabriel A.; Nicholson, Bethany L.; Siirola, John D.: Pyomo -- optimization modeling in Python (2017)

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