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 1754 articles , 1 standard article )

Showing results 1 to 20 of 1754.
Sorted by year (citations)

1 2 3 ... 86 87 88 next

  1. Ahmadi, Amir Ali; Hall, Georgina: Sum of squares basis pursuit with linear and second order cone programming (2017)
  2. Al-Dhaheri, Noura; Diabat, Ali: A Lagrangian relaxation-based heuristic for the multi-ship quay crane scheduling problem with ship stability constraints (2017)
  3. Atakan, Semih; Bülbül, Kerem; Noyan, Nilay: Minimizing value-at-risk in single-machine scheduling (2017)
  4. 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)
  5. Bruni, M.E.; Beraldi, P.; Conforti, D.: Water distribution networks design under uncertainty (2017)
  6. Ernestus, Maximilian; Friedrichs, Stephan; Hemmer, Michael; Kokemüller, Jan; Kröller, Alexander; Moeini, Mahdi; Schmidt, Christiane: Algorithms for art gallery illumination (2017)
  7. Glenn, Paul; Menasco, William W.; Morrell, Kayla; Morse, Matthew J.: MICC: a tool for computing short distances in the curve complex (2017)
  8. Göttlich, Simone; Potschka, Andreas; Ziegler, Ute: Partial outer convexification for traffic light optimization in road networks (2017)
  9. Hamdi, Imen; Loukil, Taïcir: The permutation flowshop scheduling problem with exact time lags to minimise the total earliness and tardiness (2017)
  10. 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)
  11. Humpola, Jesco; Serrano, Felipe: Sufficient pruning conditions for MINLP in gas network design (2017)
  12. Janhunen, Tomi; Gebser, Martin; Rintanen, Jussi; Nyman, Henrik; Pensar, Johan; Corander, Jukka: Learning discrete decomposable graphical models via constraint optimization (2017)
  13. Lima, Ricardo M.; Grossmann, Ignacio E.: On the solution of nonconvex cardinality Boolean quadratic programming problems: a computational study (2017)
  14. Li, M.; Liu, Q.: Inexact feasibility pump for mixed integer nonlinear programming (2017)
  15. Li, Xiang; Tomasgard, Asgeir; Barton, Paul I.: Natural gas production network infrastructure development under uncertainty (2017)
  16. Lu, Hao-Chun: Improved logarithmic linearizing method for optimization problems with free-sign pure discrete signomial terms (2017)
  17. Mencarelli, Luca; Sahraoui, Youcef; Liberti, Leo: A multiplicative weights update algorithm for MINLP (2017)
  18. Pan, Yunpeng; Liang, Zhe: Dual relaxations of the time-indexed ILP formulation for min-sum scheduling problems (2017)
  19. Santos, Rafael F.; Andrioni, Alessandro; Drummond, Andre C.; Xavier, Eduardo C.: Multicolour paths in graphs: NP-hardness, algorithms, and applications on routing in WDM networks (2017)
  20. Şeref, Onur; Razzaghi, Talayeh; Xanthopoulos, Petros: Weighted relaxed support vector machines (2017)

1 2 3 ... 86 87 88 next