BENSOLVE is a solver for vector linear programs (VLP), in particular, for the subclass of multiple objective linear programs (MOLP). It is based on Benson’s algorithm and its extensions. BENSOLVE is a free software published under the terms of the GNU General Public License. It utilizes the GNU Linear Programming Kit (GLPK). BENSOLVE (from version 2) is written in C programming language. BENSOLVE provides the following features: arbitrary pointed solid polyhedral ordering cones; primal and dual algorithms; primal and dual solutions; support of unbounded problems; 3D graphics format output

References in zbMATH (referenced in 75 articles , 1 standard article )

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  1. Crespi, Giovanni Paolo; Hamel, Andreas H.; Rocca, Matteo; Schrage, Carola: Set relations via families of scalar functions and approximate solutions in set optimization (2021)
  2. Crespi, Giovanni P.; Schrage, Carola: Applying set optimization to weak efficiency (2021)
  3. Csirmaz, Laszlo: Inner approximation algorithm for solving linear multiobjective optimization problems (2021)
  4. López, Rubén; Sama, Miguel: Horizon maps and graphical convergence revisited (2021)
  5. Som, Kuntal; Vetrivel, V.: On robustness for set-valued optimization problems (2021)
  6. Yao, Chaoli; Tang, Chunlei; Chen, Jiawei: Abstract convexity of set-valued topical functions with application in DC-type optimization (2021)
  7. Al-Homidan, Suliman; Ansari, Qamrul Hasan; Kassay, Gabor: Vectorial form of Ekeland variational principle with applications to vector equilibrium problems (2020)
  8. Hamel, Andreas H. (ed.); Löhne, Andreas (ed.): Choosing sets: preface to the special issue on set optimization and applications (2020)
  9. Ha, Truong Xuan Duc: A new concept of slope for set-valued maps and applications in set optimization studied with Kuroiwa’s set approach (2020)
  10. Kostner, Daniel: Multi-criteria decision making via multivariate quantiles (2020)
  11. Liesiö, Juuso; Andelmin, Juho; Salo, Ahti: Efficient allocation of resources to a portfolio of decision making units (2020)
  12. Noskov, S. I.: Compromise Pareto’s evaluation of parameters linear regression (2020)
  13. vom Dahl, Simeon; Löhne, Andreas: Solving polyhedral d.c. optimization problems via concave minimization (2020)
  14. Weißing, Benjamin: The polyhedral projection problem (2020)
  15. Balestro, Vitor; Martini, Horst; Teixeira, Ralph: Optimal constants in normed planes (2019)
  16. Ciripoi, Daniel; Kaihnsa, Nidhi; Löhne, Andreas; Sturmfels, Bernd: Computing convex hulls of trajectories (2019)
  17. Ciripoi, Daniel; Löhne, Andreas; Weißing, Benjamin: Calculus of convex polyhedra and polyhedral convex functions by utilizing a multiple objective linear programming solver (2019)
  18. Dinh, Nguyen; Goberna, Miguel A.; Long, Dang H.; López-Cerdá, Marco A.: New Farkas-type results for vector-valued functions: a non-abstract approach (2019)
  19. Geoffroy, Michel H.: A topological convergence on power sets well-suited for set optimization (2019)
  20. Piercy, Craig A.; Steuer, Ralph E.: Reducing wall-clock time for the computation of all efficient extreme points in multiple objective linear programming (2019)

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