The software contains some functions and drivers for solving LP problems of the form min c’x s.t Ax=b; x>=0 by a large neihghborhood infeasible predictor_corrector algorithm. It is based on Newton steps on the perturbed optimality system x.*s = m * 1 Ax = b c + A’lambda = s x>= 0 , s>=0 m-->0 The matrix A may be either full or sparse; computations are made accordingly. This is a software based on either SCILAB or matlab for solving large scale linear programming problems. It can be freely used for non commercial use.

References in zbMATH (referenced in 163 articles , 1 standard article )

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  1. Cegielski, Andrzej; Nimana, Nimit: Extrapolated cyclic subgradient projection methods for the convex feasibility problems and their numerical behaviour (2019)
  2. Effio Saldivar, Carolina; Herskovits, José; Luna, Juan Pablo; Sagastizábal, Claudia: Multidimensional calibration of crude oil and refined products via semidefinite programming techniques (2019)
  3. Ellabib, Abdellatif; Ouakrim, Youssef: Inverse problem for a class of nonlinear elliptic equations with entropy solution (2019)
  4. Grimm, Veronika; Kleinert, Thomas; Liers, Frauke; Schmidt, Martin; Zöttl, Gregor: Optimal price zones of electricity markets: a mixed-integer multilevel model and global solution approaches (2019)
  5. Wang, Guanglei; Ben-Ameur, Walid; Ouorou, Adam: A Lagrange decomposition based branch and bound algorithm for the optimal mapping of cloud virtual machines (2019)
  6. Antczak, T.: Exactness property of the exact absolute value penalty function method for solving convex nondifferentiable interval-valued optimization problems (2018)
  7. Asllanaj, Fatmir; Addoum, Ahmad; Roche, Jean Rodolphe: Fluorescence molecular imaging based on the adjoint radiative transport equation (2018)
  8. Bolte, Jérôme; Hochart, Antoine; Pauwels, Edouard: Qualification conditions in semialgebraic programming (2018)
  9. Delfino, A.; de Oliveira, W.: Outer-approximation algorithms for nonsmooth convex MINLP problems (2018)
  10. Hare, W.; Planiden, C.: Computing proximal points of convex functions with inexact subgradients (2018)
  11. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A fast gradient and function sampling method for finite-max functions (2018)
  12. Jian, Jin-bao; Tang, Chun-ming; Shi, Lu: A feasible point method with bundle modification for nonsmooth convex constrained optimization (2018)
  13. Kolosnitsyn, A. V.: Computational efficiency of the simplex embedding method in convex nondifferentiable optimization (2018)
  14. Shi, Y.; Tuan, H. D.; Apkarian, P.; Savkin, A. V.: Global optimal power flow over large-scale power transmission networks (2018)
  15. Trélat, Emmanuel; Zhu, Jiamin; Zuazua, Enrique: Allee optimal control of a system in ecology (2018)
  16. van Ackooij, W.; Danti Lopez, I.; Frangioni, A.; Lacalandra, F.; Tahanan, M.: Large-scale unit commitment under uncertainty: an updated literature survey (2018)
  17. Bajaj, Anuj; Hare, Warren; Lucet, Yves: Visualization of the (\varepsilon)-subdifferential of piecewise linear-quadratic functions (2017)
  18. Bonnans, J. Frédéric; Festa, Adriano: Error estimates for the Euler discretization of an optimal control problem with first-order state constraints (2017)
  19. Carlier, Guillaume; Dupuis, Xavier: An iterated projection approach to variational problems under generalized convexity constraints (2017)
  20. Chen, Xiang; Zhang, Xiong: An improved 2D MoF method by using high order derivatives (2017)

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