PLCP

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 211 articles , 1 standard article )

Showing results 1 to 20 of 211.
Sorted by year (citations)

1 2 3 ... 9 10 11 next

  1. Yin, Jiang-hua; Jian, Jin-bao; Jiang, Xian-zhen: A generalized hybrid CGPM-based algorithm for solving large-scale convex constrained equations with applications to image restoration (2021)
  2. Almeida Guimarães, Dilson; Salles da Cunha, Alexandre; Pereira, Dilson Lucas: Semidefinite programming lower bounds and branch-and-bound algorithms for the quadratic minimum spanning tree problem (2020)
  3. Barbarosie, Cristian; Toader, Anca-Maria; Lopes, Sérgio: A gradient-type algorithm for constrained optimization with application to microstructure optimization (2020)
  4. Chouzenoux, Emilie; Corbineau, Marie-Caroline; Pesquet, Jean-Christophe: A proximal interior point algorithm with applications to image processing (2020)
  5. Chrétien, Stéphane; Clarkson, Paul: A fast algorithm for the semi-definite relaxation of the state estimation problem in power grids (2020)
  6. Deng, Hao; Hinnebusch, Shawn; To, Albert C.: Topology optimization design of stretchable metamaterials with Bézier skeleton explicit density (BSED) representation algorithm (2020)
  7. Feppon, Florian; Allaire, Grégoire; Dapogny, Charles: Null space gradient flows for constrained optimization with applications to shape optimization (2020)
  8. Finardi, E. C.; Lobato, R. D.; de Matos, V. L.; Sagastizábal, C.; Tomasgard, A.: Stochastic hydro-thermal unit commitment via multi-level scenario trees and bundle regularization (2020)
  9. Gaar, Elisabeth; Rendl, Franz: A computational study of exact subgraph based SDP bounds for max-cut, stable set and coloring (2020)
  10. Gdawiec, Krzysztof; Shahid, Abdul Aziz; Nazeer, Waqas: Higher order methods of the basic family of iterations via (S)-iteration scheme with (s)-convexity (2020)
  11. Hare, Warren; Planiden, Chayne; Sagastizábal, Claudia: A derivative-free (\mathcalV\mathcalU)-algorithm for convex finite-max problems (2020)
  12. Helou, Elias S.; Santos, Sandra A.; Simões, Lucas E. A.: A new sequential optimality condition for constrained nonsmooth optimization (2020)
  13. Ito, Kazufumi: Value function calculus and applications (2020)
  14. Kouri, Drew P.; Surowiec, Thomas M.: Epi-regularization of risk measures (2020)
  15. Luna, Juan Pablo; Sagastizábal, Claudia; Solodov, Mikhail: A class of Benders decomposition methods for variational inequalities (2020)
  16. Mitridati, Lesia; Kazempour, Jalal; Pinson, Pierre: Heat and electricity market coordination: a scalable complementarity approach (2020)
  17. Mohammadi, Ashkan; Mordukhovich, Boris S.; Sarabi, M. Ebrahim: Superlinear convergence of the sequential quadratic method in constrained optimization (2020)
  18. Orlov, Andrei V.: On a solving bilevel d.c.-convex optimization problems (2020)
  19. Pang, Li-Ping; Wu, Qi; Wang, Jin-He; Wu, Qiong: A discretization algorithm for nonsmooth convex semi-infinite programming problems based on bundle methods (2020)
  20. Pay, Babak Saleck; Song, Yongjia: Partition-based decomposition algorithms for two-stage stochastic integer programs with continuous recourse (2020)

1 2 3 ... 9 10 11 next