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 111 articles , 1 standard article )

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  1. Apkarian, Pierre; Noll, Dominikus; Ravanbod, Laleh: Nonsmooth bundle trust-region algorithm with applications to robust stability (2016)
  2. Bolte, Jér^ome; Pauwels, Edouard: Majorization-minimization procedures and convergence of SQP methods for semi-algebraic and tame programs (2016)
  3. Burclová, Katarína; Pázman, Andrej: Optimal design of experiments via linear programming (2016)
  4. Dentcheva, Darinka; Wolfhagen, Eli: Two-stage optimization problems with multivariate stochastic order constraints (2016)
  5. de Oliveira, Welington; Solodov, Mikhail: A doubly stabilized bundle method for nonsmooth convex optimization (2016)
  6. Fliege, Jörg; Vaz, A.Ismael F.: A method for constrained multiobjective optimization based on SQP techniques (2016)
  7. Gospodarczyk, Przemysław; Lewanowicz, Stanisław; Woźny, Paweł: $G^k,l$-constrained multi-degree reduction of Bézier curves (2016)
  8. Griewank, Andreas; Walther, Andrea; Fiege, Sabrina; Bosse, Torsten: On Lipschitz optimization based on gray-box piecewise linearization (2016)
  9. Hojny, Christopher; Pfetsch, Marc E.: A polyhedral investigation of star colorings (2016)
  10. Izmailov, A.F.; Solodov, M.V.; Uskov, E.I.: Globalizing stabilized sequential quadratic programming method by smooth primal-dual exact penalty function (2016)
  11. Curtis, Frank E.; Que, Xiaocun: A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees (2015)
  12. Izmailov, A.F.; Solodov, M.V.: Newton-type methods: a broader view (2015)
  13. Kolosnitcyn, Anton Vasilevich: Using of modified simplex imbeddings method for solving special class of convex non-differentiable optimization problems (2015)
  14. Pang, Li-Ping; Chen, Shuang; Wang, Jin-He: Risk management in portfolio applications of non-convex stochastic programming (2015)
  15. Strekalovsky, A.S.; Gruzdeva, T.V.; Orlov, A.V.: On the problem polyhedral separability: a numerical solution (2015)
  16. Tahanan, Milad; van Ackooij, Wim; Frangioni, Antonio; Lacalandra, Fabrizio: Large-scale unit commitment under uncertainty (2015)
  17. Wang, Yuting; Garcia, Alfredo: Interactive model-based search for global optimization (2015)
  18. Baus, F.; Nikolova, M.; Steidl, G.: Fully smoothed $\ell_1$-$TV$ models: bounds for the minimizers and parameter choice (2014)
  19. Birk, Matthias; Dapp, Robin; Ruiter, N.V.; Becker, J.: GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography (2014)
  20. Chen, Zhenhua; An, Kaiqi; Liu, Yuan; Chen, Wenbin: Adjoint method for an inverse problem of CCPF model (2014)

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