Splitting dense columns of constraint matrix in interior point methods for large scale linear programming11The results discussed in the paper have been obtained when the author was staying at LAMSADE, University of Paris Dauphine, Pl

DOI10.1080/02331939208843796zbMath0814.65056OpenAlexW4244448023MaRDI QIDQ4327921

Jacek Gondzio

Publication date: 20 April 1995

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331939208843796

zbMATH Keywords

interior point methodCholesky factorlarge scale linear programmingdense columnsKarmarkov's projections

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Linear programming (90C05) Direct numerical methods for linear systems and matrix inversion (65F05)

Related Items

Numerical aspects in developing LP softwares, LPAKO and LPABO, Enhancing Block Cimmino for Sparse Linear Systems with Dense Columns via Schur Complement, HOPDM (version 2. 12) -- a fast LP solver based on a primal-dual interior point method, LP Formulations for Polynomial Optimization Problems, Implementing cholesky factorization for interior point methods of linear programming, HOPDM - a higher order primal-dual method for large scale linear programming, The role of the augmented system in interior point methods, A new efficient primal dual simplex algorithm

Uses Software

• symrcm

Cites Work

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• Splitting dense columns in sparse linear systems
• An implementation of Karmarkar's algorithm for linear programming
• On projected newton barrier methods for linear programming and an equivalence to Karmarkar’s projective method
• Scenarios and Policy Aggregation in Optimization Under Uncertainty
• Updating the Inverse of a Matrix
• Solving Sparse Symmetric Sets of Linear Equations by Preconditioned Conjugate Gradients
• Formulating Two-Stage Stochastic Programs for Interior Point Methods
• On Implementing Mehrotra’s Predictor–Corrector Interior-Point Method for Linear Programming