A modification of karmarkar's linear programming algorithm

Robert J. Vanderbei; Marc S. Meketon; Barry A. Freedman

doi:10.1007/BF01840454

A modification of karmarkar's linear programming algorithm

Robert J. Vanderbei, Marc S. Meketon, Barry A. Freedman

Research output: Contribution to journal › Article › peer-review

124 Scopus citations

Abstract

We present a modification of Karmarkar's linear programming algorithm. Our algorithm uses a recentered projected gradient approach thereby obviating a priori knowledge of the optimal objective function value. Assuming primal and dual nondegeneracy, we prove that our algorithm converges. We present computational comparisons between our algorithm and the revised simplex method. For small, dense constraint matrices we saw little difference between the two methods.

Original language	English (US)
Pages (from-to)	395-407
Number of pages	13
Journal	Algorithmica
Volume	1
Issue number	1-4
DOIs	https://doi.org/10.1007/BF01840454
State	Published - Nov 1 1986
Externally published	Yes

All Science Journal Classification (ASJC) codes

Computer Science(all)
Computer Science Applications
Applied Mathematics

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10.1007/BF01840454

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title = "A modification of karmarkar's linear programming algorithm",

abstract = "We present a modification of Karmarkar's linear programming algorithm. Our algorithm uses a recentered projected gradient approach thereby obviating a priori knowledge of the optimal objective function value. Assuming primal and dual nondegeneracy, we prove that our algorithm converges. We present computational comparisons between our algorithm and the revised simplex method. For small, dense constraint matrices we saw little difference between the two methods.",

author = "Vanderbei, {Robert J.} and Meketon, {Marc S.} and Freedman, {Barry A.}",

year = "1986",

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AU - Meketon, Marc S.

AU - Freedman, Barry A.

PY - 1986/11/1

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N2 - We present a modification of Karmarkar's linear programming algorithm. Our algorithm uses a recentered projected gradient approach thereby obviating a priori knowledge of the optimal objective function value. Assuming primal and dual nondegeneracy, we prove that our algorithm converges. We present computational comparisons between our algorithm and the revised simplex method. For small, dense constraint matrices we saw little difference between the two methods.

AB - We present a modification of Karmarkar's linear programming algorithm. Our algorithm uses a recentered projected gradient approach thereby obviating a priori knowledge of the optimal objective function value. Assuming primal and dual nondegeneracy, we prove that our algorithm converges. We present computational comparisons between our algorithm and the revised simplex method. For small, dense constraint matrices we saw little difference between the two methods.

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A modification of karmarkar's linear programming algorithm

Abstract

All Science Journal Classification (ASJC) codes

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