Two linear programming algorithms for the linear discrete $L_{1}$ norm problem

Authors:
Ronald D. Armstrong and James P. Godfrey

Journal:
Math. Comp. **33** (1979), 289-300

MSC:
Primary 90C05

DOI:
https://doi.org/10.1090/S0025-5718-1979-0525398-2

MathSciNet review:
0525398

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Abstract | References | Similar Articles | Additional Information

Abstract: Computational studies by several authors have indicated that linear programming is currently the most efficient procedure for obtaining ${L_1}$, norm estimates for a discrete linear problem. However, there are several linear programming algorithms, and the "best" approach may depend on the problemβs structure (e.g., sparsity, triangularity, stability). In this paper we shall compare two published simplex algorithms, one referred to as primal and the other referred to as dual, and show that they are conceptually equivalent.

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*Naval Res. Logist. Quart*. (To appear.)

*Comm. Statist.*, v. B6(4), 1977.

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Article copyright:
© Copyright 1979
American Mathematical Society