A computational evaluation of two subgradient search methods

Sanjiv Sarin; Mark H. Karwan

doi:10.1016/0305-0548(87)90027-X

A computational evaluation of two subgradient search methods

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Abstract

The success of Lagrangean relaxation in discrete optimization is well documented. Part of this success is due to efficient procedures to find (near) optimal Lagrange multipliers, often by the use of subgradient search. This paper presents a comparison of the modified subgradient search technique of Camerini et al. and the most widely employed subgradient search procedure. Empirical results in different discrete optimization problems are given and discussed. Cases in which the modified subgradient search procedure is significantly superior and inferior are represented. Both procedures are easily implemented and thus should both be tested in special applications.

Original language	English
Pages (from-to)	241-247
Number of pages	7
Journal	Computers and Operations Research
Volume	14
Issue number	3
DOIs	https://doi.org/10.1016/0305-0548(87)90027-X
State	Published - 1987
Externally published	Yes

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title = "A computational evaluation of two subgradient search methods",

abstract = "The success of Lagrangean relaxation in discrete optimization is well documented. Part of this success is due to efficient procedures to find (near) optimal Lagrange multipliers, often by the use of subgradient search. This paper presents a comparison of the modified subgradient search technique of Camerini et al. and the most widely employed subgradient search procedure. Empirical results in different discrete optimization problems are given and discussed. Cases in which the modified subgradient search procedure is significantly superior and inferior are represented. Both procedures are easily implemented and thus should both be tested in special applications.",

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AU - Sarin, Sanjiv

AU - Karwan, Mark H.

PY - 1987

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N2 - The success of Lagrangean relaxation in discrete optimization is well documented. Part of this success is due to efficient procedures to find (near) optimal Lagrange multipliers, often by the use of subgradient search. This paper presents a comparison of the modified subgradient search technique of Camerini et al. and the most widely employed subgradient search procedure. Empirical results in different discrete optimization problems are given and discussed. Cases in which the modified subgradient search procedure is significantly superior and inferior are represented. Both procedures are easily implemented and thus should both be tested in special applications.

AB - The success of Lagrangean relaxation in discrete optimization is well documented. Part of this success is due to efficient procedures to find (near) optimal Lagrange multipliers, often by the use of subgradient search. This paper presents a comparison of the modified subgradient search technique of Camerini et al. and the most widely employed subgradient search procedure. Empirical results in different discrete optimization problems are given and discussed. Cases in which the modified subgradient search procedure is significantly superior and inferior are represented. Both procedures are easily implemented and thus should both be tested in special applications.

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U2 - 10.1016/0305-0548(87)90027-X

DO - 10.1016/0305-0548(87)90027-X

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SP - 241

EP - 247

JO - Computers and Operations Research

JF - Computers and Operations Research

IS - 3

ER -

A computational evaluation of two subgradient search methods

Abstract

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