Feasible direction methods for stochastic programming problems

Andrzej Ruszczyński

doi:10.1007/BF01581643

Feasible direction methods for stochastic programming problems

Andrzej Ruszczyński

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

36 Scopus citations

Abstract

A unified approach to stochastic feasible direction methods is developed. An abstract point-to-set map description of the algorithm is used and a general convergence theorem is proved. The theory is used to develop stochastic analogs of classical feasible direction algorithms.

Original language	English (US)
Pages (from-to)	220-229
Number of pages	10
Journal	Mathematical Programming
Volume	19
Issue number	1
DOIs	https://doi.org/10.1007/BF01581643
State	Published - Dec 1980
Externally published	Yes

All Science Journal Classification (ASJC) codes

Software
Mathematics(all)

Keywords

Convergence
Feasible Direction Methods
Point-to-Set Maps
Stochastic Programming

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10.1007/BF01581643

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title = "Feasible direction methods for stochastic programming problems",

abstract = "A unified approach to stochastic feasible direction methods is developed. An abstract point-to-set map description of the algorithm is used and a general convergence theorem is proved. The theory is used to develop stochastic analogs of classical feasible direction algorithms.",

keywords = "Convergence, Feasible Direction Methods, Point-to-Set Maps, Stochastic Programming",

author = "Andrzej Ruszczy{\'n}ski",

year = "1980",

month = dec,

doi = "10.1007/BF01581643",

language = "English (US)",

volume = "19",

pages = "220--229",

journal = "Mathematical Programming",

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T1 - Feasible direction methods for stochastic programming problems

AU - Ruszczyński, Andrzej

PY - 1980/12

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N2 - A unified approach to stochastic feasible direction methods is developed. An abstract point-to-set map description of the algorithm is used and a general convergence theorem is proved. The theory is used to develop stochastic analogs of classical feasible direction algorithms.

AB - A unified approach to stochastic feasible direction methods is developed. An abstract point-to-set map description of the algorithm is used and a general convergence theorem is proved. The theory is used to develop stochastic analogs of classical feasible direction algorithms.

KW - Convergence

KW - Feasible Direction Methods

KW - Point-to-Set Maps

KW - Stochastic Programming

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JO - Mathematical Programming

JF - Mathematical Programming

SN - 0025-5610

IS - 1

ER -

Feasible direction methods for stochastic programming problems

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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