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Title: Global methods for optimizing over the efficient set

Abstract

Let X{sub E} denote the efficient set of the multiple objective linear programming problem VMAX Cx subject to x {element_of} X, where C is a p {times} n matrix and X is a polytope in R{sup n}. We consider the problem max {l_brace}dx : x {element_of} X{sub E}{r_brace} which has many applications in multiple decision making. Since X{sub E} is generally nonconvex, this problem is classified as a global optimization problem. It has been shown that there exists a simplex {Lambda} in R{sup p} such that X{sub E} = {l_brace} {element_of} X : g{lambda} - {lambda}Cx {<=} 0, {lambda} {element_of} {Lambda}{r_brace} where g{lambda} = sup {l_brace}Cy : y {element_of} X{r_brace}. We seek a global {epsilon}-optimal solution {bar x} such that dx {<=} d{bar x} + {epsilon}, {forall}x {element_of} X{sub E}. In our approach, the main problem is to check, for a given {alpha}, whether the set X{sub E}{sup {alpha}} = {l_brace}x {element_of} X{sub E} : dx {>=} {alpha}{r_brace} = {l_brace}x {element_of} : g{lambda} - {lambda}Cx {<=} 0, {lambda} {element_of} {Lambda}, dx {>=} {alpha}{r_brace} is empty or not. We show that this problem can be reduced to the problem of finding a point of the set D/C where D, C aremore » convex sets in R{sup p} {times} R. Based on this fact we propose different algorithms for obtaining a global {epsilon}-optimal solution, namely (1) an Outer Approximation algorithm; (2) a Bisection Search algorithm; (3) a Branch and Bound algorithm. All these algorithms are implementable and require solving only linear programs. Moreover, they are efficient when the number of criteria p is small relative to n.« less

Authors:
Publication Date:
OSTI Identifier:
36638
Report Number(s):
CONF-9408161-
TRN: 94:009753-0727
Resource Type:
Conference
Resource Relation:
Conference: 15. international symposium on mathematical programming, Ann Arbor, MI (United States), 15-19 Aug 1994; Other Information: PBD: 1994; Related Information: Is Part Of Mathematical programming: State of the art 1994; Birge, J.R.; Murty, K.G. [eds.]; PB: 312 p.
Country of Publication:
United States
Language:
English
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; MATRICES; NUMERICAL SOLUTION; EFFICIENCY; ALGORITHMS

Citation Formats

Phong, Thai Quynh. Global methods for optimizing over the efficient set. United States: N. p., 1994. Web.
Phong, Thai Quynh. Global methods for optimizing over the efficient set. United States.
Phong, Thai Quynh. 1994. "Global methods for optimizing over the efficient set". United States.
@article{osti_36638,
title = {Global methods for optimizing over the efficient set},
author = {Phong, Thai Quynh},
abstractNote = {Let X{sub E} denote the efficient set of the multiple objective linear programming problem VMAX Cx subject to x {element_of} X, where C is a p {times} n matrix and X is a polytope in R{sup n}. We consider the problem max {l_brace}dx : x {element_of} X{sub E}{r_brace} which has many applications in multiple decision making. Since X{sub E} is generally nonconvex, this problem is classified as a global optimization problem. It has been shown that there exists a simplex {Lambda} in R{sup p} such that X{sub E} = {l_brace} {element_of} X : g{lambda} - {lambda}Cx {<=} 0, {lambda} {element_of} {Lambda}{r_brace} where g{lambda} = sup {l_brace}Cy : y {element_of} X{r_brace}. We seek a global {epsilon}-optimal solution {bar x} such that dx {<=} d{bar x} + {epsilon}, {forall}x {element_of} X{sub E}. In our approach, the main problem is to check, for a given {alpha}, whether the set X{sub E}{sup {alpha}} = {l_brace}x {element_of} X{sub E} : dx {>=} {alpha}{r_brace} = {l_brace}x {element_of} : g{lambda} - {lambda}Cx {<=} 0, {lambda} {element_of} {Lambda}, dx {>=} {alpha}{r_brace} is empty or not. We show that this problem can be reduced to the problem of finding a point of the set D/C where D, C are convex sets in R{sup p} {times} R. Based on this fact we propose different algorithms for obtaining a global {epsilon}-optimal solution, namely (1) an Outer Approximation algorithm; (2) a Bisection Search algorithm; (3) a Branch and Bound algorithm. All these algorithms are implementable and require solving only linear programs. Moreover, they are efficient when the number of criteria p is small relative to n.},
doi = {},
url = {https://www.osti.gov/biblio/36638}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Dec 31 00:00:00 EST 1994},
month = {Sat Dec 31 00:00:00 EST 1994}
}

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