Stochastic method for the solution of unconstrained vector optimization problems

DOI10.1023/A:1015472306888zbMath1022.90027OpenAlexW1572469720MaRDI QIDQ700771

D. Kharzeev

Publication date: 8 October 2002

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1015472306888

zbMATH Keywords

vector optimization problemsBrownian motionstochastic differential equationscurves of dominated points

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Numerical solutions to stochastic differential and integral equations (65C30)

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Cites Work

• Unnamed Item
• Unnamed Item
• Multi-Attribute Utility Models: A Review of Field and Field-Like Studies
• Exceptional Paper—Lexicographic Orders, Utilities and Decision Rules: A Survey
• Unconstrained global optimization using stochastic intergral equations
• Utility Functions for Multiattributed Consequences
• A Class of Solutions for Group Decision Problems

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