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Non-Linear Programming Solvers for Decision Analysis
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media. (FSCN - Fibre Science and Communication Network)
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.ORCID iD: 0000-0002-0665-1889
2004 (English)In: Operations Research Proceedings 2003: Selected Papers of the International Conference on Operations Research (OR 2003) Heidelberg, September 3-5, 2003, Springer , 2004, 475- p.Conference paper, Published paper (Other academic)
Abstract [en]

Several methods have been developed over a number of years for solving decision problems when vague and numerically imprecise information prevails. However, the DELTA method and similar methods give rise to particular bilinear programming problems that are time consuming to solve in a real-time environment. This paper presents a set of benchmark tests for non-linear programming solvers for solving this special type of problems. With two existing linear programming based algorithms, it also investigates the performance of linear programming solvers for special decision situations in decision support systems.

Place, publisher, year, edition, pages
Springer , 2004. 475- p.
Keyword [en]
decision analysis, nonlinear programming, linear programming, quadratic programming
National Category
Computer Science
Identifiers
URN: urn:nbn:se:miun:diva-1708Local ID: 355ISBN: 3-540-21445-3 (print)OAI: oai:DiVA.org:miun-1708DiVA: diva2:26740
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2009-07-10Bibliographically approved
In thesis
1. Bilinear optimization in computational decision analysis
Open this publication in new window or tab >>Bilinear optimization in computational decision analysis
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In real-life decision analysis, significant recognition has been given to theunrealistic expectation of numerically precise information. Many modernapproaches attempting to handle imprecision have focused more on representationand less on evaluation. The DELTA method, as one of the fewleading approaches, challenges this issue by its evaluation framework thatcan accommodate both precision and imprecision. However, computationally,DELTA and similar approaches may incur time-consuming calculationsdue to the introduction of imprecise information concerning probability andutility. In general, those problems are bounded non-convex bilinear optimizationprograms with disjoint linear constraints, which cannot be solvedeffectively by any existing general-purpose global optimization technique.This thesis presents two enhanced cutting plane algorithms for solvingbounded disjoint bilinear programs arising in computational decision analysis.Each algorithm consists of a local phase designed to determine a localoptimizer from an approximate solution, and a global phase designed to systematicallyexplore the feasible region, subset by subset. These two phasesare switched automatically during the global search procedure. The basicframework builds upon previously developed efficient cutting plane methods.By embedding the lower bounding technique in a branch and bound procedure,the improvement of their performances seems encouraging in the lightof computational experience. Even though the motivation to develop thesealgorithms stems from computational decision analysis, the idea can also beextended to the development of optimization approaches for handling generalbounded disjoint bilinear programs, especially for larger sized ones.

Place, publisher, year, edition, pages
Sundsvall: Mittuniversitetet, 2005
Series
Mid Sweden University doctoral thesis, ISSN 1652-893X ; 2
National Category
Information Science
Identifiers
urn:nbn:se:miun:diva-8856 (URN)
Public defence
2005-05-27, O102, Åkroken, Sundsvall, 13:15 (English)
Opponent
Supervisors
Available from: 2009-05-06 Created: 2009-05-06 Last updated: 2009-07-13Bibliographically approved

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Citation style
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