Computing upper and lower bounds in interval decision trees

DOI10.1016/j.ejor.2006.06.030zbMath1131.91016OpenAlexW1986542341MaRDI QIDQ877064

Love Ekenberg, Mats Danielson

Publication date: 19 April 2007

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2006.06.030

zbMATH Keywords

bilinear programmingdecision analysisdecision treesmulti-linear programming

Mathematics Subject Classification ID

Decision theory (91B06) Integer programming (90C10)

Related Items (10)

Combining Second-Order Belief Distributions with Qualitative Statements in Decision Analysis ⋮ A flexible elicitation procedure for additive model scale constants ⋮ A prescriptive approach to elicitation of decision data ⋮ Implementing second-order decision analysis: concepts, algorithms, and tool ⋮ The Hurwicz Decision Rule’s Relationship to Decision Making with the Triangle and Beta Distributions and Exponential Utility ⋮ Sequential decision making with partially ordered preferences ⋮ Eliciting dual interval probabilities from interval comparison matrices ⋮ Generalized evaluation in decision analysis ⋮ Augmenting Ordinal Methods of Attribute Weight Approximation ⋮ Decision making with interval probabilities

Uses Software

• CG+

Cites Work

• Generalized evaluation in decision analysis
• A new polynomial-time algorithm for linear programming
• A methodology for uncertainty in knowledge-based systems
• A block active set algorithm for large-scale quadratic programming with box constraints
• A framework for analysing decisions under risk
• Continuity properties of the Karush-Kuhn-Tucker point set in quadratic programming problems
• A dual-active-set algorithm for positive semi-definite quadratic programming
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• Stable reduced Hessian updates for indefinite quadratic programming
• Basis- and partition identification for quadratic programming and linear complementarity problems
• The Simplex Method for Quadratic Programming
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• A New Algorithm for Solving Strictly Convex Quadratic Programs
• The Use ofQRFactorization in Sparse Quadratic Programming and Backward Error Issues
• The use of Previous Experience in Reaching Statistical Decisions
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