Optimizing over coherent risk measures and non-convexities: a robust mixed integer optimization approach

DOI10.1007/s10589-015-9755-3zbMath1357.90174OpenAlexW2090738555MaRDI QIDQ902084

Akiko Takeda, Dimitris J. Bertsimas

Publication date: 7 January 2016

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

Full work available at URL: http://hdl.handle.net/1721.1/103345

zbMATH Keywords

portfolio optimizationrobust optimizationnonconvexitybinary classificationcoherent risk measure minimization

Mathematics Subject Classification ID

Mixed integer programming (90C11) Minimax problems in mathematical programming (90C47) Portfolio theory (91G10)

Related Items (1)

Convergence of a Scholtes-type regularization method for cardinality-constrained optimization problems with an application in sparse robust portfolio optimization

Uses Software

• UCI-ml
• PRMLT

Cites Work

• Unnamed Item
• On the role of norm constraints in portfolio selection
• On generalization performance and non-convex optimization of extended \(\nu \)-support vector machine
• Support-vector networks
• Interaction between financial risk measures and machine learning methods
• Coherent Measures of Risk
• Sparse and stable Markowitz portfolios
• Constructing Risk Measures from Uncertainty Sets
• Constructing Uncertainty Sets for Robust Linear Optimization
• A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms
• A Unified Classification Model Based on Robust Optimization
• Optimization of Convex Risk Functions

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