 Optimization under uncertainty and risk: Quadratic and copositive approachesImmanuel M. Bomze and Markus Gabl European Journal of Operational Research, 2023, vol. 310, issue 2, 449-476 Abstract: Robust optimization and stochastic optimization are the two main paradigms for dealing with the uncertainty inherent in almost all real-world optimization problems. The core principle of robust optimization is the introduction of parameterized families of constraints. Sometimes, these complicated semi-infinite constraints can be reduced to finitely many convex constraints, so that the resulting optimization problem can be solved using standard procedures. Hence flexibility of robust optimization is limited by certain convexity requirements on various objects. However, a recent strain of literature has sought to expand applicability of robust optimization by lifting variables to a properly chosen matrix space. Doing so allows to handle situations where convexity requirements are not met immediately, but rather intermediately. Keywords: Conic programming and interior point methods; Quadratically constrained quadratic problems; Two-stage stochastic standard quadratic problems; Adjustable robust optimization; Distributionally robust optimization (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:310:y:2023:i:2:p:449-476 DOI: 10.1016/j.ejor.2022.11.020 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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