Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10107-005-0677-1
Title: Tractable approximations to robust conic optimization problems
Authors: Bertsimas, D.
Sim, M. 
Keywords: Conic Optimization
Robust Optimization
Stochastic Optimization
Issue Date: 2006
Source: Bertsimas, D., Sim, M. (2006). Tractable approximations to robust conic optimization problems. Mathematical Programming 107 (1-2) : 5-36. ScholarBank@NUS Repository. https://doi.org/10.1007/s10107-005-0677-1
Abstract: In earlier proposals, the robust counterpart of conic optimization problems exhibits a lateral increase in complexity, i.e., robust linear programming problems (LPs) become second order cone problems (SOCPs), robust SOCPs become semidefinite programming problems (SDPs), and robust SDPs become NP-hard. We propose a relaxed robust counterpart for general conic optimization problems that (a) preserves the computational tractability of the nominal problem; specifically the robust conic optimization problem retains its original structure, i.e., robust LPs remain LPs, robust SOCPs remain SOCPs and robust SDPs remain SDPs, and (b) allows us to provide a guarantee on the probability that the robust solution is feasible when the uncertain coefficients obey independent and identically distributed normal distributions.
Source Title: Mathematical Programming
URI: http://scholarbank.nus.edu.sg/handle/10635/44007
ISSN: 00255610
DOI: 10.1007/s10107-005-0677-1
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