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
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

161
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

137
checked on Nov 19, 2017

Page view(s)

64
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.