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Robust solutions to uncertain linear programs
 OR Letters
, 1999
"... We consider linear programs with uncertain parameters, lying in some prescribed uncertainty set, where part of the variables must be determined before the realization of the uncertain parameters (”nonadjustable variables”), while the other part are variables that can be chosen after the realization ..."
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Cited by 358 (15 self)
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the realization (”adjustable variables”). We extend the Robust Optimization methodology ([1, 4, 5, 6, 7, 9, 13, 14]) to this situation by introducing the Adjustable Robust Counterpart (ARC) associated with an LP of the above structure. Often the ARC is significantly less conservative than the usual Robust
Robust Solutions To Uncertain Semidefinite Programs
 SIAM J. OPTIMIZATION
, 1998
"... In this paper we consider semidefinite programs (SDPs) whose data depend on some unknown but bounded perturbation parameters. We seek "robust" solutions to such programs, that is, solutions which minimize the (worstcase) objective while satisfying the constraints for every possible value ..."
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Cited by 109 (8 self)
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In this paper we consider semidefinite programs (SDPs) whose data depend on some unknown but bounded perturbation parameters. We seek "robust" solutions to such programs, that is, solutions which minimize the (worstcase) objective while satisfying the constraints for every possible value
Robust solutions for combinatorial auctions
 In Proceedings of the 6th ACM Conference on Electronic Commerce
, 2005
"... Bids submitted in auctions are usually treated as enforceable commitments in most bidding and auction theory literature. In reality bidders often withdraw winning bids before the transaction when it is in their best interests to do so. Given a bidwithdrawal in a combinatorial auction, finding an al ..."
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Cited by 21 (2 self)
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taker to preempt such uncertainty by having a solution that is robust to bidwithdrawal and provides a guarantee that possible withdrawals may be repaired easily with a bounded loss in revenue. Firstly, we use the Weighted Super Solutions framework [13], from the field of Constraint Programming, to solve
Robust Solutions To Uncertain Semidefinite Programs
, 1998
"... In this paper we consider semidenite programs (SDPs) whose data depends on some unknownbutbounded perturbation parameters. We seek "robust" solutions to such programs, that is, solutions which minimize the (worstcase) objective while satisfying the constraints for every possible values ..."
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Cited by 83 (3 self)
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In this paper we consider semidenite programs (SDPs) whose data depends on some unknownbutbounded perturbation parameters. We seek "robust" solutions to such programs, that is, solutions which minimize the (worstcase) objective while satisfying the constraints for every possible values
Tabu Searching for Robust Solutions
 PROCEEDINGS OF THE 4 TH METAHEURISTICS INTERNATIONAL CONFERENCE
, 2001
"... In this paper, we investigate how a basic tabu search technique can be adapted so that it finds solutions that (1) have a good solution quality and (2) are more robust than other solutions. We show that there is a need for robust solutions in many practical problems and discuss di#erent types of rob ..."
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Cited by 5 (1 self)
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In this paper, we investigate how a basic tabu search technique can be adapted so that it finds solutions that (1) have a good solution quality and (2) are more robust than other solutions. We show that there is a need for robust solutions in many practical problems and discuss di#erent types
Robust Solutions To LeastSquares Problems With Uncertain Data
, 1997
"... . We consider leastsquares problems where the coefficient matrices A; b are unknownbutbounded. We minimize the worstcase residual error using (convex) secondorder cone programming, yielding an algorithm with complexity similar to one singular value decomposition of A. The method can be interpret ..."
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Cited by 198 (14 self)
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be interpreted as a Tikhonov regularization procedure, with the advantage that it provides an exact bound on the robustness of solution, and a rigorous way to compute the regularization parameter. When the perturbation has a known (e.g., Toeplitz) structure, the same problem can be solved in polynomial
Stability and Robustness • Robust solutions
"... • Environmental monitoring: large scale data collection • Surveillance: alarm propagation, data storage and query Key Features • Large in quantity; deployed in bulk • Close proximity; often dutycycled • Locations often random rather than precisely controlled ..."
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• Environmental monitoring: large scale data collection • Surveillance: alarm propagation, data storage and query Key Features • Large in quantity; deployed in bulk • Close proximity; often dutycycled • Locations often random rather than precisely controlled
Robust solutions of Linear Programming problems contaminated with uncertain data
 Mathematical Programming
, 2000
"... Optimal solutions of Linear Programming problems may become severely infeasible if the nominal data is slightly perturbed. We demonstrate this phenomenon by studying 90 LPs from the wellknown NETLIB collection. We then apply the Robust Optimization methodology (BenTal and Nemirovski [13]; El Ghao ..."
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Cited by 175 (7 self)
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Optimal solutions of Linear Programming problems may become severely infeasible if the nominal data is slightly perturbed. We demonstrate this phenomenon by studying 90 LPs from the wellknown NETLIB collection. We then apply the Robust Optimization methodology (BenTal and Nemirovski [13]; El
Robust solutions to Job Shop problems
, 1999
"... The problem of finding robust solutions for scheduling problems is of utmost importance for realworld applications as they operate in dynamic environments. In such environments it is often necessary to reschedule the existing plan due to various failures (e.g., machine breakdowns, sickness of employ ..."
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Cited by 7 (0 self)
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The problem of finding robust solutions for scheduling problems is of utmost importance for realworld applications as they operate in dynamic environments. In such environments it is often necessary to reschedule the existing plan due to various failures (e.g., machine breakdowns, sickness
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
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Cited by 826 (88 self)
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), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm
Results 1  10
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