Results 1  10
of
194
for Robust and MaxMin Optimization
, 912
"... The general problem of robust optimization is this: one of several possible scenarios will appear tomorrow, but things are more expensive tomorrow than they are today. What should you anticipatorily buy today, so that the worstcase cost (summed over both days) is minimized? For example, in a set co ..."
Abstract
 Add to MetaCart
The general problem of robust optimization is this: one of several possible scenarios will appear tomorrow, but things are more expensive tomorrow than they are today. What should you anticipatorily buy today, so that the worstcase cost (summed over both days) is minimized? For example, in a set
Thresholded Covering Algorithms for Robust and MaxMin Optimization
, 2009
"... The general problem of robust optimization is this: one of several possible scenarios will appear tomorrow, but things are more expensive tomorrow than they are today. What should you anticipatorily buy today, so that the worstcase cost (summed over both days) is minimized? For example, in a set co ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
The general problem of robust optimization is this: one of several possible scenarios will appear tomorrow, but things are more expensive tomorrow than they are today. What should you anticipatorily buy today, so that the worstcase cost (summed over both days) is minimized? For example, in a set
A Cooperative . . . for the MultipleScenario MaxMin Knapsack Problem
, 2009
"... The purpose of this article is to present a novel method to approximately solve the MultipleScenario MaxMin Knapsack Problem (MSM2KP). This problem models many real world situations, e.g. when for many scenarios noted pi ∈ P = {1,..., P}, the aim is to identify the one offering a better alternativ ..."
Abstract
 Add to MetaCart
The purpose of this article is to present a novel method to approximately solve the MultipleScenario MaxMin Knapsack Problem (MSM2KP). This problem models many real world situations, e.g. when for many scenarios noted pi ∈ P = {1,..., P}, the aim is to identify the one offering a better
Efficient Algorithms for Robustness in Matroid Optimization
 PROCEEDINGS OF THE EIGHTH ANNUAL ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS (NEW
, 1996
"... The robustness function of a matroid measures the maximum increase in the weight of its minimum weight bases that can be produced by increases of a given total cost on the weights of its elements. We present an algorithm for computing this function, that runs in strongly polynomial time for matroids ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
The robustness function of a matroid measures the maximum increase in the weight of its minimum weight bases that can be produced by increases of a given total cost on the weights of its elements. We present an algorithm for computing this function, that runs in strongly polynomial time
Robust discrete optimization under ellipsoidal uncertainty sets
, 2004
"... We address the complexity and practically e cient methods for robust discrete optimization under ellipsoidal uncertainty sets. Speci cally, weshowthat the robust counterpart of a discrete optimization problem with correlated objective function data is NPhard even though the nominal problem is polyn ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
We address the complexity and practically e cient methods for robust discrete optimization under ellipsoidal uncertainty sets. Speci cally, weshowthat the robust counterpart of a discrete optimization problem with correlated objective function data is NPhard even though the nominal problem
Combinatorial auctions: A survey
, 2000
"... Many auctions involve the sale of a variety of distinct assets. Examples are airport time slots, delivery routes and furniture. Because of complementarities (or substitution effects) between the different assets, bidders have preferences not just for particular items but for sets or bundles of items ..."
Abstract

Cited by 212 (1 self)
 Add to MetaCart
Many auctions involve the sale of a variety of distinct assets. Examples are airport time slots, delivery routes and furniture. Because of complementarities (or substitution effects) between the different assets, bidders have preferences not just for particular items but for sets or bundles
MINIMIZATION ON STOCHASTIC MATROIDS
, 1990
"... NPS559014 Approved for public release; distribution is unlimited. Prepared for: T̂aval Postgraduate School, ..."
Abstract
 Add to MetaCart
NPS559014 Approved for public release; distribution is unlimited. Prepared for: T̂aval Postgraduate School,
Linear Programming: Foundations and Extensions
, 1996
"... under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a data base or retrieval system, without the prior written permission of the publisher. ISBN 0000000000 The text for this book was formated in Time ..."
Abstract

Cited by 196 (0 self)
 Add to MetaCart
under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a data base or retrieval system, without the prior written permission of the publisher. ISBN 0000000000 The text for this book was formated
Results 1  10
of
194