A Plant Location Guide for the Unsure: Approximation Algorithms for Min-Max Location Problems

Authors:
Barbara Anthony;Vineet Goyal;Anupam Gupta;Viswanath Nagarajan
Affiliations:
Department of Mathematics and Computer Science, Southwestern University, Georgetown, Texas 78626;Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;IBM T. J. Watson Research Center, Yorktown Heights, New York 10598
Venue:
Mathematics of Operations Research
Year:
2010

Citing 26
Cited 0

Fast approximation algorithms for fractional packing and covering problems

Mathematics of Operations Research
Randomized algorithms

Randomized algorithms
Combinatorial optimization

Combinatorial optimization
P-Complete Approximation Problems

Journal of the ACM (JACM)
Approximation algorithms for metric facility location and k-Median problems using the primal-dual schema and Lagrangian relaxation

Journal of the ACM (JACM)
Approximation algorithms

Approximation algorithms
Relations between average case complexity and approximation complexity

STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A constant-factor approximation algorithm for the k-median problem

Journal of Computer and System Sciences - STOC 1999
Fault-tolerant facility location

SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
On-line Algorithms in Machine Learning

Developments from a June 1996 seminar on Online algorithms: the state of the art
The Online Median Problem

SIAM Journal on Computing
Local Search Heuristics for k-Median and Facility Location Problems

SIAM Journal on Computing
On the costs and benefits of procrastination: approximation algorithms for stochastic combinatorial optimization problems

SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Boosted sampling: approximation algorithms for stochastic optimization

STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Improved Combinatorial Algorithms for Facility Location Problems

SIAM Journal on Computing
Approximating k-median with non-uniform capacities

SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
How to Pay, Come What May: Approximation Algorithms for Demand-Robust Covering Problems

FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
A general approach for incremental approximation and hierarchical clustering

SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Hedging Uncertainty: Approximation Algorithms for Stochastic Optimization Problems

Mathematical Programming: Series A and B
Prediction, Learning, and Games

Prediction, Learning, and Games
Dependent rounding and its applications to approximation algorithms

Journal of the ACM (JACM)
An approximation scheme for stochastic linear programming and its application to stochastic integer programs

Journal of the ACM (JACM)
Ruling Out PTAS for Graph Min-Bisection, Dense k-Subgraph, and Bipartite Clique

SIAM Journal on Computing
The reverse greedy algorithm for the metric k-median problem

Information Processing Letters
Pay today for a rainy day: improved approximation algorithms for demand-robust min-cut and shortest path problems

STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
Sampling bounds for stochastic optimization

APPROX'05/RANDOM'05 Proceedings of the 8th international workshop on Approximation, Randomization and Combinatorial Optimization Problems, and Proceedings of the 9th international conference on Randamization and Computation: algorithms and techniques

Quantified Score

Hi-index	0.00

Visualization

Abstract

In a combinatorial auction m heterogenous indivisible items are sold to n bidders. This paper considers settings in which the valuation functions of the bidders are known to be complement free (a.k.a. subadditive). We provide several approximation ...