Approximation via cost sharing: Simpler and better approximation algorithms for network design
Journal of the ACM (JACM)
Commitment under uncertainty: Two-stage stochastic matching problems
Theoretical Computer Science
Stochastic Combinatorial Optimization with Controllable Risk Aversion Level
Mathematics of Operations Research
A Plant Location Guide for the Unsure: Approximation Algorithms for Min-Max Location Problems
Mathematics of Operations Research
New results on minimax regret single facility ordered median location problems on networks
ESA'07 Proceedings of the 15th annual European conference on Algorithms
A generic algorithm for approximately solving stochastic graph optimization problems
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
On the Power of Robust Solutions in Two-Stage Stochastic and Adaptive Optimization Problems
Mathematics of Operations Research
A primal-dual approximation algorithm for the k-level stochastic facility location problem
AAIM'10 Proceedings of the 6th international conference on Algorithmic aspects in information and management
On the approximability of robust spanning tree problems
Theoretical Computer Science
Strict Cost Sharing Schemes for Steiner Forest
SIAM Journal on Computing
Sampling and Cost-Sharing: Approximation Algorithms for Stochastic Optimization Problems
SIAM Journal on Computing
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Single-source stochastic routing
APPROX'06/RANDOM'06 Proceedings of the 9th international conference on Approximation Algorithms for Combinatorial Optimization Problems, and 10th international conference on Randomization and Computation
An approximation algorithm for the k-level stochastic facility location problem
Operations Research Letters
Stochastic vehicle routing with recourse
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
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We study two-stage, finite-scenario stochastic versions of several combinatorial optimization problems, and provide nearly tight approximation algorithms for them. Our problems range from the graph-theoretic (shortest path, vertex cover, facility location) to set-theoretic (set cover, bin packing), and contain representatives with different approximation ratios.The approximation ratio of the stochastic variant of a typical problem is found to be of the same order of magnitude as its deterministic counterpart. Furthermore, we show that common techniques for designing approximation algorithms such as LP rounding, the primal-dual method, and the greedy algorithm, can be adapted to obtain these results.