A General Approximation Technique for Constrained Forest Problems
SIAM Journal on Computing
Approximation in stochastic scheduling: the power of LP-based priority policies
Journal of the ACM (JACM)
Allocating Bandwidth for Bursty Connections
SIAM Journal on Computing
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Computational Optimization and Applications
Improved Approximation Algorithms for Metric Facility Location Problems
APPROX '02 Proceedings of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization
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
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Stochastic Optimization is (Almost) as easy as Deterministic Optimization
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Approximation algorithms for clustering problems
Approximation algorithms for clustering problems
Facility location and the analysis of algorithms through factor-revealing programs
Facility location and the analysis of algorithms through factor-revealing programs
Confidence level solutions for stochastic programming
Automatica (Journal of IFAC)
Introduction to Stochastic Programming
Introduction to Stochastic Programming
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
Stochastic packing-market planning
Proceedings of the 8th ACM conference on Electronic commerce
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Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inability to know the future. In this survey, we outline some recent progress in the design of polynomial-time algorithms with performance guarantees on the quality of the solutions found for an important class of stochastic programming problems — 2-stage problems with recourse. In particular, we show that for a number of concrete problems, algorithmic approaches that have been applied for their deterministic analogues are also effective in this more challenging domain. More specifically, this work highlights the role of tools from linear programming, rounding techniques, primal-dual algorithms, and the role of randomization more generally.