Probabilistic construction of deterministic algorithms: approximating packing integer programs
Journal of Computer and System Sciences - 27th IEEE Conference on Foundations of Computer Science October 27-29, 1986
Improved approximations of packing and covering problems
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Strengthening integrality gaps for capacitated network design and covering problems
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Tight Approximation Results for General Covering Integer Programs
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
On Multidimensional Packing Problems
SIAM Journal on Computing
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
Adaptivity and approximation for stochastic packing problems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Sampling-based Approximation Algorithms for Multi-stage Stochastic
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
What about wednesday? approximation algorithms for multistage 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
Near-optimal algorithms for shared filter evaluation in data stream systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Stochastic Submodular Maximization
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Approximation algorithms for optimal decision trees and adaptive TSP problems
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Theory and Applications of Robust Optimization
SIAM Review
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
Adaptive submodularity: theory and applications in active learning and stochastic optimization
Journal of Artificial Intelligence Research
Minimum latency submodular cover
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Active planning for underwater inspection and the benefit of adaptivity
International Journal of Robotics Research
Note: Adaptivity in the stochastic blackjack knapsack problem
Theoretical Computer Science
Hi-index | 0.00 |
We introduce a class of “stochastic covering” problems where the target set X to be covered is fixed, while the “items” used in the covering are characterized by probability distributions over subsets of X. This is a natural counterpart to the stochastic packing problems introduced in [5]. In analogy to [5], we study both adaptive and non-adaptive strategies to find a feasible solution, and in particular the adaptivity gap, introduced in [4]. It turns out that in contrast to deterministic covering problems, it makes a substantial difference whether items can be used repeatedly or not. In the model of Stochastic Covering with item multiplicity, we show that the worst case adaptivity gap is Θ(log d), where d is the size of the target set to be covered, and this is also the best approximation factor we can achieve algorithmically. In the model without item multiplicity, we show that the adaptivity gap for Stochastic Set Cover can be Ω(d). On the other hand, we show that the adaptivity gap is bounded by O(d2), by exhibiting an O(d2)-approximation non-adaptive algorithm.