SIAM Journal on Discrete Mathematics
Algorithmic theory of random graphs
Random Structures & Algorithms - Special issue: average-case analysis of algorithms
Independent sets with domination constraints
Proceedings of the 5th Twente workshop on on Graphs and combinatorial optimization
Computationally feasible VCG mechanisms
Proceedings of the 2nd ACM conference on Electronic commerce
Greedy local improvement and weighted set packing approximation
Journal of Algorithms
Truth revelation in approximately efficient combinatorial auctions
Journal of the ACM (JACM)
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Management Science
Adaptivity and approximation for stochastic packing problems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Combinatorial Auctions
Truthful and Near-Optimal Mechanism Design via Linear Programming
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
On maximizing welfare when utility functions are subadditive
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Truthful randomized mechanisms for combinatorial auctions
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
On the complexity of approximating k-set packing
Computational Complexity
Approximation algorithms for 2-stage stochastic optimization problems
FSTTCS'06 Proceedings of the 26th international conference on Foundations of Software Technology and Theoretical Computer Science
More expressive market models and the future of combinatorial auctions
ACM SIGecom Exchanges
Truthful and Near-Optimal Mechanism Design via Linear Programming
Journal of the ACM (JACM)
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Motivated by the problem of centralized market clearing in a market with probabilistic supply and demand, we introduce the Stochastic Packing-Market Planning problem (SPMP), which is a stochastic generalization of the Maximum k-Set Packing problem. We provide an O(k) approximation algorithm for SPMP, as well as a O(k) approximation mechanism that is truthful in expectation. This matches up to constants the best approximation ratio known for Maximum k-Set Packing. Along the way, we develop techniques for obtaining sparse subhypergraphs of an input hypergraph that preserves Ep(•) up to an O(k) factor, where Ep(G) measures the expected weight of a maximum weight set packing in a random subhypergraph of G. We also give a linear programming based approximation for Ep(G). These techniques may be of independent interest.