Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
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
Randomized algorithms
Improved Approximation Guarantees for Packing and Covering Integer Programs
SIAM Journal on Computing
On Multidimensional Packing Problems
SIAM Journal on Computing
Mathematical Programming: Series A and B
An Extension of the Lova´sz Local Lemma, and its Applications to Integer Programming
SIAM Journal on Computing
Theoretical Computer Science
Improved approximation algorithms for the Min-Max Selecting Items problem
Information Processing Letters
Hi-index | 0.89 |
In this paper the problem of selecting p items out of n available to minimize the total cost is discussed. This problem is a special case of many important combinatorial optimization problems such as 0-1 knapsack, minimum assignment, single machine scheduling, minimum matroid base or resource allocation. It is assumed that the item costs are uncertain and they are specified as a scenario set containing K distinct cost scenarios. In order to choose a solution the min-max and min-max regret criteria are applied. It is shown that both min-max and min-max regret problems are not approximable within any constant factor unless P=NP, which strengthens the results known up to date. In this paper a deterministic approximation algorithm with performance ratio of O(lnK) for the min-max version of the problem is also proposed.