A Case Study of De-randomization Methods for Combinatorial Approximation Algorithms

José D.P. Rolim and Luca Trevisan
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José D.P. Rolim: University of Geneva
Luca Trevisan: MIT Laboratory for Computer Science, Room NE43-371

Journal of Combinatorial Optimization, 1998, vol. 2, issue 3, No 2, 219-236

Abstract: Abstract We study three different de-randomization methods that are often applied to approximate combinatorial optimization problems. We analyze the conditional probabilities method in connection with randomized rounding for routing, packing and covering integer linear programming problems. We show extensions of such methods for non-independent randomized rounding for the assignment problem. The second method, the so-called random walks is exemplified with algorithms for dense instances of some NP problems. Another often used method is the bounded independence technique; we explicit this method for the sparsest cut and maximum concurrent flow problems.

Keywords: Conditional Probability; Approximation Algorithm; Programming Problem; Assignment Problem; Integer Linear Programming (search for similar items in EconPapers)
Date: 1998
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DOI: 10.1023/A:1009793909670

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