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Derandomization through approximation : an NC algorithm for minimum cuts

Author: David Karger; Rajeev Motwani; Stanford University. Computer Science Department.
Publisher: Stanford, Calif. : Stanford University, Dept. of Computer Science, [1993]
Series: Report (Stanford University. Computer Science Department), no. STAN- CS-93-1471.
Edition/Format:   Book : EnglishView all editions and formats
Database:WorldCat
Summary:
Abstract: "We prove that the minimum cut problem can be solved in NC on arbitrarily weighted undirected graphs. We do so by giving three separate and independently interesting results. The first is a relatively straightforward NC (2 + [epsilon])-approximation algorithm for the minimum cut. It uses O(m²/n) processors. The second result is a randomized reduction of the minimum cut problem to the minimum cut  Read more...
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Document Type: Book
All Authors / Contributors: David Karger; Rajeev Motwani; Stanford University. Computer Science Department.
OCLC Number: 30498431
Notes: "May 4, 1993."
Description: 18 pages ; 28 cm.
Series Title: Report (Stanford University. Computer Science Department), no. STAN- CS-93-1471.
Responsibility: David R. Karger, Rajeev Motwani.

Abstract:

Abstract: "We prove that the minimum cut problem can be solved in NC on arbitrarily weighted undirected graphs. We do so by giving three separate and independently interesting results. The first is a relatively straightforward NC (2 + [epsilon])-approximation algorithm for the minimum cut. It uses O(m²/n) processors. The second result is a randomized reduction of the minimum cut problem to the minimum cut approximation problem. The third is a derandomization of this reduction. Performing the derandomization requires a novel combination of two previously known derandomization techniques: pairwise independence and random walks on expanders."

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