Derandomization through approximation: an NC algorithm for minimum cuts

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 u...

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Bibliographische Detailangaben
Hauptverfasser: Karger, David (VerfasserIn), Motwani, Rajeev 1962-2009 (VerfasserIn)
Format: Buch
Sprache:English
Veröffentlicht: Stanford, Calif. 1993
Schriftenreihe:Stanford University / Computer Science Department: Report STAN CS 1471
Schlagworte:
Zusammenfassung: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."
Beschreibung:18 S.

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