TY - JOUR

T1 - On the rate of convergence of deterministic and randomized RAS matrix scaling algorithms

AU - Kalantari, Bahman

AU - Khachiyan, Leonid

N1 - Funding Information:
Correspondence to: Prof. B. Kalantari, Department of Computer Science, Rutgers University, New Brunswick, NJ 08903, USA. * This research was supported in part by the National Science Foundation under Grant No. CCR-9208371.

PY - 1993/12

Y1 - 1993/12

N2 - We consider the well-known RAS algorithm for the problem of positive matrix scaling. We give a new bound on the number of iterations of the method for scaling a given d-dimensional matrix A to a prescribed accuracy. Although the RAS method is not a polynomial-time algorithm even for d=2, our bound implies that for any dimension d the method is a fully polynomial-time approximation scheme. We also present a randomized variant of the algorithm whose (expected) running time improves that of the deterministic method by a factor of d.

AB - We consider the well-known RAS algorithm for the problem of positive matrix scaling. We give a new bound on the number of iterations of the method for scaling a given d-dimensional matrix A to a prescribed accuracy. Although the RAS method is not a polynomial-time algorithm even for d=2, our bound implies that for any dimension d the method is a fully polynomial-time approximation scheme. We also present a randomized variant of the algorithm whose (expected) running time improves that of the deterministic method by a factor of d.

KW - RAS method

KW - diagonal matrix scaling

KW - fully polynomial-time approximation schemes

KW - randomized algorithms

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U2 - 10.1016/0167-6377(93)90087-W

DO - 10.1016/0167-6377(93)90087-W

M3 - Article

AN - SCOPUS:38249001115

SN - 0167-6377

VL - 14

SP - 237

EP - 244

JO - Operations Research Letters

JF - Operations Research Letters

IS - 5

ER -