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Saddle Points in Random Matrices: Analysis of Knuth Search Algorithms

M. Hofri1 and P. Jacquet2

(1)  Department of Computer Science, WPI, Worcester, MA 01609-2280, USA. hofri@cs.wpi.edu., US
(2)  INRIA, Domaine de Voluceau - Rocquencourt, B.P. 105, 78153 Le Chesnay Cedex, France. Philippe.Jacquet@inria.fr., FR
Abstract.    We present an analysis of algorithms for finding saddle points in a random matrix, presented by Donald E. Knuth as Exercise 1.3.2-12 in The Art of Computer Programming . We estimate the average computing costs of three saddle point search algorithms. Amusingly, the asymptotic results in this analysis about matrix saddle points uses the same approach that leads to the celebrated saddle point method in complex analysis.

Key words. Matrix saddle point, Search, Probabilistic analysis.

Received December 22, 1997; revised March 15, 1998.

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  1. Nikolova, Mila (2000) Local Strong Homogeneity of a Regularized Estimator. SIAM Journal on Applied Mathematics 61(2)
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  2. Vaynblat, Dimitri (2000) Stability of Pole Solutions for Planar Propagating Flames: I. Exact Eigenvalues and Eigenfunctions. SIAM Journal on Applied Mathematics 60(2)
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  3. Chu, Moody T. (1998) Inverse Eigenvalue Problems. SIAM Review 40(1)
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  4. MacCluer, C. R. (2000) The Many Proofs and Applications of Perron's Theorem. SIAM Review 42(3)
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  5. Rump, Siegfried M. (1999) Ill-Conditioned Matrices Are Componentwise Near to Singularity. SIAM Review 41(1)
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  6. Li, Chi-Kwong (2000) Extremal Characterizations of the Schur Complement and Resulting Inequalities. SIAM Review 42(2)
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