Eigenvalues and condition numbers of random matrices
SIAM Journal on Matrix Analysis and Applications
Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Journal of the ACM (JACM)
Tails of Condition Number Distributions
SIAM Journal on Matrix Analysis and Applications
On random ±1 matrices: Singularity and determinant
Random Structures & Algorithms
The smoothed analysis of algorithms
FCT'05 Proceedings of the 15th international conference on Fundamentals of Computation Theory
On probabilistic analysis of randomization in hybrid symbolic-numeric algorithms
Proceedings of the 2007 international workshop on Symbolic-numeric computation
Smoothed analysis: an attempt to explain the behavior of algorithms in practice
Communications of the ACM - A View of Parallel Computing
Smooth Analysis of the Condition Number and the Least Singular Value
APPROX '09 / RANDOM '09 Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques
Additive preconditioning for matrix computations
CSR'08 Proceedings of the 3rd international conference on Computer science: theory and applications
Smoothed Analysis of Moore-Penrose Inversion
SIAM Journal on Matrix Analysis and Applications
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Let M be an arbitrary n by n matrix. We study the conditionnumber a random perturbation M+Nn of M, where Nn is arandom matrix. It is shown that, under very general conditions on M and Mn, the condition number of M+Nn is polynomial in nwith very high probability. The main novelty here is that we allow Nn to have discrete distribution.