Eigenvalues and condition numbers of random matrices
SIAM Journal on Matrix Analysis and Applications
ISCA '89 Proceedings of the 16th annual international symposium on Computer architecture
Polynomial and matrix computations (vol. 1): fundamental algorithms
Polynomial and matrix computations (vol. 1): fundamental algorithms
Fast Gaussian elimination with partial pivoting for matrices with displacement structure
Mathematics of Computation
Fast Probabilistic Algorithms for Verification of Polynomial Identities
Journal of the ACM (JACM)
Structured matrices and polynomials: unified superfast algorithms
Structured matrices and polynomials: unified superfast algorithms
Accuracy and Stability of Numerical Algorithms
Accuracy and Stability of Numerical Algorithms
On Wiedemann's Method of Solving Sparse Linear Systems
AAECC-9 Proceedings of the 9th International Symposium, on Applied Algebra, Algebraic Algorithms and Error-Correcting Codes
Probabilistic algorithms for sparse polynomials
EUROSAM '79 Proceedings of the International Symposiumon on Symbolic and Algebraic Computation
Journal of Complexity
Smoothed Analysis of the Condition Numbers and Growth Factors of Matrices
SIAM Journal on Matrix Analysis and Applications
Effect of small rank modification on the condition number of a matrix
Computers & Mathematics with Applications
Theoretical Computer Science
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MBA algorithm inverts a structured matrix in nearly linear arithmetic time but requires a serious restriction on the input class. We remove this restriction by means of randomization and extend the progress to some fundamental computations with polynomials, e.g., computing their GCDs and AGCDs, where most effective known algorithms rely on computations with matrices having Toeplitz-like structure. Furthermore, our randomized algorithms fix rank deficiency and ill conditioning of general and structured matrices. At the end we comment on a wide range of other natural extensions of our progress and underlying ideas.