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math.NA2016★ 15 cited
Linearly Convergent Randomized Iterative Methods for Computing the Pseudoinverse
Robert M. Gower, Peter Richtárik
We develop the first stochastic incremental method for calculating the Moore-Penrose pseudoinverse of a real matrix. By leveraging three alternative characterizations of pseudoinve…
math.NA2016★ 13 cited
Randomized Quasi-Newton Updates are Linearly Convergent Matrix Inversion Algorithms
Robert M. Gower, Peter Richtárik
We develop and analyze a broad family of stochastic/randomized algorithms for inverting a matrix. We also develop specialized variants maintaining symmetry or positive definiteness…
math.NA2016★ 53 cited
Stochastic Dual Ascent for Solving Linear Systems
Robert Mansel Gower, Peter Richtarik
We develop a new randomized iterative algorithm---stochastic dual ascent (SDA)---for finding the projection of a given vector onto the solution space of a linear system. The method…