3k citations · 9k across the 154 of their papers we have counts for
8 papers · 1 filter
The Complexity of Primal-Dual Fixed Point Methods for Ridge Regression
Ademir Alves Riberio, Peter Richtárik
We study the ridge regression (L2 regularized least squares) problem and its dual, which is also a ridge regression problem. We observe that the optimality conditions describing th…
Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory
Peter Richtárik, Martin Takáč
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive…
Parallel Stochastic Newton Method
Mojmír Mutný, Peter Richtárik
We propose a parallel stochastic Newton method (PSN) for minimizing unconstrained smooth convex functions. We analyze the method in the strongly convex case, and give conditions un…
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…
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…
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…