15 citations · 18 across the 2 of their papers we have counts for
5 papers
A new framework for the computation of Hessians
Robert M. Gower, Margarida P. Mello
We investigate the computation of Hessian matrices via Automatic Differentiation, using a graph model and an algebraic model. The graph model reveals the inherent symmetries involv…
Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization
Ahmed Khaled, Othmane Sebbouh, Nicolas Loizou +2
We present a unified theorem for the convergence analysis of stochastic gradient algorithms for minimizing a smooth and convex loss plus a convex regularizer. We do this by extendi…
Optimal mini-batch and step sizes for SAGA
Nidham Gazagnadou, Robert M. Gower, Joseph Salmon
Recently it has been shown that the step sizes of a family of variance reduced gradient methods called the JacSketch methods depend on the expected smoothness constant. In particul…
Stochastic Block BFGS: Squeezing More Curvature out of Data
Robert M. Gower, Donald Goldfarb, Peter Richtárik
We propose a novel limited-memory stochastic block BFGS update for incorporating enriched curvature information in stochastic approximation methods. In our method, the estimate of…
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…