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20182022
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math.OC2022

The Inexact Cyclic Block Proximal Gradient Method and Properties of Inexact Proximal Maps

Leandro Maia, David Huckleberry Gutman, Ryan Christopher Hughes

This paper expands the Cyclic Block Proximal Gradient method for block separable composite minimization by allowing for inexactly computed gradients and proximal maps. The resultan…

math.OC2019

Coordinate Descent Without Coordinates: Tangent Subspace Descent on Riemannian Manifolds

David Huckleberry Gutman, Nam Ho-Nguyen

We extend coordinate descent to manifold domains, and provide convergence analyses for geodesically convex and non-convex smooth objective functions. Our key insight is to draw an…

math.OC2019

The condition number of a function relative to a set

David H. Gutman, Javier F. Pena

The condition number of a differentiable convex function, namely the ratio of its smoothness to strong convexity constants, is closely tied to fundamental properties of the functio…

math.OC2018

Enhanced Basic Procedures for the Projection and Rescaling Algorithm

David H. Gutman

Using an efficient algorithmic implementation of Caratheodory's theorem, we propose three enhanced versions of the Projection and Rescaling algorithm's basic procedures each of whi…

math.OC2018

Convergence rates of proximal gradient methods via the convex conjugate

David H. Gutman, Javier F. Pena

We give a novel proof of the and convergence rates of the proximal gradient and accelerated proximal gradient methods for composite convex minimization. The cru…