2 citations · 2 across the 2 of their papers we have counts for
4 papers
A -Symmetric Quasi-Newton Method for Minimax Problems
Azam Asl, Haihao Lu, Jinwen Yang
Minimax problems have gained tremendous attentions across the optimization and machine learning community recently. In this paper, we introduce a new quasi-Newton method for minima…
Behavior of Limited Memory BFGS when Applied to Nonsmooth Functions and their Nesterov Smoothings
Azam Asl, Michael L. Overton
The motivation to study the behavior of limited-memory BFGS (L-BFGS) on nonsmooth optimization problems is based on two empirical observations: the widespread success of L-BFGS in…
Analysis of Limited-Memory BFGS on a Class of Nonsmooth Convex Functions
Azam Asl, Michael L. Overton
The limited memory BFGS (L-BFGS) method is widely used for large-scale unconstrained optimization, but its behavior on nonsmooth problems has received little attention. L-BFGS can…
Analysis of the Gradient Method with an Armijo-Wolfe Line Search on a Class of Nonsmooth Convex Functions
Azam Asl, Michael L. Overton
It has long been known that the gradient (steepest descent) method may fail on nonsmooth problems, but the examples that have appeared in the literature are either devised specific…