47 citations · 132 across the 12 of their papers we have counts for
11 papers · 1 filter
Multi-fidelity robust controller design with gradient sampling
Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer
Robust controllers that stabilize dynamical systems even under disturbances and noise are often formulated as solutions of nonsmooth, nonconvex optimization problems. While methods…
On Properties of Univariate Max Functions at Local Maximizers
Tim Mitchell, Michael L. Overton
More than three decades ago, Boyd and Balakrishnan established a regularity result for the two-norm of a transfer function at maximizers. Their result extends easily to the stateme…
Local Minimizers of the Crouzeix Ratio: A Nonsmooth Optimization Case Study
Michael L. Overton
Given a square matrix and a polynomial , the Crouzeix ratio is the norm of the polynomial on the field of values of divided by the 2-norm of the matrix . Crouzeix'…
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…
Gradient Sampling Methods for Nonsmooth Optimization
James V. Burke, Frank E. Curtis, Adrian S. Lewis +2
This paper reviews the gradient sampling methodology for solving nonsmooth, nonconvex optimization problems. An intuitively straightforward gradient sampling algorithm is stated an…