53 citations · 143 across the 24 of their papers we have counts for
6 papers · 1 filter
Variational Properties of Matrix Functions via the Generalized Matrix-Fractional Function
James V. Burke, Yuan Gao, Tim Hoheisel
We show that many important convex matrix functions can be represented as the partial infimal projection of the generalized matrix fractional (GMF) and a relatively simple convex f…
Strong Metric (Sub)regularity of KKT Mappings for Piecewise Linear-Quadratic Convex-Composite Optimization
James V. Burke, Abraham Engle
This work concerns the local convergence theory of Newton and quasi-Newton methods for convex-composite optimization: minimize f(x):=h(c(x)), where h is an infinite-valued proper c…
Line Search and Trust-Region Methods for Convex-Composite Optimization
James V. Burke, Abraham Engle
We consider descent methods for solving non-finite valued nonsmooth convex-composite optimization problems that employ Gauss-Newton subproblems to determine the iteration update. S…
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…
Inexact Sequential Quadratic Optimization with Penalty Parameter Updates Within the QP Solve: Extended Version
James V. Burke, Frank E. Curtis, Hao Wang +1
This paper focuses on the design of sequential quadratic optimization (commonly known as SQP) methods for solving large-scale nonlinear optimization problems. The most computationa…
Fast Robust Methods for Singular State-Space Models
Jonathan Jonker, Aleksandr Y. Aravkin, James V. Burke +2
State-space models are used in a wide range of time series analysis formulations. Kalman filtering and smoothing are work-horse algorithms in these settings. While classic algorith…