53 citations · 143 across the 24 of their papers we have counts for
22 papers · 1 filter
A Scale-Shape Dual Newton Method for Entropic Least Squares
Nicholas Barnfield, James V. Burke, Michael P. Friedlander +1
We give a damped inexact Newton method for entropy-regularized least-squares on the nonnegative orthant that converges globally at a linear rate with iteration comp…
Convergence of the Gradient Sampling Algorithm on Directionally Lipschitz Functions
James V. Burke, Qiuying Lin
The convergence theory for the gradient sampling algorithm is extended to directionally Lipschitz functions. Although directionally Lipschitz functions are not necessarily locally…
A study of convex convex-composite functions via infimal convolution with applications
James V. Burke, Tim Hoheisel, Quang V. Nguyen
In this note we provide a full conjugacy and subdifferential calculus for convex convex-composite functions in finite-dimensional space. Our approach, based on infimal convolution…
Robust Singular Smoothers For Tracking Using Low-Fidelity Data
Jonathan Jonker, Aleksandr Aravkin, James V. Burke +2
Tracking underwater autonomous platforms is often difficult because of noisy, biased, and discretized input data. Classic filters and smoothers based on standard assumptions of Gau…
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…