53 citations · 143 across the 24 of their papers we have counts for
33 papers
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…
Analysis of Relaxation Methods for Feature Selection in Mixed Effects Models
Aleksandr Aravkin, James Burke, Aleksei Sholokhov +1
Linear Mixed-Effects (LME) models are a fundamental tool for modeling clustered data, including cohort studies, longitudinal data analysis, and meta-analysis. The design and analys…
A Relaxation Approach to Feature Selection for Linear Mixed Effects Models
Aleksei Sholokhov, James V. Burke, Damian F. Santomauro +2
Linear Mixed-Effects (LME) models are a fundamental tool for modeling correlated data, including cohort studies, longitudinal data analysis, and meta-analysis. Design and analysis…
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…
IRLS for Sparse Recovery Revisited: Examples of Failure and a Remedy
Aleksandr Y. Aravkin, James V. Burke, Daiwei He
Compressed sensing is a central topic in signal processing with myriad applications, where the goal is to recover a signal from as few observations as possible. Iterative re-weight…
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…