JMLR
Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method
Authors
Ryan J. Tibshirani
Samy Wu Fung
Howard Heaton
Stanley Osher
Paper Information
-
Journal:
Journal of Machine Learning Research -
Added to Tracker:
Jul 15, 2025
Abstract
We study approximations to the Moreau envelope---and infimal convolutions more broadly---based on Laplace's method, a classical tool in analysis which ties certain integrals to suprema of their integrands. We believe the connection between Laplace's method and infimal convolutions is generally deserving of more attention in the study of optimization and partial differential equations, since it bears numerous potentially important applications, from proximal-type algorithms to Hamilton-Jacobi equations.
Author Details
Ryan J. Tibshirani
AuthorSamy Wu Fung
AuthorHoward Heaton
AuthorStanley Osher
AuthorCitation Information
APA Format
Ryan J. Tibshirani
,
Samy Wu Fung
,
Howard Heaton
&
Stanley Osher
.
Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method.
Journal of Machine Learning Research
.
BibTeX Format
@article{JMLR:v26:24-0944,
author = {Ryan J. Tibshirani and Samy Wu Fung and Howard Heaton and Stanley Osher},
title = {Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method},
journal = {Journal of Machine Learning Research},
year = {2025},
volume = {26},
number = {72},
pages = {1--36},
url = {http://jmlr.org/papers/v26/24-0944.html}
}