Variational analysis of proximal compositions and integral proximal mixtures

This paper establishes various variational properties of parametrized versions of two convexity-preserving constructs that were recently introduced in the literature: the proximal composition of a function and a linear operator, and the integral proximal mixture of arbitrary families of functions and linear operators. We study in particular convexity, Legendre conjugacy, differentiability, Moreau envelopes, coercivity, minimizers, recession functions, and perspective functions of these constructs, as well as their asymptotic behavior as the parameter varies. The special case of the proximal expectation of a family of functions is also discussed.

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Publication Details

Journal
Evolution equations and control theory
Published
2025-02-10
DOI
https://doi.org/10.3934/eect.2025017
Citations
1
Primary Topic
Numerical methods in inverse problems
Type
article
Field-Weighted Citation Impact
3.69

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article

Variational analysis of proximal compositions and integral proximal mixtures

Patrick L. Combettes, Diego J. Cornejo
1 citations
Evolution equations and control theory
Numerical methods in inverse problems
3.69
article

Variational analysis of proximal compositions and integral proximal mixtures

Patrick L. Combettes, Diego J. Cornejo
article en
1 citations

Abstract

This paper establishes various variational properties of parametrized versions of two convexity-preserving constructs that were recently introduced in the literature: the proximal composition of a function and a linear operator, and the integral proximal mixture of arbitrary families of functions and linear operators. We study in particular convexity, Legendre conjugacy, differentiability, Moreau envelopes, coercivity, minimizers, recession functions, and perspective functions of these constructs, as well as their asymptotic behavior as the parameter varies. The special case of the proximal expectation of a family of functions is also discussed.

Evolution equations and control theoryVol. 17(0)
North Carolina State University (US)
National Science Foundation
Climate action
Openalex Percentile: Top 14%
Numerical methods in inverse problems
3.69
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