Resolvent compositions for positive linear operators

Abstract Resolvent compositions were recently introduced as monotonicity-preserving operations that combine a set-valued monotone operator and a bounded linear operator. They generalize in particular the notion of a resolvent average. We analyze the resolvent compositions when the monotone operator is a positive linear operator. We establish several new properties, including Löwner partial order relations, concavity, and asymptotic behavior. In addition, we show that the resolvent composition operations are nonexpansive with respect to the Thompson metric. We also introduce a new form of geometric interpolation and explore its connections to resolvent compositions. Finally, we study two nonlinear equations based on resolvent compositions.

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Publication Details

Journal
Positivity
Published
2026-05-12
DOI
https://doi.org/10.1007/s11117-026-01176-w
Primary Topic
Optimization and Variational Analysis
Type
preprint
Field-Weighted Citation Impact
0.00

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preprint

Resolvent compositions for positive linear operators

Diego J. Cornejo
Positivity
Optimization and Variational Analysis
preprint

Resolvent compositions for positive linear operators

Diego J. Cornejo
preprint en

Abstract

Abstract Resolvent compositions were recently introduced as monotonicity-preserving operations that combine a set-valued monotone operator and a bounded linear operator. They generalize in particular the notion of a resolvent average. We analyze the resolvent compositions when the monotone operator is a positive linear operator. We establish several new properties, including Löwner partial order relations, concavity, and asymptotic behavior. In addition, we show that the resolvent composition operations are nonexpansive with respect to the Thompson metric. We also introduce a new form of geometric interpolation and explore its connections to resolvent compositions. Finally, we study two nonlinear equations based on resolvent compositions.

PositivityVol. 30(3)
North Carolina State University (US)
National Science Foundation
Openalex Percentile: Top 97%
Optimization and Variational Analysis
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