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New framework accelerates fixed-point iterations using learning to optimize

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for optimizing fixed-point iterations, a common method for solving mathematical problems. This framework, termed "learning to optimize" (L2O), involves introducing carefully chosen perturbations to standard Krasnosel'skii-Mann iterations. The goal is to enhance average-case performance across a range of problems while ensuring convergence guarantees are maintained. The approach has been shown to accelerate solutions for structured monotone inclusion problems and has been validated on a best approximation problem using an augmented Douglas-Rachford splitting algorithm. AI

IMPACT This research could lead to more efficient solvers for complex mathematical problems encountered in AI and machine learning.

RANK_REASON The cluster contains a research paper detailing a new algorithmic framework for solving fixed-point problems. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework accelerates fixed-point iterations using learning to optimize

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The cluster contains a research paper detailing a new algorithmic framework for solving fixed-point problems. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Andrea Martin, Giuseppe Belgioioso ·

    Learning to accelerate Krasnosel'skii-Mann fixed-point iterations with guarantees

    arXiv:2601.07665v2 Announce Type: replace-cross Abstract: We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our idea is to deliberately inject summable perturbations into a standard Krasnosel's…

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