Adaptive Mirror Descent Methods for Convex Programming Problems with delta-subgradients
Fedor S. Stonyakin
TL;DR
This paper introduces adaptive mirror descent algorithms tailored for convex optimization problems involving delta-subgradients, providing theoretical guarantees for their performance.
Contribution
It presents novel adaptive mirror descent methods specifically designed for convex problems with delta-subgradients, along with theoretical analysis.
Findings
Proved convergence properties of the proposed methods
Established bounds on the performance with delta-subgradients
Demonstrated effectiveness through theoretical results
Abstract
We propose some adaptive mirror descent dethods for convex programming problems with delta-subgradients and prove some theoretical results.
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Advanced Optimization Algorithms Research · Sparse and Compressive Sensing Techniques