Parallel and Distributed algorithms for ML problems
Darina Dvinskikh, Alexander Gasnikov, Alexander Rogozin, Alexander, Beznosikov
TL;DR
This paper surveys modern parallel and distributed algorithms designed to efficiently solve sum-type convex minimization problems prevalent in machine learning applications.
Contribution
It provides a comprehensive overview of current approaches, highlighting recent advances and techniques in parallel and distributed optimization for ML.
Findings
Summarizes key algorithms and their theoretical properties
Identifies challenges and open problems in the field
Highlights recent trends and future directions
Abstract
In this paper we make a survey of modern parallel and distributed approaches to solve sum-type convex minimization problems come from ML applications.
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Complexity and Algorithms in Graphs · Sparse and Compressive Sensing Techniques