A Survey of Optimization Methods from a Machine Learning Perspective

TL;DR

This survey reviews optimization methods in machine learning, discussing their principles, applications, challenges, and future directions to guide ongoing research in both fields.

Contribution

It provides a comprehensive systematic overview of optimization techniques from a machine learning perspective, highlighting recent progress and open problems.

Findings

Summarizes key optimization methods and their principles.

Highlights applications across various machine learning fields.

Identifies challenges and future research directions.

Abstract

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this paper, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Next, we summarize the applications and developments of optimization methods in some popular machine learning fields. Finally, we explore and give some challenges and open problems for the optimization in machine learning.