Gradient Methods for Non-convex Optimization

Abstract

Non-convex optimization forms bedrock of most modern machine learning (ML) techniques such as deep learning. While non-convex optimization problems have been studied for the past several decades, ML-based problems have significantly different characteristics and requirements due to large datasets and high-dimensional parameter spaces along with the statistical nature of the problem. Over the last few years, there has been a flurry of activity in non-convex optimization for such ML problems. This article surveys a few of the foundational approaches in this domain.