# Connections Between Adaptive Control and Optimization in Machine Learning

## TL;DR

This paper explores the deep connections between adaptive control and machine learning optimization, revealing shared concepts and proposing new avenues for algorithm analysis and improvement.

## Contribution

It uncovers fundamental links between adaptive control and machine learning optimization, leading to novel insights and solutions for higher order learning problems.

## Key findings

- Identifies similarities in update laws between the fields
- Discusses shared concepts like stability and performance
- Provides new analysis opportunities and solves a higher order learning problem

## Abstract

This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning. Starting from common output error formulations, similarities in update law modifications are examined. Concepts in stability, performance, and learning, common to both fields are then discussed. Building on the similarities in update laws and common concepts, new intersections and opportunities for improved algorithm analysis are provided. In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.