Optimization and Machine Learning

Introduction

Rachid CHELOUAH

CY Cergy Paris University, France

Machine learning is revolutionizing our world. It is difficult to conceive of any other information technology that has developed so rapidly in recent years, in terms of real impact.

The fields of machine learning and optimization are highly interwoven. Optimization problems form the core of machine learning methods and modern optimization algorithms are using machine learning more and more to improve their efficiency.

Machine learning has applications in all areas of science. There are many learning methods, each of which uses a different algorithmic structure to optimize predictions, based on the data received. Hence, the first objective of this book is to shed light on key principles and methods that are common within both fields.

Machine learning and optimization share three components: representation, evaluation and iterative search. Yet while optimization solvers are generally designed to be fast and accurate on implicit models, machine learning methods need to be generic and trained offline on datasets. Machine learning problems present new challenges for optimization researchers, and machine learning practitioners seek simpler, generic optimization algorithms.

Quite recently, modern approaches to machine learning have also been applied to the design of optimization algorithms themselves, taking advantage of their ability to capture valuable information from complex structures in large spaces. Those capacities ...