Optimization approaches in machine learning (ML) are essential for training models to
obtain high performance across numerous domains. The article provides a comprehensive …

Machine learning is fast evolving, with numerous theoretical advances and applications in a
variety of domains. In reality, most machine learning algorithms are based on optimization …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

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 …

The fields of machine learning and mathematical programming are increasingly intertwined.
Optimization problems lie at the heart of most machine learning approaches. The Special …

Optimization happens everywhere. Machine learning is one example of such and gradient
descent is probably the most famous algorithm for performing optimization. Optimization …

This section first provides an overview of the machine learning field in artificial intelligence
(AI). The most well-regarded classes of methods in AI are discussed to show where AI …

This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

The extraordinary success of Machine Learning (ML) in many complex heuristic fields has
promoted its introduction in more analytical engineering fields, improving or substituting …