Overview

This concise, self-contained volume introduces convex analysis and optimization algorithms, with an emphasis on bridging the two areas. It explores cutting-edge algorithms--such as the proximal gradient, Douglas-Rachford, Peaceman-Rachford, and FISTA--that have applications in machine learning, signal processing, image reconstruction, and other fields. An Introduction to Convexity, Optimization, and Algorithms contains algorithms illustrated by Julia examples and more than 200 exercises that enhance the reader's understanding of the topic. Clear explanations and step-by-step algorithmic descriptions facilitate self-study for individuals looking to enhance their expertise in convex analysis and optimization.

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9781611977790

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Heinz H. Bauschke is a professor of mathematics at the University of British Columbia (Kelowna Campus), Canada. His research interests lie in continuous optimization and analysis. He has authored more than 150 refereed publications, including books and conference proceedings. Dr. Bauschke has also collaborated with engineers from industry and received two patents. Walaa M. Moursi is an assistant professor in the Department of Combinatorics and Optimization, University of Waterloo, Canada. Her research interests lie in convex analysis and optimization algorithms, particularly splitting algorithms. In 2022, she was awarded the Ontario Early Researcher Award.

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