An Introduction to Convexity, Optimization, and Algorithms

A concise, self-contained volume introducing convex analysis and optimization algorithms with an emphasis on bridging the two. This volume explores cutting-edge algorithms, e.g. the proximal gradient, Douglas-Rachford, Peaceman-Rachford, and FISTA, with applications in fields including machine learning, signal processing, and image reconstruction.

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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.