Textbook in PDF format

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,
more than 200 exercises that enhance the reader's understanding of the topic, and
clear explanations and step-by-step algorithmic descriptions that facilitate self-study for individuals looking to enhance their expertise in convex analysis and optimization.
Audience
Designed for courses in convex analysis, numerical optimization, and related subjects, this volume is intended for undergraduate and graduate students in mathematics, computer science, and engineering. Its concise length makes it ideal for a one-semester course. Researchers and professionals in applied areas, such as data science and machine learning, will find insights relevant to their work