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bauschke h introduction convexity optimization algorithms 2024

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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

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Bauschke H. An Introduction to Convexity, Optimization, and Algorithms 2024.pdf 15.0 MB

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bauschke h introduction convexity optimization algorithms 2024

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