Algorithms for Convex Optimization provides a rigorous yet accessible introduction to modern optimization methods that underpin both discrete and continuous problems. Readers explore how these techniques have transformed maximum flow, matching, and submodular function minimization.

The text derives key algorithms from first principles, emphasizing clear proofs and precise runtime analysis. Major methods include:

• Gradient and mirror descent
• Interior point methods
• Ellipsoid methods

Ideal for researchers and professionals in computer science, data science, and machine learning, this self-contained volume builds deep understanding of convex optimization and its impact on cutting-edge algorithm design.

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