Description
Convex Optimization provides a rigorous and comprehensive introduction to the theory, algorithms, and practical applications of convex optimization. The book explains how a wide range of engineering, scientific, and decision-making problems can be formulated as convex optimization problems and solved efficiently using well-established mathematical methods. Covering topics such as convex sets and functions, duality, constrained optimization, interior-point methods, and numerical algorithms, it combines mathematical rigor with practical insight to demonstrate the broad applicability of optimization techniques in fields including machine learning, signal processing, control systems, finance, and operations research. Widely regarded as a standard reference, it is an essential resource for advanced students, researchers, and professionals in applied mathematics, engineering, and computer science.
