09-23-2026, 08:03 PM
Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, Second Edition, by Amir Beck, is a graduate/advanced-undergraduate introduction to the mathematical foundations and computational methods of nonlinear optimization. The book develops the subject progressively, beginning with calculus, linear algebra, optimality conditions, and least-squares problems, then moving to gradient and Newton methods, convex sets and convex functions, and convex optimization. It emphasizes the connection between mathematical theory and practical algorithms rather than treating optimization purely analytically.
The later chapters focus on constrained optimization, including optimization over convex sets, linearly constrained problems, the Karush–Kuhn–Tucker (KKT) conditions, and duality. A distinctive feature of the second edition is its treatment of topics such as sparsity-constrained optimization, hidden convexity, and total least squares, together with applications including denoising, clustering, circle fitting, the Fermat–Weber problem, and orthogonal regression.
Python and MATLAB implementations—including CVXPY and MATLAB's CVX toolbox—are used to connect the mathematics with actual computation, supported by more than 250 theoretical, algorithmic, and numerical exercises.
BOOK
The later chapters focus on constrained optimization, including optimization over convex sets, linearly constrained problems, the Karush–Kuhn–Tucker (KKT) conditions, and duality. A distinctive feature of the second edition is its treatment of topics such as sparsity-constrained optimization, hidden convexity, and total least squares, together with applications including denoising, clustering, circle fitting, the Fermat–Weber problem, and orthogonal regression.
Python and MATLAB implementations—including CVXPY and MATLAB's CVX toolbox—are used to connect the mathematics with actual computation, supported by more than 250 theoretical, algorithmic, and numerical exercises.
BOOK
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