Description
Numerical Optimization presents a comprehensive treatment of the theory and computational methods used to solve optimization problems encountered in science, engineering, and applied mathematics. Focusing on the development and analysis of numerical algorithms, the book examines both unconstrained and constrained optimization, covering topics such as line search methods, trust-region methods, Newton and quasi-Newton algorithms, nonlinear least squares, and large-scale optimization. Combining mathematical rigor with practical implementation, it explains the convergence, efficiency, and stability of optimization techniques, making it an authoritative reference for advanced students, researchers, and professionals working in optimization, machine learning, engineering, and computational science.
