Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with MATLAB
Amir Beck, Israel Institute of Technology
SIAM, 2014
ISBN: 978-1-611973-64-8;
Language: English
Introduction to Nonlinear Optimization provides the foundations of the theory of nonlinear optimization, as well as some related algorithms, and presents a variety of applications from diverse areas of applied sciences. The author combines three pillars of optimization—theoretical and algorithmic foundation, familiarity with various applications, and the ability to apply the theory and algorithms on actual problems—and rigorously and gradually builds the connection between theory, algorithms, applications, and implementation.
Readers will find:
- More than 170 theoretical, algorithmic, and numerical exercises that deepen and enhance understanding of specified topics
- Several subjects not typically found in optimization books—for example, optimality conditions in sparsity-constrained optimization, hidden convexity, and total least squares
- A large number of applications discussed theoretically and algorithmically, such as circle fitting, Chebyshev center, the Fermat–Weber problem, denoising, clustering, total least squares, and orthogonal regression
- Theoretical and algorithmic topics demonstrated by the MATLAB toolbox CVX and a package of MATLAB code files posted on the website
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