Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, 2nd Edition free ebook download.

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Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, 2nd Edition free ebook download

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Author(s):	Amir Beck

Date:	2023	Format:	pdf	Language:	English	ISBN/ASIN:	1611977614
Pages:	366	OCR:		Quality:		ISBN13:
Uploader:	rubybook	Upload Date:	7/26/2024 3:14:43 AM

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1611977614 pdf
1611977614 pdf
Built on the framework of the successful first edition, this book serves as a modern introduction to the field of optimization. The author's objective is to provide the foundations of theory and algorithms of nonlinear optimization as well as to present a variety of applications from diverse areas of applied sciences. Introduction to Nonlinear Optimization gradually yet rigorously builds connections between theory, algorithms, applications, and actual implementation. The book contains several topics not typically included in optimization books, such as optimality conditions in sparsity constrained optimization, hidden convexity, and total least squares. Readers will discover a wide array of applications such as circle fitting, Chebyshev center, the Fermat–Weber problem, denoising, clustering, total least squares, and orthogonal regression. These applications are studied both theoretically and algorithmically, illustrating concepts such as duality. Python and MATLAB programs are used to show how the theory can be implemented. The extremely popular CVX toolbox (MATLAB) and CVXPY module (Python) are described and used. More than 250 theoretical, algorithmic, and numerical exercises enhance the reader's understanding of the topics. (More than 70 of the exercises provide detailed solutions, and many others are provided with final answers.) The theoretical and algorithmic topics are illustrated by Python and MATLAB examples.

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3267072	Download	https://nitroflare.com/view/A1796B1B4414...		23.00	rubybook

Tags: Algorithms Applications Introduction MATLAB Nonlinear Optimization Python Theory

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