Modern Numerical Nonlinear Optimization

Author:   Neculai Andrei
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2022
Volume:   195
ISBN:  

9783031087226


Pages:   807
Publication Date:   19 October 2023
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Modern Numerical Nonlinear Optimization


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Author:   Neculai Andrei
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2022
Volume:   195
Weight:   1.576kg
ISBN:  

9783031087226


ISBN 10:   3031087224
Pages:   807
Publication Date:   19 October 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Reviews

“This book gives a comprehensive description of the theoretical details and the computational performance of the modern optimization algorithms for solving … different areas of activity. … I think this book has the following two features. First, it emphasizes and illustrates a number of reliable and robust packages for solving … nonlinear optimization problems and applications. Second, the text is well illustrated with drawings and numerical studies of many large-scale test problems, which significantly increase the readability of the book.” (Xiaoliang Dong, Mathematical Reviews, September, 2023)


Author Information

Neculai Andrei holds a position at the Center for Advanced Modeling and Optimization at the Academy of Romanian Scientists in Bucharest, Romania. Dr. Andrei’s areas of interest include mathematical modeling, linear programming, nonlinear optimization, high performance computing, and numerical methods in mathematical programming. In addition to this present volume, Neculai Andrei has published several books with Springer including A Derivative-free Two Level Random Search Method for Unconstrained Optimization (2021), Nonlinear Conjugate Gradient Methods for Unconstrained Optimization (2020), Continuous Nonlinear Optimization for Engineering Applications in GAMS Technology (2017), and Nonlinear Optimization Applications Using the GAMS Technology (2013).

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