A Derivative-free Two Level Random Search Method for Unconstrained Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Derivative-free Two Level Random Search Method for Unconstrained Optimization write by Neculai Andrei. This book was released on 2021-03-31. A Derivative-free Two Level Random Search Method for Unconstrained Optimization available in PDF, EPUB and Kindle. The book is intended for graduate students and researchers in mathematics, computer science, and operational research. The book presents a new derivative-free optimization method/algorithm based on randomly generated trial points in specified domains and where the best ones are selected at each iteration by using a number of rules. This method is different from many other well established methods presented in the literature and proves to be competitive for solving many unconstrained optimization problems with different structures and complexities, with a relative large number of variables. Intensive numerical experiments with 140 unconstrained optimization problems, with up to 500 variables, have shown that this approach is efficient and robust. Structured into 4 chapters, Chapter 1 is introductory. Chapter 2 is dedicated to presenting a two level derivative-free random search method for unconstrained optimization. It is assumed that the minimizing function is continuous, lower bounded and its minimum value is known. Chapter 3 proves the convergence of the algorithm. In Chapter 4, the numerical performances of the algorithm are shown for solving 140 unconstrained optimization problems, out of which 16 are real applications. This shows that the optimization process has two phases: the reduction phase and the stalling one. Finally, the performances of the algorithm for solving a number of 30 large-scale unconstrained optimization problems up to 500 variables are presented. These numerical results show that this approach based on the two level random search method for unconstrained optimization is able to solve a large diversity of problems with different structures and complexities. There are a number of open problems which refer to the following aspects: the selection of the number of trial or the number of the local trial points, the selection of the bounds of the domains where the trial points and the local trial points are randomly generated and a criterion for initiating the line search.
A Derivative-free Two Level Random Search Method for Unconstrained Optimization
A Derivative-free Two Level Random Search Method for Unconstrained Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Derivative-free Two Level Random Search Method for Unconstrained Optimization write by Neculai Andrei. This book was released on 2021. A Derivative-free Two Level Random Search Method for Unconstrained Optimization available in PDF, EPUB and Kindle. The book is intended for graduate students and researchers in mathematics, computer science, and operational research. The book presents a new derivative-free optimization method/algorithm based on randomly generated trial points in specified domains and where the best ones are selected at each iteration by using a number of rules. This method is different from many other well established methods presented in the literature and proves to be competitive for solving many unconstrained optimization problems with different structures and complexities, with a relative large number of variables. Intensive numerical experiments with 140 unconstrained optimization problems, with up to 500 variables, have shown that this approach is efficient and robust. Structured into 4 chapters, Chapter 1 is introductory. Chapter 2 is dedicated to presenting a two level derivative-free random search method for unconstrained optimization. It is assumed that the minimizing function is continuous, lower bounded and its minimum value is known. Chapter 3 proves the convergence of the algorithm. In Chapter 4, the numerical performances of the algorithm are shown for solving 140 unconstrained optimization problems, out of which 16 are real applications. This shows that the optimization process has two phases: the reduction phase and the stalling one. Finally, the performances of the algorithm for solving a number of 30 large-scale unconstrained optimization problems up to 500 variables are presented. These numerical results show that this approach based on the two level random search method for unconstrained optimization is able to solve a large diversity of problems with different structures and complexities. There are a number of open problems which refer to the following aspects: the selection of the number of trial or the number of the local trial points, the selection of the bounds of the domains where the trial points and the local trial points are randomly generated and a criterion for initiating the line search.
Derivative-Free and Blackbox Optimization
Derivative-Free and Blackbox Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Derivative-Free and Blackbox Optimization write by Charles Audet. This book was released on 2017-12-02. Derivative-Free and Blackbox Optimization available in PDF, EPUB and Kindle. This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix.
Nonlinear Optimization Applications Using the GAMS Technology
Nonlinear Optimization Applications Using the GAMS Technology - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonlinear Optimization Applications Using the GAMS Technology write by Neculai Andrei. This book was released on 2013-06-22. Nonlinear Optimization Applications Using the GAMS Technology available in PDF, EPUB and Kindle. Here is a collection of nonlinear optimization applications from the real world, expressed in the General Algebraic Modeling System (GAMS). The concepts are presented so that the reader can quickly modify and update them to represent real-world situations.
Introduction to Derivative-Free Optimization
Introduction to Derivative-Free Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Derivative-Free Optimization write by Andrew R. Conn. This book was released on 2009-04-16. Introduction to Derivative-Free Optimization available in PDF, EPUB and Kindle. The first contemporary comprehensive treatment of optimization without derivatives. This text explains how sampling and model techniques are used in derivative-free methods and how they are designed to solve optimization problems. It is designed to be readily accessible to both researchers and those with a modest background in computational mathematics.
