Optimization Techniques With Matlab
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Optimization Techniques with MATLAB
Author: Clapton E.
language: en
Publisher: Createspace Independent Publishing Platform
Release Date: 2016-11-10
MATLAB Optimization Toolbox provides widely used algorithms for and large-scale optimization. These algorithms solve constrained and unconstrained continuous and discrete problems. The toolbox, developed in this book, includes functions for linear programming, quadratic programming, binary integer programming, nonlinear optimization, nonlinear least squares, systems of nonlinear equations, and multiobjective optimization. You can use them to find optimal solutions, perform tradeoff analyses, balance multiple design alternatives, and incorporate optimization methods into algorithms and models.
Solving Optimization Problems with MATLAB®
Author: Dingyü Xue
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2020-04-06
This book focuses on solving optimization problems with MATLAB. Descriptions and solutions of nonlinear equations of any form are studied first. Focuses are made on the solutions of various types of optimization problems, including unconstrained and constrained optimizations, mixed integer, multiobjective and dynamic programming problems. Comparative studies and conclusions on intelligent global solvers are also provided.
Classical and Modern Optimization Techniques Applied to Control and Modeling
The book presents a detailed and unified treatment of the theory and applications of optimization applied to control and modeling, focusing on nature-inspired optimization algorithms to optimally tune the parameters of linear and nonlinear controllers and models, with emphasis on tower crane systems and other representative applications. Classical and Modern Optimization Techniques Applied to Control and Modeling combines classical and modern approaches to optimization, based on the authors’ experience in the field, and presents in a unified structure the essential aspects of optimization in control and modeling from a control engineer’s point of view. It covers linear and nonlinear controllers, and neural networks based on reinforcement learning are considered and analyzed because of the need to reduce the complexity of the controllers and their design so that they can be practical to implement as low-cost automation solutions. The chapters are designed to quickly make the concepts of optimization, control, reinforcement learning, and neural networks understandable to readers with limited experience. This book is intended for a broad audience, including undergraduate and graduate students, engineers (designers, practitioners, and researchers), and anyone facing challenging control problems.