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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
"Optimization Techniques: Practical Approaches" provides a comprehensive guide to the various methods and applications of optimization across multiple fields. The book begins with an introduction to the fundamental concepts of optimization and progresses through classical and modern techniques such as gradient-based methods, metaheuristics, and hybrid approaches. It delves into specific applications in machine learning, industry, energy systems, finance, economics, and healthcare. The book also explores advanced topics like quantum optimization and real-time optimization, and it includes detailed explanations, mathematical formulations, examples, pseudocode, and practical applications. Supplementary sections cover mathematical foundations, a glossary of terms, a bibliography, and an index, making this book a valuable resource for students, researchers, practitioners, and educators.
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This title is printed to order. This book may have been self-published. If so, we cannot guarantee the quality of the content. In the main most books will have gone through the editing process however some may not. We therefore suggest that you be aware of this before ordering this book. If in doubt check either the author or publisher’s details as we are unable to accept any returns unless they are faulty. Please contact us if you have any questions.
"Optimization Techniques: Practical Approaches" provides a comprehensive guide to the various methods and applications of optimization across multiple fields. The book begins with an introduction to the fundamental concepts of optimization and progresses through classical and modern techniques such as gradient-based methods, metaheuristics, and hybrid approaches. It delves into specific applications in machine learning, industry, energy systems, finance, economics, and healthcare. The book also explores advanced topics like quantum optimization and real-time optimization, and it includes detailed explanations, mathematical formulations, examples, pseudocode, and practical applications. Supplementary sections cover mathematical foundations, a glossary of terms, a bibliography, and an index, making this book a valuable resource for students, researchers, practitioners, and educators.