“Linear and Nonlinear Programming” is a comprehensive textbook meticulously crafted for undergraduate students in the United States, offering a thorough exploration of optimization theory and practice. Designed to be accessible yet rigorous, this book serves as an essential resource for students studying mathematics, computer science, engineering, economics, and related disciplines.
The book begins with a comprehensive introduction to linear programming, covering fundamental concepts such as linear programming models, the simplex method, duality theory, and sensitivity analysis. Building upon this foundation, subsequent chapters delve into the realm of nonlinear programming, exploring convex optimization, gradient-based methods, and algorithms for solving nonlinear optimization problems.
One of the distinguishing features of this book is its emphasis on bridging theory with practice. Real-world examples and case studies drawn from diverse fields such as logistics, finance, and machine learning illustrate the practical relevance of optimization techniques, providing students with tangible insights into their application in various domains.
Accessible language, clear explanations, and carefully crafted examples make this book suitable for students at all levels of expertise. Whether you’re encountering optimization for the first time or seeking to deepen your understanding of advanced techniques, “Linear and Nonlinear Programming” offers a comprehensive and engaging journey into the fascinating world of optimization. With its blend of theory, application, and practical exercises, this book equips students with the tools they need to tackle optimization problems with confidence and proficiency.
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