Ciarlet P Linear and Nonlinear Functional Analysis with Applications 2ed 2025 | 338.61 MB
Title: Linear and Nonlinear Programming Essentials
Author: Tanushri Kaniyar
Description:
"Linear and Nonlinear Programming Essentials" is a comprehensive textbook crafted for undergraduate students, providing an in-depth exploration of optimization theory and practice. Designed to be both accessible and rigorous, this book is an essential resource for students in mathematics, computer science, engineering, economics, and related fields.
We begin with an introduction to linear programming, covering fundamental concepts such as linear programming models, the simplex method, duality theory, and sensitivity analysis. Building upon this foundation, we delve into nonlinear programming, exploring convex optimization, gradient-based methods, and algorithms for solving nonlinear optimization problems.
Our emphasis on bridging theory with practice is a distinguishing feature. Real-world examples and case studies from fields like logistics, finance, and machine learning illustrate the practical relevance of optimization techniques, providing tangible insights into their applications.
With clear explanations, illustrative examples, and engaging exercises, we make the content 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 Essentials" offers a comprehensive and engaging journey into the world of optimization. This book equips you with the tools to tackle optimization problems confidently and proficiently.
"Linear and Nonlinear Programming Essentials" is a comprehensive textbook crafted for undergraduate students, providing an in-depth exploration of optimization theory and practice. Designed to be both accessible and rigorous, this book is an essential resource for students in mathematics, computer science, engineering, economics, and related fields.
We begin with an introduction to linear programming, covering fundamental concepts such as linear programming models, the simplex method, duality theory, and sensitivity analysis. Building upon this foundation, we delve into nonlinear programming, exploring convex optimization, gradient-based methods, and algorithms for solving nonlinear optimization problems.
Our emphasis on bridging theory with practice is a distinguishing feature. Real-world examples and case studies from fields like logistics, finance, and machine learning illustrate the practical relevance of optimization techniques, providing tangible insights into their applications.
With clear explanations, illustrative examples, and engaging exercises, we make the content 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 Essentials" offers a comprehensive and engaging journey into the world of optimization. This book equips you with the tools to tackle optimization problems confidently and proficiently.
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