May 1, 2024
Updated May 9, 2025
19 minute read
Optimization, at its core, is the art and science of making the best possible decision given a set of choices and limitations. It's about finding the most effective or efficient way to achieve a specific goal, whether that's minimizing costs, maximizing profits, or finding the quickest route. This field is a cornerstone of decision-making in countless areas, from everyday life to complex scientific endeavors.
mxvlcl|
Find a path to becoming a Optimization. Learn more at:
OpenCourser.com/topic/mxvlcl/optimizatio
Reading list
We've selected 35 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Optimization.
Is considered a foundational text in convex optimization, a subfield with wide applications. It provides a comprehensive introduction to the subject, covering theoretical concepts and practical algorithms. It is widely used as a textbook in universities and is an excellent reference for researchers and practitioners. While mathematically rigorous, it emphasizes intuitive understanding and practical problem-solving.
A comprehensive and well-regarded textbook focusing on numerical methods for optimization. It covers both unconstrained and constrained optimization problems and is known for its clear explanations of algorithms. is suitable for graduate students and researchers and serves as a valuable reference for those implementing optimization techniques.
Focuses specifically on convex optimization, an important topic within optimization with many applications in signal processing, machine learning, and control theory. It provides a detailed treatment of the theory, algorithms, and applications of convex optimization, including recent advances in the field. Stephen Boyd world-renowned expert in convex optimization and this book is widely considered to be the definitive reference in the field.
Authored by a leading figure in convex optimization, this book provides a concise and advanced treatment of the subject. It is highly theoretical and suitable for graduate students and researchers with a strong mathematical background interested in the cutting edge of convex optimization theory.
Offers a rigorous introduction to linear optimization, a cornerstone of the field. It covers fundamental concepts, simplex method, duality, and network flow problems. It is often used as a textbook for introductory graduate courses in optimization and operations research. It solid foundation for understanding more advanced topics.
Provides a rigorous foundation in convex analysis and its applications in optimization. It delves into duality theory, minimax problems, and constrained optimization. It valuable resource for those seeking a deep theoretical understanding of convexity in optimization and is suitable for advanced graduate students and researchers.
For readers who are new to the field of optimization, this book provides a thorough grounding in the theory and techniques of optimization. It covers a wide range of optimization topics, including linear, nonlinear, convex, and stochastic optimization with an emphasis on real-world applications. It includes new material on direct methods for solving large-scale problems and on optimal control. One author, Edwin Chong, wrote two other books in optimization that are highly recommended for advanced readers.
Provides an accessible yet rigorous introduction to optimization theory and methods. It covers unconstrained and constrained optimization, linear programming, and introduces global search methods. It is suitable for upper-undergraduate and graduate students and can serve as a good starting point for those new to the field.
Provides an introduction to dynamic programming and optimal control, two closely related areas of optimization. It covers the theory and algorithms of dynamic programming and optimal control, as well as their applications in a wide range of fields, including robotics, finance, and manufacturing.
Provides a comprehensive treatment of stochastic optimization problems, which involve uncertainty. It covers theoretical foundations, algorithms, and applications. It is suitable for graduate students and researchers working on optimization problems with random elements.
A foundational text specifically on integer programming, a key area of discrete optimization. It covers the theory, formulations, and algorithms for solving problems where variables must take integer values. is essential for graduate students and researchers specializing in discrete optimization and combinatorial optimization.
A comprehensive textbook on combinatorial optimization, covering a wide range of topics includingグラフ理論 (graph theory), matroids, and approximation algorithms. It is suitable for graduate students and researchers in mathematics, computer science, and operations research.
Provides a balanced introduction to both linear and nonlinear optimization. It covers theoretical concepts and algorithms, with a focus on numerical methods. It is suitable for graduate students and researchers and can be a good reference for those working on practical optimization problems.
This widely used textbook provides a broad overview of operations research, with a significant focus on optimization techniques like linear programming, integer programming, and network models. It's known for its clear explanations and numerous examples, making it suitable for undergraduate and graduate students seeking a practical understanding of optimization within the context of operations research.
Covers nonlinear programming comprehensively and in-depth. It presents the underlying theory, algorithms, and applications of nonlinear programming in a clear and accessible manner. Topics covered include unconstrained and constrained optimization, convex and nonconvex optimization, and global and local optimization.
Comprehensive and rigorous treatment of mathematical optimization theory and methods. It covers a wide range of topics, including convex optimization, variational analysis, and optimal control. It is suitable for advanced undergraduates and graduate students in mathematics, engineering, and operations research.
This classic book provides a comprehensive introduction to combinatorial optimization, focusing on algorithms and complexity analysis. It covers a wide range of problems and techniques relevant to discrete optimization. While older, the fundamental concepts remain highly relevant for students and researchers in computer science and operations research.
This textbook offers a novel and geometrically oriented approach to optimization theory with continuous variables. It provides deep insights through numerous examples and historical context, making it suitable for a wide range of readers from beginners to experts interested in the mathematical foundations of optimization.
Focuses on optimization methods with a strong emphasis on engineering applications. It covers a variety of techniques, including gradient and non-gradient methods, and discusses applications in design and analysis. It is suitable for advanced undergraduate and graduate engineering students and practicing engineers.
Focuses on the modeling aspect of optimization, guiding readers on how to formulate various problems as optimization models. It's a valuable resource for students and practitioners who need to bridge the gap between real-world problems and mathematical optimization frameworks.
This text is an excellent introduction to nonlinear programming, which is an expansion of linear programming to problems with nonlinear objective functions and constraints. It includes theoretical foundations, algorithms, and applications. An example of the applications explained includes chemical and mechanical engineering.
Focuses on the crucial skill of formulating real-world problems as mathematical programming models. It provides numerous examples across various domains and emphasizes the modeling aspect rather than just the solution algorithms. It's highly recommended for students and practitioners who need to translate practical problems into an optimization framework.
Provides a thorough introduction to stochastic optimization. It covers the theory, algorithms, and applications of stochastic optimization, including topics such as Markov chains, Monte Carlo methods, and dynamic programming. It emphasizes the use of stochastic optimization to solve problems in a wide range of fields, including finance, manufacturing, and communication.
Based on a popular online course, this book provides an introduction to applied optimization with a focus on practical problem-solving using computational tools. It's suitable for students and practitioners who want to learn how to model and solve optimization problems in various applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/mxvlcl/optimizatio
Save
