May 1, 2024
Updated May 9, 2025
18 minute read
A Comprehensive Guide to Discrete Optimization
89a5cx|
Find a path to becoming a Discrete Optimization. Learn more at:
OpenCourser.com/topic/89a5cx/discrete
Reading list
We've selected 17 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
Discrete Optimization.
Comprehensive graduate-level text covering the theory and algorithms of integer and combinatorial optimization. It is considered a foundational text in the field and is highly relevant for deepening understanding. While not recently published, its content remains essential for serious students and researchers.
Provides a comprehensive and systematic treatment of techniques for designing approximation algorithms. It is an excellent resource for those wanting to delve into contemporary algorithmic approaches for discrete optimization problems. It is suitable for graduate students and researchers.
Focusing on algorithms for NP-hard optimization problems, this book is essential for understanding contemporary topics in discrete optimization where finding optimal solutions is intractable. It's a well-regarded textbook for graduate students and researchers.
Explores the intersection of geometric methods and combinatorial optimization, providing advanced topics and techniques. It valuable resource for researchers interested in the theoretical underpinnings and advanced algorithms in the field. It is considered a classic in this niche.
A classic in the field, this book provides a solid introduction to combinatorial optimization, covering fundamental algorithms and complexity theory. It's an excellent resource for gaining a broad understanding and is often used as a textbook in undergraduate and graduate courses. While older, its foundational material is still highly relevant.
This recent publication explores the role of convexity in both discrete and continuous optimization, presenting new developments in the field. It is highly relevant for understanding contemporary research and advanced topics. Suitable for graduate students and researchers.
Provides a succinct and up-to-date view of combinatorial algorithms for network flow problems, including recent work. It valuable reference for those focusing on this specific area within discrete optimization and is suitable for graduate students and professionals.
This widely used introductory textbook for operations research, covering various optimization techniques, including linear programming and integer programming, which are foundational to discrete optimization. It's suitable for gaining a broad understanding and is often used in undergraduate programs. The latest edition includes recent topics like analytics, AI, and machine learning.
Collection of recent research papers in discrete optimization and scheduling, offering insights into the latest developments and contemporary topics in the field. It is most suitable for researchers and advanced graduate students looking for cutting-edge information.
Another excellent introductory text covering mathematical programming, including integer programming and network models. It provides a good foundation for understanding the modeling and algorithmic aspects of discrete optimization. Suitable for undergraduate and beginning graduate students.
Provides a concise and elegant introduction to algorithms, including topics relevant to discrete optimization like graph algorithms and linear programming. It's often used in undergraduate algorithms courses and is good for building a foundational understanding of algorithmic concepts.
Aims to provide a balance between the theoretical foundations and practical aspects of discrete optimization. It can be a good resource for both gaining a broad understanding and seeing how concepts are applied.
Offers a collection of solved problems, making it a practical resource for students to build skills in integer and combinatorial optimization. It complements theoretical texts and helps solidify understanding through practice. Suitable for undergraduate and beginning graduate students.
A widely-regarded comprehensive introduction to algorithms, this book covers many algorithms and data structures used in discrete optimization. While not exclusively focused on optimization, it provides essential algorithmic background and standard reference in computer science.
While not solely focused on optimization, this book provides essential background in discrete mathematics, which is fundamental to understanding discrete optimization concepts and proofs. It covers topics like graph theory, combinatorics, and algorithms. It widely used textbook for undergraduate discrete mathematics courses.
While aimed at competitive programmers, this book covers many algorithmic techniques relevant to discrete optimization, particularly in graph theory and dynamic programming. It's a good resource for building algorithmic skills and seeing applications in a problem-solving context. Suitable for high school and undergraduate students interested in the algorithmic side.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/89a5cx/discrete
Save
