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210079901
Approximation Algorithms and Semidefinite Programming
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Item #:
210079901
BDT 12617
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Semidefinite programs constitute one of the largest classes of optimization problems that can be solved with reasonable efficiency.
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What Stands Out
Comprehensive Coverage
This edition provides an in-depth exploration of both approximation algorithms and semidefinite programming, making complex concepts accessible for students and researchers seeking a robust understanding of these critical areas in optimization.
Practical Applications
Real-world case studies and examples illustrate the utility of approximation algorithms in solving NP-hard problems, empowering readers to apply theoretical knowledge to practical situations effectively.
Updated Insights
The 2012 edition includes the latest research developments and techniques, positioning it as an essential reference for academics and practitioners keen on staying at the forefront of optimization advancements.
Product Details
- Semidefinite programs constitute one of the largest classes of optimization problems that can be solved with reasonable efficiency - both in theory and practice. They play a key role in a variety of research areas, such as combinatorial optimization, approximation algorithms, computational complexity, graph theory, geometry, real algebraic geometry and quantum computing. This book is an introduction to selected aspects of semidefinite programming and its use in approximation algorithms. It covers the basics but also a significant amount of recent and more advanced material. There are many computational problems, such as MAXCUT, for which one cannot reasonably expect to obtain an exact solution efficiently, and in such case, one has to settle for approximate solutions. For MAXCUT and its relatives, exciting recent results suggest that semidefinite programming is probably the ultimate tool. Indeed, assuming the Unique Games Conjecture, a plausible but as yet unproven hypothesis, it was shown that for these problems, known algorithms based on semidefinite programming deliver the best possible approximation ratios among all polynomial-time algorithms. This book follows the “semidefinite side” of these developments, presenting some of the main ideas behind approximation algorithms based on semidefinite programming. It develops the basic theory of semidefinite programming, presents one of the known efficient algorithms in detail, and describes the principles of some others. It also includes applications, focusing on approximation algorithms.
| Publisher | Springer |
| Publication date | January 13, 2012 |
| Edition | 2012th |
| Language | English |
| Print length | 262 pages |
| ISBN-10 | 9783642220142 |
| ISBN-13 | 978-3642220142 |
| Item Weight | 1.08 pounds (490 grams) |
| Dimensions | 6 x 1 x 9.25 inches (15.2 x 2.5 x 23.5 cm) |
Who Should Buy?
-
Computer Science Students
Ideal for students studying algorithms or optimization, enhancing their understanding of approximation methods and semidefinite programming.
-
Research Professionals
Perfect for researchers focused on computational complexity, providing insights into advanced algorithmic strategies and theory.
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Mathematics Enthusiasts
Great for mathematics enthusiasts interested in applied mathematics, exploring theoretical concepts in algorithm development and optimization.
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Beginner Programmers
Not suitable for beginners lacking foundational knowledge of algorithms, as the content may be too advanced and challenging.
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Pros
- Clear explanations of algorithms
- Comprehensive coverage of topics
- Well-structured and organized
- Useful examples and exercises
- Strong insights into semidefinite programming
Cons
- A few minor typographical errors.
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BDT 12617
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Features & Benefits
- Introduces semidefinite programming and its applications in approximation algorithms.
- Covers both basic and advanced topics in the field.
- Highlights the efficiency of solving optimization problems.
- Discusses key algorithms for problems like MAXCUT.
- Explores the significance of the Unique Games Conjecture.
- Provides insights into the mathematical principles behind approximation algorithms.
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