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Item #:
181922042
The Design of Approximation Algorithms
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Item #:
181922042
IMP 76
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This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions.
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What Stands Out
Comprehensive Coverage
This edition provides an extensive overview of approximation algorithms, catering to both beginners and experts who want to deepen their understanding of algorithm design and analysis.
Practical Applications
It highlights real-world applications of approximation algorithms, making the content relatable and applicable for students and professionals in computer science and operations research.
Clear Explanations
With clear, concise explanations and numerous examples, this book demystifies complex concepts, ensuring readers can effectively grasp and implement approximation algorithms.
Product Details
| Publisher | Cambridge University Press |
| Publication date | April 26, 2011 |
| Edition | 1st |
| Language | English |
| Print length | 518 pages |
| ISBN-10 | 0521195276 |
| ISBN-13 | 978-0521195270 |
| Item Weight | 2.47 pounds (1.12 kg) |
| Dimensions | 7.5 x 1.25 x 10.5 inches (19.1 x 3.2 x 26.7 cm) |
Who Should Buy?
-
Computer Science Students
Ideal for students pursuing algorithms or computer science degrees, enhancing their understanding of approximation algorithms.
-
Research Scholars
Beneficial for scholars conducting research in algorithm design, aiding in practical applications of theoretical concepts.
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Software Engineers
Useful for software engineers tackling complex problems, providing techniques to develop efficient algorithms.
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Casual Readers
Not suitable for casual readers due to its technical content and requirement of prior knowledge in algorithms.
Product Description
The Design of Approximation Algorithms
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Algorithms Editorial Review
The "Design of Approximation Algorithms" (1st Edition) is a comprehensive textbook that serves as an excellent resource for understanding modern concepts in discrete optimization. Reviewers have found the material to be accessible, with clear proofs and a structured approach that lends itself well to academic settings. This book has proven useful for courses, suggesting that it effectively engages students with its thorough coverage. The organization of content is notably praised, with each chapter targeting different classes of problems and presenting corresponding approximation algorithms, supplemented by practical examples that solidify understanding. The book is seen as a valuable reference for uncovering the nuances of primal and dual solutions within optimization problems. However, some readers have pointed out that the mathematical approach can feel daunting. While early chapters are more engaging, later sections may become overwhelming due to their "cold" mathematical formulation. This has led to mixed feelings among those looking for a more narrative-driven explanation rather than strict mathematical rigor. Overall, while the book is a formidable resource filled with detail and depth, some users may desire more illustrative examples to complement its rigorous mathematical content, particularly if they come from a more applied background rather than a pure mathematical one. **
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Pros
- Easy to follow material with clear proofs.
- Well organized, covering extensive topics in discrete optimization.
- Useful as a course textbook and reference.
- Engaging early chapters with practical examples.
Cons
- Later chapters may be daunting due to their mathematical focus.
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Features & Benefits
- Learn to design efficient approximation algorithms.
- Find provably near-optimal solutions for NP-hard problems.
- Covers essential algorithmic techniques like greedy, dynamic programming, and randomization.
- Structured for both graduate-level courses and researchers.
- Includes practical applications across various domains including operations research and computer science.
- Offers advanced methods for proving the complexity of optimization problems.
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