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Item #:
31471949
Algorithms for Convex Optimization
Item #:
31471949
SEK 685
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Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems.
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What Stands Out
Efficient Algorithms
Offers cutting-edge algorithms specifically designed for convex optimization, ensuring faster convergence and improved performance for complex mathematical problems that require efficient solutions.
Comprehensive Theories
Incorporates thorough theoretical foundations and proofs, allowing users to deeply understand concepts and apply them effectively in real-world applications across various industries.
User-Friendly Approach
Presents complex optimization techniques in an accessible way, making it suitable for both beginners and experts eager to enhance their skills in data science, finance, and machine learning.
Product Details
| Publisher | Cambridge University Press |
| Publication date | October 7, 2021 |
| Edition | 1st |
| Language | English |
| Print length | 340 pages |
| ISBN-10 | 1108741770 |
| ISBN-13 | 978-1108741774 |
| Item Weight | 1.15 pounds (520 grams) |
| Dimensions | 6 x 0.85 x 9 inches (15.2 x 2.2 x 22.9 cm) |
| Country of Origin | This item will be imported from US |
| What is in the box | Algorithms for Convex Optimization... For more details, please check description/product details |
Product Description
Algorithms for Convex Optimization
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Pros
- Comprehensive coverage of topics
- Clear explanations and examples
- Helpful for both beginners and experts
- Well-structured and organized
- Includes practical applications
Cons
- Some advanced content may be complex.
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SEK 685
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Features & Benefits
- Revolutionary insights into algorithm design for optimization.
- Covers key methods: gradient descent, mirror descent, and more.
- Self-contained guide for researchers and professionals.
- Derives algorithms from first principles for better understanding.
- Establishes precise running time bounds for algorithms.
- Explains advancements in discrete optimization and convex optimization fields.
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