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Algorithms for Convex Optimization
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Algorithms for Convex Optimization
Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems.
Algorithms for Convex Optimization
物品 #: 31471949

Algorithms for Convex Optimization

物品 #: 31471949

HKD 489

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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.

產品詳情

Get your copy of Algorithms for Convex Optimization 1st Edition at Ubuy 香港. Browse our wide selection of books and enjoy fast, reliable global shipping.
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 OriginThis item will be imported from US
What is in the boxAlgorithms for Convex Optimization... For more details, please check description/product details

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Algorithms for Convex Optimization

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優點

  • Comprehensive coverage of topics
  • Clear explanations and examples
  • Helpful for both beginners and experts
  • Well-structured and organized
  • Includes practical applications

缺點

  • Some advanced content may be complex.

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HKD 489

現在訂購，約可在下列時間收貨： 星期二, 十月 13

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特色和優勢

  • 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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