Product details:
| ISBN13: | 9781009166171 |
| ISBN10: | 1009166174 |
| Binding: | Hardback |
| No. of pages: | 400 pages |
| Size: | 257x181x23 mm |
| Weight: | 890 g |
| Language: | English |
| 687 |
Category:
An Introduction to Optimization on Smooth Manifolds
Publisher: Cambridge University Press
Date of Publication: 16 March 2023
Normal price:
Publisher's listprice:
GBP 100.00
GBP 100.00
Your price:
43 470 (41 400 HUF + 5% VAT )
discount is: 10% (approx 4 830 HUF off)
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
Click here to subscribe.
Availability:
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
Can't you provide more accurate information?
Not in stock at Prospero.
Short description:
An invitation to optimization with Riemannian geometry for applied mathematics, computer science and engineering students and researchers.
Long description:
Optimization on Riemannian manifolds-the result of smooth geometry and optimization merging into one elegant modern framework-spans many areas of science and engineering, including machine learning, computer vision, signal processing, dynamical systems and scientific computing. This text introduces the differential geometry and Riemannian geometry concepts that will help students and researchers in applied mathematics, computer science and engineering gain a firm mathematical grounding to use these tools confidently in their research. Its charts-last approach will prove more intuitive from an optimizer's viewpoint, and all definitions and theorems are motivated to build time-tested optimization algorithms. Starting from first principles, the text goes on to cover current research on topics including worst-case complexity and geodesic convexity. Readers will appreciate the tricks of the trade for conducting research and for numerical implementations sprinkled throughout the book.
'With its inviting embedded-first progression and its many examples and exercises, this book constitutes an excellent companion to the literature on Riemannian optimization - from the early developments in the late 20th century to topics that have gained prominence since the 2008 book 'Optimization Algorithms on Matrix Manifolds', and related software, such as Manopt/Pymanopt/Manopt.jl.' P.-A. Absil, University of Louvain
'With its inviting embedded-first progression and its many examples and exercises, this book constitutes an excellent companion to the literature on Riemannian optimization - from the early developments in the late 20th century to topics that have gained prominence since the 2008 book 'Optimization Algorithms on Matrix Manifolds', and related software, such as Manopt/Pymanopt/Manopt.jl.' P.-A. Absil, University of Louvain
Table of Contents:
Notation; 1. Introduction; 2. Simple examples; 3. Embedded geometry: first order; 4. First-order optimization algorithms; 5. Embedded geometry: second order; 6. Second-order optimization algorithms; 7. Embedded submanifolds: examples; 8. General manifolds; 9. Quotient manifolds; 10. Additional tools; 11. Geodesic convexity; References; Index.
