Eine Einführung in die Optimierung glatter Verteiler von Nicolas Boumal: Neu-

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An Introduction to Optimization on Smooth Manifolds by Nicolas Boumal: New
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Book Title
An Introduction to Optimization on Smooth Manifolds
Publication Date
2023-02-24
Pages
400
ISBN
1009166158
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
Cambridge University Press
ISBN-10
1009166158
ISBN-13
9781009166157
eBay Product ID (ePID)
8057250923

Product Key Features

Number of Pages
400 Pages
Publication Name
Introduction to Optimization on Smooth Manifolds
Language
English
Subject
General
Publication Year
2023
Type
Textbook
Subject Area
Mathematics
Author
Nicolas Boumal
Format
Trade Paperback

Dimensions

Item Height
0.8 in
Item Length
10 in
Item Width
7 in

Additional Product Features

LCCN
2022-032575
Dewey Edition
23
Reviews
'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, 'This new book by Nicolas Boumal focuses on optimization on manifolds, which appears naturally in many areas of data science. It successfully covers all important and required concepts in differential geometry with an intuitive and pedagogical approach which is adapted to readers with no prior exposure. Algorithms and analysis are then presented with the perfect mix of significance and mathematical depth. This is a must-read for all graduate students and researchers in data science.' Francis Bach, INRIA
TitleLeading
An
Illustrated
Yes
Dewey Decimal
516.07
Table Of Content
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.
Synopsis
This is a modern invitation to algorithms for optimization with geometry for researchers and advanced undergraduate and graduate students in applied mathematics, computer science and engineering. Readers will appreciate the approachable, yet proof-based, introduction to differential geometry, which is often restricted to pure mathematics curricula., 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.
LC Classification Number
QA613.B68 2023

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