Optimization Algorithms On Matrix Manifolds(1st Edition)

Authors:

P A Absil ,Robert Mahony ,Rodolphe Sepulchre

Type:Hardcover/ PaperBack / Loose Leaf
Condition: Used/New

In Stock: 2 Left

Shipment time

Expected shipping within 2 - 3 Days
Access to 35 Million+ Textbooks solutions Free
Ask Unlimited Questions from expert AI-Powered Answers 30 Min Free Tutoring Session
7 days-trial

Total Price:

$0

List Price: $77.31 Savings: $77.31 (100%)
Access to 30 Million+ solutions
Ask 50 Questions from expert AI-Powered Answers 24/7 Tutor Help Detailed solutions for Optimization Algorithms On Matrix Manifolds

Price:

$9.99

/month

Book details

ISBN: 0691132984, 978-0691132983

Book publisher: Princeton University Press

Book Price $0 : Many Problems In The Sciences And Engineering Can Be Rephrased As Optimization Problems On Matrix Search Spaces Endowed With A So-called Manifold Structure. This Book Shows How To Exploit The Special Structure Of Such Problems To Develop Efficient Numerical Algorithms. It Places Careful Emphasis On Both The Numerical Formulation Of The Algorithm And Its Differential Geometric Abstraction--illustrating How Good Algorithms Draw Equally From The Insights Of Differential Geometry, Optimization, And Numerical Analysis. Two More Theoretical Chapters Provide Readers With The Background In Differential Geometry Necessary To Algorithmic Development. In The Other Chapters, Several Well-known Optimization Methods Such As Steepest Descent And Conjugate Gradients Are Generalized To Abstract Manifolds. The Book Provides A Generic Development Of Each Of These Methods, Building Upon The Material Of The Geometric Chapters. It Then Guides Readers Through The Calculations That Turn These Geometrically Formulated Methods Into Concrete Numerical Algorithms. The State-of-the-art Algorithms Given As Examples Are Competitive With The Best Existing Algorithms For A Selection Of Eigenspace Problems In Numerical Linear Algebra. Optimization Algorithms On Matrix Manifolds Offers Techniques With Broad Applications In Linear Algebra, Signal Processing, Data Mining, Computer Vision, And Statistical Analysis. It Can Serve As A Graduate-level Textbook And Will Be Of Interest To Applied Mathematicians, Engineers, And Computer Scientists.