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Jean H Gallier Linear Algebra And Optimization With Applications To (Hardback)
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Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- Book Title
- Linear Algebra And Optimization With Applications To Machine Lear
- Title
- Linear Algebra And Optimization With Applications To Machine Lear
- EAN
- 9789811216565
- ISBN
- 9789811216565
- Country/Region of Manufacture
- SG
- Genre
- Science Nature & Math
- Topic
- Computing & Internet
- Release Date
- 05/12/2020
- Release Year
- 2020
About this product
Product Identifiers
Publisher
World Industries Scientific Publishing Co Pte LTD
ISBN-10
9811216568
ISBN-13
9789811216565
eBay Product ID (ePID)
23038779781
Product Key Features
Number of Pages
895 Pages
Publication Name
Linear Algebra and Optimization with Applications to Machine Learning - Volume II: Fundamentals of Optimization Theory with Applications to Machine Learning
Language
English
Subject
Algebra / Linear, Computer Science, General, Optimization
Publication Year
2020
Type
Textbook
Subject Area
Mathematics, Computers
Format
Hardcover
Dimensions
Item Weight
0 Oz
Additional Product Features
Intended Audience
Trade
LCCN
2020-001727
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
512.5
Synopsis
Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
LC Classification Number
QA184.2.G35 2020
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Seller feedback (514,584)
- m***m (901)- Feedback left by buyer.Past monthVerified purchaseThanks for a smooth transaction with fast shipping of an as described item.
- a***a (2231)- Feedback left by buyer.Past monthVerified purchaseGreat book, condition just as described, too quality, prompt shipping, thanks!
- 3***o (2849)- Feedback left by buyer.Past monthVerified purchaseSorry!! I’ve been really busy lately!! Listening to the CD now!! It’s great!! 😊 Thanks for selling on EBay!! It’s where I shop the most!!