Bookshock Ask Tez ✨
Scientific Computing Vol. II - Eigenvalues and Optimization cover

Scientific Computing Vol. II - Eigenvalues and Optimization

by John A. Trangenstein

Lowest price on Bookshock
$86.14
1 offer
In stock

Ask Tez about this book →

This title is temporarily out of stock. Email support@bookshock.ai or call (972) 638-0790 and we'll let you know when it's back.
Free US shipping
30-day free returns
Stripe-secured checkout

All offers (1)

PriceConditionSeller
$86.14Best price New Basi6 International LLC

Stock and pricing refresh on page load. Tez can also compare prices on Amazon, AbeBooks, and ThriftBooks if you ask.

About this book

<p>This is the second of three volumes providing a comprehensive presentation of the fundamentals of scientific computing. This volume discusses more advanced topics than volume one, and is largely not a prerequisite for volume three. This book and its companions show how to determine the quality of computational results, and how to measure the relative efficiency of competing methods. Readers learn how to determine the maximum attainable accuracy of algorithms, and how to select the best method for computing problems. This book also discusses programming in several languages, including C++, Fortran and MATLAB. There are 49 examples, 110 exercises, 66 algorithms, 24 interactive JavaScript programs, 77 references to software programs and 1 case study.</p> <p>Topics are introduced with goals, literature references and links to public software. There are descriptions of the current algorithms in LAPACK, GSLIB and MATLAB.<br></p><p></p> <p>This book could be used for a second course innumerical methods, for either upper level undergraduates or first year graduate students. Parts of the text could be used for specialized courses, such as nonlinear optimization or iterative linear algebra.<br></p><p></p><p></p><p></p>

Details

Format
Hardcover
Pages
600
Publisher
Springer International Publishing
Language
EN
Edition
1st ed. 2017
ISBN-13
9783319691060
ISBN-10
3319691066

Categories

Mathematics, Numerical Analysis, Differential Equations, Optimization