book advertisement

[David G. Hough at validgh]

I normally don't pass along ads of this sort but this seems to be an unusual
book, covering unusually topical issues and published in an unusual way.
I don't know when I'll get a chance to look at it, but perhaps somebody on this
list with a current interest will do so.

Article: 12347 of sci.math.num-analysis
From: mnashanis.synapse.net (M Nash)
Newsgroups: sci.math.num-analysis
Subject: new book on scientific computing
Date: 1 Jan 95 22:33:46 GMT
Organization: Synapse Internet [Gatineau, Quebec, Canada]

Re: Scientific Computing with PCs (by FTP)
From: John C. Nash (jcnashaaix1.uottawa.ca)
      Mary M. Nash (mnashanis.synapse.net)

Announcing the publication of  

         Scientific Computing with PCs

         by John C. Nash and Mary M. Nash

         Copyright (C) 1994 J C and M M Nash

Available by FTP from the experimental server

         MacNash.admin.uottawa.ca         

There is a $10 licence fee for printing this book. 

The table of contents, the preface and a sample chapter may be
downloaded and printed freely without violating the copyright. 
This sample file is named SCPCDOC.PS.

The complete 200-page book is in four PostScript files SCPC-1.PS, ... 
SCPC-4.PS.  For MS-DOS users, there is a ZIP file that reduces the
size of the transmission considerably.  The layout is designed to use
paper efficiently (a traditional layout took 300 pages).

The book is a condensation and overview of the extensive experience
of the authors with personal computers in scientific applications. 
The intent is to address "what to" and "why to" calculate rather than
"how to".

Some of the examples -- not published elsewhere -- are likely to be
of special interest to the numerical analysis community.  For
example, one chapter addresses the issue of comparing implementations
of numerical algorithms for efficiency.  The results -- spanning
almost two decades of testing -- provide a unique and possibly
surprising panorama of the difficulties of predicting program
performance on even the simplest of codes.  The example programs are
three variants of the well-known Cholesky decomposition.

Another chapter discusses the use of statistical graphics in
the comparison on program performance, this time using published 
results for the performance of optimization codes.