Scientific Computing with Case Studies
SIAM Press , 2009
Learning through doing is the foundation of this book,
which allows readers to explore case studies as well
as expository material. The book provides a practical
guide to the numerical solution of linear and nonlinear
equations, differential equations, optimization problems,
and eigenvalue problems. It treats standard problems and
introduces important variants such as sparse systems,
differential-algebraic equations, constrained optimization,
Monte Carlo simulations, and parametric studies. Stability
and error analysis is emphasized, and the
algorithms are grounded in sound principles of software
design and in the understanding of machine arithmetic and
Nineteen case studies allow readers to become familiar
with mathematical modeling and algorithm design, motivated
by problems in physics, engineering, epidemiology,
chemistry, and biology.
If this is a textbook for your course, contact Elizabeth
Greenspan (greenspan at siam.org) to arrange for a 30% discount
for your students.
This website contains supplementary material for the book.
Some of the partial solutions are revisions of articles originally
printed in ``Computing in Science and Engineering,"
These are posted here with permission of the IEEE.
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