604
IV.
Branches of Mathematics
Karp, R. M., and V. Ramachandran. 1990. Parallel algorithms
for sharedmemory machines. In
Handbook of Theoret
ical Computer Science
, volume A,
Algorithms and Com
plexity
, edited by J. van Leeuwen. Cambridge, MA: MIT
Press/Elsevier.
Kearns, M. J., and U. V. Vazirani. 1994.
An Introduction
to Computational Learning Theory
. Cambridge, MA: MIT
Press.
Kitaev, A., A. Shen, and M. Vyalyi. 2002.
Classical and Quan
tum Computation
. Providence, RI: American Mathematical
Society.
Kushilevitz, E., and N. Nisan. 1996.
Communication Com
plexity
. Cambridge: Cambridge University Press.
Ron, D. 2001. Property testing (a tutorial). In
Handbook on
Randomized Computing
, volume II. Dordrecht: Kluwer.
Shaltiel, R. 2002. Recent developments in explicit construc
tions of extractors.
Bulletin of the European Association
for Theoretical Computer Science
77:67–95.
Sipser, M. 1997.
Introduction to the Theory of Computation
.
Boston, MA: PWS.
Strassen, V. 1990: Algebraic complexity theory. In
Hand
book of Theoretical Computer Science
, volume A,
Algo
rithms and Complexity
, edited by J. van Leeuwen. Cam
bridge, MA: MIT Press/Elsevier.
IV.21
Numerical Analysis
Lloyd N. Trefethen
1
The Need for Numerical Computation
Everyone knows that when scientists and engineers
need numerical answers to mathematical problems,
they turn to computers. Nevertheless, there is a wide
spread misconception about this process.
The power of numbers has been extraordinary. It is
often noted that the scientiFc revolution was set in
motion when Galileo and others made it a principle
that everything must be measured. Numerical measure
ments led to physical laws expressed mathematically,
and, in the remarkable cycle whose fruits are all around
us, Fner measurements led to reFned laws, which in
turn led to better technology and still Fner measure
ments. The day has long since passed when an advance
in the physical sciences could be achieved, or a signiF
cant engineering product developed, without numerical
mathematics.
Computers certainly play a part in this story, yet
there is a misunderstanding about what their role is.
Many people imagine that scientists and mathemati
cians generate formulas, and then, by inserting num
bers into these formulas, computers grind out the nec
essary results. The reality is nothing like this. What
really goes on is a far more interesting process of exe
cution of
algorithms
. In most cases the job could not
be done even in principle by formulas, for most mathe
matical problems cannot be solved by a Fnite sequence
of elementary operations. What happens instead is
that fast algorithms quickly converge to “approximate”
answers that are accurate to three or ten digits of pre
cision, or a hundred. ±or a scientiFc or engineering
application, such an answer may be as good as exact.
We can illustrate the complexities of exact versus
approximate solutions by an elementary example. Sup
pose we have one polynomial of degree 4,
p(z)
=
c
0
+
c
1
z
+
c
2
z
2
+
c
3
z
3
+
c
4
z
4
,
and another of degree 5,
q(z)
=
d
0
+
d
1
z
+
d
2
z
2
+
d
3
z
3
+
d
4
z
4
+
d
5
z
5
.
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 Spring '11
 Friedman
 Numerical Analysis, The Land, PDEs

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