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Expansions and techiques of
integration
Authors: Bill Davis, Horacio Porta
and Jerry Uhl < STRONG>©1999
Producer: Bruce
Carpenter
Publisher:
Distr
ibutor:
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Remarkable plots explained by order of contact. Splining for smoothness at the knots.
Experiments geared at discovering that the smoother the transition from one curve to another at a knot, the better both curves approximate each other near the knot. Splining functions and polynomials. Splines in road design. Landing an airplane. &nbs p;The natural cubic spline. Order of contact for derivatives and integrals.
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The expansion of a function
in powers of
as a file of
polynomials with higher and higher orders of contact with
at
. The expansions every literate calculus person
knows:
,
,
, and
.
Converting known expansions
to others via change of variable.
Expansions for approximations.
Experiments geared toward discovering that using more and more
of the expansion results in better and better
approximation. &nbs p;Halley's way of calculating
accurate decimals of
. Expansions by substitution. Expansions by
differentiation. ;Expansions by
integration. Recognition
of expansions. Expansions that satisfy a priori
error bounds.
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The expansion of a function
in powers of
as a file of polynomials
with higher and higher orders of contact with
at
. Netwon's
method. Multiplying and di viding expansions. Using
expansions to help calculate limits at a point. Expansions and
the complex exponential function.& nbsp; Using expansions to help
to get precise estimates of some integrals.
Centering expansions for good approximation. Newton's
method for root finding. Successes and failures of Newton's
method.& nbsp; Using the complex exponential to generate
trigonometric identities. Comparing reflecting properties of
spherical mirrors and the reflecting properties of parabolic
mirrors. Using expansions to see why spherical mirrors have
limited ability to concentrate light rays. Behavior of
expansions very close to
. Behavior of expansions far away from
.
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Taylor's formula for expansions in powers of
.
Euler, Midpoint and Runge-Kutta approximations of
given
. Experiments comparing
the quality of Midpoint and Runge-Kutta approximations. A
daption of Euler, Midpoint and Runge-Kutta approximations to approximating
the plots of the differential equation
, with
given. Taylor's formula in
rever se. L'Hospital's rule by dividing the leading term of the expansion
of the denominator into the leading term of the expansion of the
numerator. Centering the expansion for best
approximation. Experiments comparing the derivative of the
expansion and the expansion of ;the derivative.
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Barriers and complex singularities. The convergence interval
of an expansion as the interval between the barriers. Why som e
functions such as
have convergence barriers and others such as
and
do not. Why functions such
as
and
do
not have expansions in powers of
but do have expansions in powers of
for
. Why the convergence in tervals for
,
and
Shortcuts based on the expansion of
in powers of
. Using the
expansion of
in
powers of
for
drug dosing. Infinite sums of numbers resulting from
expansions. Barriers resulting from splines. Infinite sums and
decimals. Experiments relating expansions in powers of
to
interpolating polynomials. Rung e's disaster.
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Functions defined by a power series. Functions defined by
power series via differential equations. The power series
conver gence principle, which says that if for some positive number
the
infinite list
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is bounded , then the power
series ![]()
converges for
.
Experiments in trying to plot functions defined by power
series. Experiments in plotting a function defined by a power
series via a&n bsp; differential equation versus plotting the
same function directly through Mathematica's numerical
differential equati on solver. The ratio test for power series
as a consequence of the power series convergence
principle.
The functions ,
and
from the viewpoint of power series.
Experiments in truncation of power series. The Airy function as
a function defined by a power series.
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Using the chain rule and the fundamental formula to see why
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and using this fact to transform
one integral into another. Measuring area under curves given
parametrically. Bell shaped curves and Gauss's normal probab
ility law; mean and standard deviation.
Study of the error function,
. Using tranformations to explain
Mathematica output. Polar plots and area measurements. Using
transformations to expla in the meaning of standard deviation in Gauss's
normal law. Expected life of light bulbs and how long to set the guarantee
on them. Using Gauss' s normal law to help to program coin-operated coffee
machines. IQ test results. Using Gauss's normal law to organize SAT scores
into quartiles a nd deciles. Comparison of 1967 and 1987 SAT scores.
"Grading on the curve."
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Meaning of the plot of
. The 2D integral
as a volume measurement via slicing and
acculumulating. Gauss-Green formula (Green's theorem) as a way
of calculating a double integral numerically as a single integral.
Volume and area measurements with 2D integrals. Area and volume measurements via the Gauss-Green formula. Average value and centroids. &nbs p;Calculation strategies. Plotting and measuring. Gauss's normal law in 2D and using it, as done in the Pentagon, to decide how many bombs to dr op on a target.
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Separating the variables and integrating to get formulas for the
solutions of some differential equations. Integration by
parts. Comp lex numbers and the complex exponential
.
Formulas for the solutions of the differential equations involved in the chemical model and the spread of infection model. Hyperbolic functions and their relation to trigonometric functions. Using the complex exponential to help to understand the Mathematica output from the Solve instruction. Gamma function. Integration by parts and integration by iteration. Error propagation in forward iteration. Error reducti on by backwards iteration.
The nasty quotient
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simplifies
to
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So the nasty quotient is easy to
integrate.
Undetermined coefficients. Complex numbers and partial fractions. Wild card substitutions with the help of a trigonometric, hyperboli c or ad hoc function. Integration by parts.
Not much, although the experience gained from trying the method of undetermined coefficients is good experience in setting up and solving system s of linear equations.
Copyright © 2006 Calculus & Mathematica at UIUC
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