HP 33s hp 33s_user's manual_English_E_HDPM20PIE56.pdf - Page 341

take into account the presence of the most rapid

Page 341 highlights

f (x) x With this number of sample points, the algorithm will calculate the same approximation for the integral of any of the functions shown. The actual integrals of the functions shown with solid blue and black lines are about the same, so the approximation will be fairly accurate if f(x) is one of these functions. However, the actual integral of the function shown with a dashed line is quite different from those of the others, so the current approximation will be rather inaccurate if f(x) is this function. The algorithm comes to know the general behavior of the function by sampling the function at more and more points. If a fluctuation of the function in one region is not unlike the behavior over the rest of the interval of integration, at some iteration the algorithm will likely detect the fluctuation. When this happens, the number of sample points is increased until successive iterations yield approximations that take into account the presence of the most rapid, but characteristic, fluctuations. For example, consider the approximation of ³ ∞ xe −xdx. 0 Since you're evaluating this integral numerically, you might think that you should represent the upper limit of integration as 10499, which is virtually the largest number you can key into the calculator. Try it and see what happens. Enter the function f(x) = xe-x. More about Integration E-3

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More about Integration
Eā€“3
f (x)
x
With this number of sample points, the algorithm will calculate the same
approximation for the integral of any of the functions shown. The actual integrals
of the functions shown with solid blue and black lines are about the same, so the
approximation will be fairly accurate if
f(x)
is one of these functions. However, the
actual integral of the function shown with a dashed line is quite different from those
of the others, so the current approximation will be rather inaccurate if
f(x)
is this
function.
The algorithm comes to know the general behavior of the function by sampling the
function at more and more points. If a fluctuation of the function in one region is
not unlike the behavior over the rest of the interval of integration, at some iteration
the algorithm will likely detect the fluctuation. When this happens, the number of
sample points is increased until successive iterations yield approximations that
take into account the presence of the most rapid,
but characteristic
, fluctuations.
For example, consider the approximation of
.
0
āˆž
āˆ’
dx
xe
x
Since you're evaluating this integral numerically, you might think that you should
represent the upper limit of integration as 10
499
, which is virtually the largest
number you can key into the calculator.
Try it and see what happens. Enter the function
f(x)
= xe
ā€“
x
.