This example shows how to use variable-precision arithmetic to obtain high precision using Symbolic Math Toolbox™.

Search for formulas that represent near-integers. A classic example is the following: compute to 30 digits. The result appears to be an integer that is displayed with a rounding error.

digits(30); f = exp(sqrt(sym(163))*sym(pi)); vpa(f)

ans = 262537412640768743.999999999999

Compute the same value to 40 digits. It turns out that this is not an integer.

digits(40); vpa(f)

ans = 262537412640768743.9999999999992500725972

Investigate this phenomenon further. Below, numbers up to occur, and the investigation needs some correct digits after the decimal point. Compute the required working precision:

d = log10(exp(vpa(1000)))

d = 434.2944819032518276511289189166050822944

Set the required precision before the first call to a function that depends on it. Among others, `round`

, `vpa`

, and `double`

are such functions.

digits(ceil(d) + 50);

Look for similar examples of the form . Of course, you can obtain more such numbers n by multiplying 163 by a square. But apart from that, many more numbers of this form are close to some integer. You can see this from a histogram plot of their fractional parts:

A = exp(pi*sqrt(vpa(1:1000))); B = A-round(A); histogram(double(B), 50)

Calculate if there are near-integers of the form .

A = exp(vpa(1:1000)); B = A-round(A); find(abs(B) < 1/1000)

ans = Empty matrix: 1-by-0

It turns out that this time the fractional parts of the elements of `A`

are rather evenly distributed.

histogram(double(B), 50)

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