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.
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,
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.