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Just like the title says. Is this due to roundoff?
The discussion centers on the discrepancies in integral results when using decimal versus fractional representations in Mathematica. It highlights that floating-point numbers cannot always accurately represent certain values, leading to computational errors. For instance, expressions like 3.0*sin(x)*(1.0/3.0) may yield different results than (3.0*sin(x)) / 3.0 due to Mathematica's handling of approximate versus exact numbers. The conversation emphasizes the importance of understanding floating-point arithmetic to avoid such issues.
NIntegrate function and its applicationsMathematicians, computer scientists, and software developers who utilize Mathematica for numerical computations and seek to understand the implications of floating-point arithmetic on their results.
jedishrfu said:Can't you illustrate it with a simpler example?
Its well known in computerdom that some numbers can't be represented properly in floating pt format so if Mathematica does a numerical computation with them them then computational error will creep into the calculation.
As an example, if you had some expression like ##3.0*sin(x)*(1.0/3.0)## and Mathematica symbolically reduces it to sin(x) then that result might be different from ##(3.0*sin(x)) / 3.0## where Mathematica didn't see the ONES identity and did the numerical computations of 3.0 * sin(x) then dividing by 3.0.
For larger numbers the result difference might be more pronounced as floating pt numbers kep to a certain limited digit precision.