Hi all,
some ideas implemented in the solver interalg (INTERval ALGorithm)
that already turn out to be more effective than its competitors in
numerical optimization (benchmark) appears to be extremely effective
in numerical integration with guaranteed precision.
Here are some examples where interalg works perfectly while
scipy.integrate solvers fail to solve the problems and lie about
obtained residual:
* 1-D (vs scipy.integrate quad)
> * 2-D (vs scipy.integrate dblquad)
> * 3-D (vs scipy.integrate tplquad)
>
see http://openopt.org/IP for more details.
Regards, D.
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