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 with guaranteed precision appears to be extremely effective in numerical integration (also with with guaranteed precision).

Here are some examples where interalg works perfectly while scipy.integrate solvers (based on Fortran routine QUADPACK) fail to solve the problems and lie about obtained residual.

See http://openopt.org/IP for more details.

Future plans on interalg: solving ODE (and maybe PDE) with guaranteed precision, adding discrete variables and general constraints, adding more funcs with overloaded interval arithmetics.

Regards, D.

asked 24 Jun '11, 16:20

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Dmitrey
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edited 07 Jul '12, 15:34

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fbahr ♦
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Asked: 24 Jun '11, 16:20

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Last updated: 07 Jul '12, 15:34

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