Hello,
I am the creator of the Mantissa library
(http://www.spaceroots.org/software/mantissa/index.html), a Java library
providing some mathematical algorithms.
Mantissa provides several algorithms that could be useful for
Commons-Math. I think the objectives of the two libraries are quite
similar, but I don't know (yet) the point were you consider "commons
problems" addressed by Commons-Math end and where problems are
considered too specific to be provided here.
I would be very glad to donate parts of Mantissa code to Commons-Math if
you wish so. Mantissa is released under a revised-BSD type license, but
I am quite happy with Apache license too and ready to change.
I don't think everything in Mantissa is useful for Commons-Math. IMHO,
the most interesting parts are :
- the estimation package
Gauss-Newton estimator (based on LU decomposition),
Levenberg-Marquardt estimator (based on QR decomposition)
- the fitting package (curve fitting, using the estimation package)
- the Ordinary Differential Equations package
this is clearly THE best package in Mantissa, with several
state of the art integrators with fixed steps, variable stepsize
(including Dormand-Prince 8 (5,3) and Gragg-Bulirsch-Stoer),
all of them supporting continuous output and multiple switching
functions (can be used for G-stop, but not limited to that), well
tested and used
- the roots package
provides a Brent algorithm when the derivatives are not available
Some package that may be interesting are :
- the geometry package
mainly for the 3D rotations implementations using quaternions
internally and axes/angles, Cardan angles, Euler angles, matrices,
single or double vectors pairs and quaternions in the interface
- the functions package
providing notions like computable or sampled functions
- the quadrature package
(Riemann, trapezoid, enhanced Simpson, Gauss-Legendre up
to 5 points, easily extended)
- the utilities package
for the array mapping paradigm
Some package that are probably not interesting are :
- the algebra package
orthogonal polynomials, inefficient and awkward implementation
- the raw linear algebra package
basic implementation developped for speed ... not sure the goal was
achieved and using only straightforward algorithms, not state of the art
- the random number generators
supports correlated vectors generation
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