FuncDesigner is a free and open source computer algebra system (CAS) written in Python + NumPy, allowing it to run on multiple platforms Mac OS X, Windows and Linux.
Here are some key features of "FuncDesigner":
· Enhances RAD abilities of Python language for developing scientific software, especially for numerical optimization and solving systems of linear/non-linear equations
· Perfectly stacks with NumPy arrays and other Python functions and code, thus you can connect parts of code written in C, Fortran, MATLAB etc and import/export data in formats of text, HDF5, xml/xls, weka arff, mtx, netcdf, MATLAB mat files etc (via numpy.io and scipy.io modules)
· Key feature of the framework is Automatic differentiation (AD) (not to be confused with Numerical differentiation via finite-differences derivatives approximation and symbolic differentiation provided by Maxima, SymPy etc).
Requirements:
· Python 2.5 or later
· NumPy
· DerApproximator
What`s New in This Release: [ read full changelog ]
OpenOpt:
· cplex has been connected
· New GLP solver interalg with guarantied precision (also can work in inexact mode)
· New solver amsg2p for medium-scaled NLP and NSP
FuncDesigner:
· Essential speedup for automatic differentiation when vector-variables are involved, for both dense and sparse cases
· Solving MINLP become available
· Add Uncertainty analysis
· Add Interval analysis
· Now you can solve systems of equations with automatic determination is the system linear or nonlinear (subjected to given set of free or fixed variables), see the doc entry for details
· FD Funcs min and max can work on lists of oofuns
· Bugfix for sparse SLE (system of linear equations), that slowed down computation time and demanded more memory
· New oofuns angle, cross
· Using OpenOpt result(oovars) is available, also, start points with oovars() now can be assigned easier
SpaceFuncs:
· Some bugfixes
DerApproximator:
· Adjusted with some changes in FuncDesigner
Backward incompatibilities:
· FD oovars() now returns ooarray inst...
