Ports Search

SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.

Qalculate! is a multi-purpose desktop calculator. It is small and simple to use but with much power and versatility underneath. Features include customizable functions, units, arbitrary precision, plotting, and a user-friendly interface.

qrupdate is a Fortran library for fast updates of QR and Cholesky decomposition. It was originally part of GNU-Octave.

This port contains data and baseline images for VTK regression testing and other VTK examples. The Data directory are data files of various types. This includes polygonal data, images, volumes, structured grids, rectilinear grids, and multi-variate data. The Baseline are the testing images. These are used in testing to compare a valid image against a generated image. If a difference between the two images is found, then the test is considered to have failed.

Clasp is an answer set solver for (extended) normal logic programs. It combines the high-level modeling capacities of answer set programming (ASP) with state-of-the-art techniques from the area of Boolean constraint solving. The primary clasp algorithm relies on conflict-driven nogood learning, a technique that proved very successful for satisfiability checking (SAT). Unlike other learning ASP solvers, clasp does not rely on legacy software, such as a SAT solver or any other existing ASP solver. Rather, clasp has been genuinely developed for answer set solving based on conflict-driven nogood learning. clasp can be applied as an ASP solver (on SMODELS format, as output by Gringo), as a SAT solver (on a simplified version of DIMACS/CNF format), or as a PB solver (on OPB format).

This is the Worldforge math library. The primary focus of WFMath is geometric objects. Thus, it includes several shapes (boxes, balls, lines), in addition to the basic math objects that are used to build these shapes (points, vectors, matricies).

Sage is a free open-source mathematics software system licensed under the GPL. It combines the power of many existing open-source packages into a common Python-based interface. Mission: Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab. For instructions on adding optional packages, see files/optional-packages.txt. You may want to avoid the command "make install" and instead simply use the bin/mv command to move the ${WRKSRC} directory to where-ever you want it.

Scilab is a scientific software package for numerical computations in a user-friendly environment. Main features * Hundreds of mathematical functions * High level programming language * 2-D and 3-D graphics * Advanced data structures and user defined data types * Xcos: hybrid dynamic systems modeler and simulator 2-D and 3-D visualization * Lines * Pie charts * Histograms * Surfaces * Animations * Graphics export in many formats: GIF, BMP, JPEG, SVG, PDF... Numerical computation * Linear algebra * Sparse matrices * Polynomials and rational functions * Simulation: explicit and implicit systems of differential equations solvers * Classic and robust control * Differentiable and non-differentiable optimization Data analysis * Interpolation, approximation * Signal Processing * Statistics Extended features * Graphs and Networks * Interface with Fortran, C, C++, Java * Functions for calling Scilab from C, C++, Fortran and Java * LabVIEW Gateway * A large number of modules available via ATOMS

The SDPA (SemiDefinite Programming Algorithm) is a software package for solving semidefinite program (SDP). It is based on a Mehrotra-type predictor-corrector infeasible primal-dual interior-point method. The SDPA handles the standard form SDP and its dual. It is implemented in C++ language utilizing the LAPACK for matrix computation. The SDPA incorporates dynamic memory allocation and deallocation. So, the maximum size of an SDP to be solved depends on the size of memory which users' computers install. The SDPA enjoys the following features: 1. Callable library of the SDPA is available. 2. Efficient method for computing the search directions when an SDP to be solved is large scale and sparse. 3. Block diagonal matrix structure and sparse matrix structure in data matrices are available. 4. Some information on infeasibility of a semidefinite program to be solved is provided.

The SDPARA (SemiDefinite Programming Algorithm PARAllel version) is a parallel version of the SDPA. C++ source codes of the SDPARA are available in this homepage. They form a stand-alone software package for solving SDPs in parallel with the help of MPI (Message Passing Interface) and ScaLAPACK (Scalable LAPACK). However callable libraries of the SDPARA, which could be used combinedly with other C++ programs, are not available. We assume that you know how to use the latest version of the SDPA and MPICH.