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SNNS (Stuttgart Neural Network Simulator) is a software simulator for neural networks on Unix workstations developed at the Institute for Parallel and Distributed High Performance Systems (IPVR) at the University of Stuttgart. The goal of the SNNS project is to create an efficient and flexible simulation environment for research on and application of neural nets. The SNNS simulator consists of two main components: 1) simulator kernel written in C 2) graphical user interface under X The simulator kernel operates on the internal network data structures of the neural nets and performs all operations of learning and recall. It can also be used without the other parts as a C program embedded in custom applications. It supports arbitrary network topologies and the concept of sites. SNNS can be extended by the user with user defined activation functions, output functions, site functions and learning procedures, which are written as simple C programs and linked to the simulator kernel. The graphical user interface XGUI (X Graphical User Interface), built on top of the kernel, gives a 2D and a 3D graphical representation of the neural networks and controls the kernel during the simulation run. In addition, the 2D user interface has an integrated network editor which can be used to directly create, manipulate and visualize neural nets in various ways.

Solitaire is an encryption system based on a deck of cards by Bruce Schneier. Although it is designed to be worked out by a human, it can work on computers. This is the reference implementation programmed in Perl. The program itself is installed as 'solitaire', and the source code and test vectors are installed in share/doc/solitaire. Please read the web site below before relying on this for real security.

This program demonstrates the working principles of some 20 sorting algorithms and is very easy to use. Select the algorithm, the speed of the visualisation and whether you want to get sound output (that reflects the values being moved and is characteristic for each kind of algorithm).

SPOOLES is a library for solving sparse real and complex linear systems of equations, written in the C language using object oriented design. At present, there is the following functionality: 1. Compute multiple minimum degree, generalized nested dissection and multisection orderings of matrices with symmetric structure. 2. Factor and solve square linear systems of equations with symmetric structure, with or without pivoting for stability. The factorization can be symmetric LDLT, Hermitian LDLH, or nonsymmetric LDU. A direct factorization or a drop tolerance factorization can be computed. The factors and solve can be done in serial mode, multithreaded with Solaris or POSIX threads, or with MPI. 3. Factor and solve overdetermined full rank systems of equations using a multifrontal QR factorization, in serial or using POSIX threads. 4. Solve square linear systems using a variety of Krylov iterative methods. The preconditioner is a drop tolerance factorization, with or without pivoting for stability.

STP is a constraint solver (also referred to as a decision procedure or automated prover) aimed at solving constraints generated by program analysis tools, theorem provers, automated bug finders, intelligent fuzzers and model checkers. STP has been used in many research projects at Stanford, Berkeley, MIT, CMU and other universities. It is also being used at many companies such as NVIDIA, some startup companies, and by certain government agencies. The input to STP are formulas over the theory of bit-vectors and arrays (This theory captures most expressions from languages like C/C++/Java and Verilog), and the output of STP is a single bit of information that indicates whether the formula is satisfiable or not. If the input is satisfiable, then it also generates a variable assignment to satisfy the input formula.

SuiteSparse is a set of sparse matrices libraries. It contains: * AMD: symmetric approximate minimum degree * BTF: permutation to block triangular form (beta) * CCOLAMD: constrained column approximate minimum degree * COLAMD: column approximate minimum degree * CHOLMOD: sparse supernodal Cholesky factorization and update/downdate * KLU: sparse LU factorization, for circuit simulation (beta) * LDL: a simple LDL^T factorization * UMFPACK: sparse multifrontal LU factorization * UFconfig: common configuration for all of the above * CSparse: a concise sparse matrix package * CXSparse: and extended version of CSparse

SuperLU is a general purpose library for the direct solution of large, sparse, nonsymmetric systems of linear equations on high performance machines. The library is written in C and is callable from either C or Fortran. The library routines will perform an LU decomposition with partial pivoting and triangular system solves through forward and back substitution. The LU factorization routines can handle non-square matrices but the triangular solves are performed only for square matrices. The matrix columns may be preordered (before factorization) either through library or user supplied routines. This preordering for sparsity is completely separate from the factorization. Working precision iterative refinement subroutines are provided for improved backward stability. Routines are also provided to equilibrate the system, estimate the condition number, calculate the relative backward error, and estimate error bounds for the refined solutions.

SuperLU_MT (version 2.0) ======================== SuperLU_MT contains a set of subroutines to solve a sparse linear system A*X=B. It uses Gaussian elimination with partial pivoting (GEPP). The columns of A may be preordered before factorization; the preordering for sparsity is completely separate from the factorization. SuperLU_MT is a parallel extension to the serial SuperLU library. SuperLU_MT is implemented in ANSI C, with multithreading extension, for example, using POSIX threads. Currently, only the LU factorization routine, which is the most time-consuming part of the solution process, is parallelized on machines with a shared address space. The other routines, such as column preordering and the forward and back substitutions are performed sequentially. This "alpha" release contains only double-precision real data type. Xiaoye S. Li, Lawrence Berkeley National Lab, xiaoye@nersc.gov James Demmel, Univ. of California Berkeley, demmel@cs.berkeley.edu John R. Gilbert, Xerox Palo Alto Research Center, gilbert@parc.xerox.com NOTE: This library has to be linked with BLAS or a thread safe replacement.

surf is a tool to visualize some real algebraic geometry: plane algebraic curves, algebraic surfaces and hyperplane sections of surfaces. surf is script driven and has (optionally) a nifty GUI using the Gtk widget set. The algorithms should be stable enough not to be confused by curve/surface singularities in codimension greater than one and the degree of the surface or curve. This has been achieved quite a bit. We have drawn curves of degree up to 30 and surfaces of degree up to 20 successfully. However, there are examples of curves/surfaces of lower degree where surf fails to produce perfect images. This happens especially if the equation of the curve/surface is not reduced. Best results are achieved using reduced equations. On the other hand, surf displays the Fermat-curves accurately for degree up to 98.

Tablix is a powerful free software kernel for solving general timetabling problems. It uses a coarse-grained parallel genetic algorithm in combination with other techniques to construct sensible timetables from XML formatted problem descriptions. Tablix can run on a single host as well as on a heterogeneous parallel virtual machine using PVM3. Tablix kernel supports a very wide range of timetabling problems, from high school timetabling to barge scheduling. A number of timetable constraints are already implemented in the default installation. Because of kernel's modular design it is easy to add custom timetable constraints and/or modify existing ones. Kernel modules are written in C. Extensive API documentation is available on the internet and in the source distribution.