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LASPack (version 1.12.2) LASPack is a package for solving large sparse systems of linear equations like those which arise from discretization of partial differential equations. Main features: - The primary aim of LASPack is the implementation of efficient iterative methods for the solution of systems of linear equations. - Beside the obligatory Jacobi, succesive over-relaxation, Chebyshev, and conjugate gradient solvers, LASPack contains selected state-of-the-art algorithms which are commonly used for large sparse systems: - CG-like methods for non-symmetric systems: CGN, GMRES, BiCG, QMR, CGS, and BiCGStab, - multilevel methods such as multigrid and conjugate gradient method preconditioned by multigrid and BPX preconditioners. A complete description of the package (including the installation procedure) you may find in LASPack Reference Manual:

libflame contains implementations of many dense linear algebra operations that are provided by the BLAS and LAPACK libraries. (However, not all FLAME implementations support every datatype, and, in many cases, libflame uses a different naming convention for the routines.) The library is a product of the Formal Linear Algebra Methods Environment (FLAME), which encompasses a new notation for expressing algorithms, a methodology for systematic derivation of algorithms, Application Program Interfaces (APIs) for representing the algorithms in code, and tools for mechanical derivation, implementation and analysis of algorithms and implementations.

Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.

libocas implements an Optimized Cutting Plane Algorithm (OCAS) for training linear SVM classifiers from large-scale data. The computational effort of OCAS scales with O(m log m) where m is the sample size. In an extensive empirical evaluation, OCAS significantly outperforms current state-of-the-art SVM solvers. libocas also implements the COFFIN framework for efficient training of translation invariant image classifiers from virtual examples.

libranlip is a C++ library created by G. Beliakov, which generates random variates with arbitrary Lipschitz-continuous densities via the acceptance / rejection method. The density should have a dimension of no more than about five. The user needs to supply the density function using a simple syntax, and then call the methods of construction and generation provided in libranlip.

libtsnnls is a fast solver for least-squares problems in the form Ax = b under the constraint that all entries in the solution vector x are non-negative.

ltl2ba implements an algorithm of P. Gastin and D. Oddoux to generate Buechi automata from linear temporal logic (LTL) formulae. This algorithm generates a very weak alternating automaton and then transforms it into a Buechi automaton, using a generalized Buechi automaton as an intermediate step. Each automaton is simplified on-the-fly in order to save memory and time. As usual the LTL formula is simplified before any treatment. ltl2ba is more efficient than Spin 3.4.1, with regard to the size of the resulting automaton, the time of the computation, and the memory used.

A comprehensive package for structural multivariate function estimation using smoothing splines.

basecalc came with Xlib Programming Manual from O'Reilly as an example of X lib programming. mbasecalc is an immitation of basecalc which is available on different platforms.

METIS is a set of serial programs for partitioning graphs, partitioning finite element meshes, and producing fill-reducing orderings for sparse matrices. The algorithms implemented in METIS are based on the multilevel recursive-bisection, multilevel k-way, and multi-constraint partitioning schemes developed in our lab. METIS provides high-quality partitions, is extremely fast, and produces low-fill orderings.