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.