Project details for Armadillo library

Screenshot Armadillo library 5.500

by cu24gjf - September 5, 2015, 04:52:44 CET [ Project Homepage BibTeX Download ]

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Description:

Armadillo is a template C++ linear algebra library (matrix maths) aiming towards a good balance between speed and ease of use. The API is similar to MATLAB.

Provides efficient classes for vectors, matrices and cubes, as well as many functions which operate on the classes (eg. contiguous and non-contiguous submatrix views)

Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided via an optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL, AMD ACML, or OpenBLAS).

A delayed evaluation approach is employed (at compile time) to combine several operations into one and reduce (or eliminate) the need for temporaries. This is automatically accomplished through recursive templates and template meta-programming.

Useful for conversion of research code into production environments, or if C++ has been decided as the language of choice, due to speed and/or integration capabilities.

Distributed under a license that is useful in both open-source and commercial/proprietary contexts.

Primarily developed at NICTA (Australia) by Conrad Sanderson, with contributions from around the world.

Changes to previous version:
  • added null() for finding the orthonormal basis of null space
  • added find_unique() for finding indices of unique values
  • added diff() for calculating differences between consecutive elements
  • added cumprod() for calculating cumulative product
  • expanded interp1() to handle repeated locations
  • expanded unique() to handle complex numbers
  • expanded object constructors to handle size() based specification of dimensions
  • faster handling of submatrix rows
  • faster clamp()
  • faster flipud()
  • faster row-wise cumsum()
  • fixes for handling sparse matrices
  • fix for k-means clustering in gmm_diag class
BibTeX Entry: Download
Supported Operating Systems: Linux, Windows, Unix, Mac Os X
Data Formats: Ascii, Binary, Hdf, Csv
Tags: Matlab, Matrix Library, Atlas, Lapack, Linear Algebra, Templates
Archive: download here

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