Copyright 2008-2021 Conrad Sanderson (http://conradsanderson.id.au)
Copyright 2008-2016 National ICT Australia (NICTA)
Copyright 2017-2021 Data61 / CSIRO
Armadillo is a high quality C++ library for linear algebra and scientific computing, aiming towards a good balance between speed and ease of use.
It's useful for algorithm development directly in C++, and/or quick conversion of research code into production environments. It has high-level syntax and functionality which is deliberately similar to Matlab.
The library provides efficient classes for vectors, matrices and cubes, as well as 200+ associated functions covering essential and advanced functionality for data processing and manipulation of matrices.
Various matrix decompositions (eigen, SVD, QR, etc) are provided through integration with LAPACK, or one of its high performance drop-in replacements (eg. OpenBLAS, Intel MKL, Apple Accelerate framework, etc).
A sophisticated expression evaluator (via C++ template meta-programming) automatically combines several operations (at compile time) to increase speed and efficiency.
The library can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc.
Authors:
- Conrad Sanderson - http://conradsanderson.id.au
- Ryan Curtin - http://ratml.org
Please cite the following papers if you use Armadillo in your research and/or software.
Citations are useful for the continued development and maintenance of the library.
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Conrad Sanderson and Ryan Curtin.
Armadillo: a template-based C++ library for linear algebra.
Journal of Open Source Software, Vol. 1, pp. 26, 2016. -
Conrad Sanderson and Ryan Curtin.
A User-Friendly Hybrid Sparse Matrix Class in C++.
Lecture Notes in Computer Science (LNCS), Vol. 10931, pp. 422-430, 2018.
Armadillo can be used in both open-source and proprietary (closed-source) software.
Armadillo is licensed under the Apache License, Version 2.0 (the "License"). A copy of the License is included in the "LICENSE.txt" file.
Any software that incorporates or distributes Armadillo in source or binary form must include, in the documentation and/or other materials provided with the software, a readable copy of the attribution notices present in the "NOTICE.txt" file. See the License for details. The contents of the "NOTICE.txt" file are for informational purposes only and do not modify the License.
Armadillo 10.x requires a C++ compiler that supports at least the C++11 standard. Use Armadillo 9.900 if your compiler only supports the old C++98/C++03 standards.
The functionality of Armadillo is partly dependent on other libraries: LAPACK, BLAS (preferably OpenBLAS), ARPACK and SuperLU. LAPACK and BLAS are used for dense matrices, while ARPACK and SuperLU are used for sparse matrices.
Armadillo can work without the above libraries, but its functionality will be reduced. Basic functionality will be available (eg. matrix addition and multiplication), but operations like eigen decomposition or matrix inversion will not be. Matrix multiplication (mainly for big matrices) may not be as fast.
As Armadillo is a template library, we recommended that optimisation is enabled during compilation of programs that use Armadillo. For example, for GCC and Clang compilers use -O2 or -O3
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Step 1: Ensure a C++ compiler is installed on your system.
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On macOS systems install Xcode (version 8 or later) and then type the following command in a terminal window:
xcode-select --install
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Step 2: Ensure the CMake tool is installed on your system.
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Cmake can be downloaded from http://www.cmake.org or (preferably) installed using the package manager on your system.
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On Linux-based systems, CMake can be installed using dnf, yum, apt, aptitude, ...
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On macOS systems, CMake can be installed through MacPorts or Homebrew.
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Step 3: Ensure that OpenBLAS (or standard BLAS and LAPACK) is installed on your system. On macOS, the Accelerate framework can be used for BLAS/LAPACK.
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On macOS, optionally install OpenBLAS for better performance.
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If support for sparse matrices is required, also install ARPACK and SuperLU. Caveat: only SuperLU version 5.2 can be used!
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On Linux-based systems, the following libraries are recommended to be present: OpenBLAS, LAPACK, SuperLU and ARPACK. It is also necessary to install the corresponding development files for each library. For example, when installing the "libopenblas" package, also install the "libopenblas-dev" package.
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Step 4: Run the cmake installer.
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Open a terminal window and change into the directory that was created by unpacking the armadillo archive.
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The simplest case is to run cmake using:
cmake .
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NOTE: the full stop separated from "cmake" by a space is important.
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Options to the cmake installer:
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On macOS, to enable the detection of OpenBLAS, use the additional ALLOW_OPENBLAS_MACOS option when running cmake:
cmake -DALLOW_OPENBLAS_MACOS=ON .
Note: depending on your installation, OpenBLAS may masquerade as standard BLAS. To detect standard BLAS and LAPACK, use the ALLOW_BLAS_LAPACK_MACOS option:
cmake -DALLOW_BLAS_LAPACK_MACOS=ON .
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By default, cmake assumes that the Armadillo library and the corresponding header files will be installed in the default system directory (eg. in the /usr hierarchy in Linux-based systems). To install the library and headers in an alternative directory, use the additional option CMAKE_INSTALL_PREFIX in this form:
cmake . -DCMAKE_INSTALL_PREFIX:PATH=alternative_directory
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CMake will detect which relevant libraries are installed on your system (eg. OpenBLAS, LAPACK, SuperLU, ARPACK, etc) and will modify Armadillo's configuration correspondingly. CMake will also generate the Armadillo run-time library, which is a wrapper for all the detected libraries.
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If cmake needs to re-run, it's a good idea to first delete the "CMakeCache.txt" file (not "CMakeLists.txt").
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Caveat: if Armadillo is installed in a non-system directory, make sure that the C++ compiler is configured to use the "lib" and "include" sub-directories present within this directory. Note that the "lib" directory might be named differently on your system. On recent 64 bit Debian & Ubuntu systems it is "lib/x86_64-linux-gnu". On recent 64 bit Fedora & RHEL systems it is "lib64".
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Step 5: If you and have access to root/administrator/superuser privileges (ie. able to use "sudo") and didn't use the CMAKE_INSTALL_PREFIX option, type the following command:
sudo make install
If you don't have root/administrator/superuser privileges, make sure that you use the CMAKE_INSTALL_PREFIX option in Step 4, and type the following command:
make install
If you have installed Armadillo via the CMake installer, use the following command:
g++ prog.cpp -o prog -std=c++11 -O2 -larmadillo
Otherwise, if you want to use Armadillo without installation (ie. without the Armadillo runtime library), use the following command:
g++ prog.cpp -o prog -std=c++11 -O2 -I /home/blah/armadillo-7.200.3/include -DARMA_DONT_USE_WRAPPER -lopenblas
The above command assumes that the armadillo archive was unpacked into /home/blah/
The command needs to be adjusted if the archive was unpacked into a different directory
and/or for each specific version of Armadillo (ie. "7.200.3" needs to be changed).
If you don't have OpenBLAS, on Linux change -lopenblas to -lblas -llapack and on macOS change -lopenblas to -framework Accelerate
See the Questions page for more info on linking: http://arma.sourceforge.net/faq.html
The "examples" directory contains a short example program that uses the Armadillo library.
The installation is comprised of 3 steps:
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Step 1: Copy the entire "include" folder to a convenient location and tell your compiler to use that location for header files (in addition to the locations it uses already). Alternatively, the "include" folder can be used directly.
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Step 2: Modify "include/armadillo_bits/config.hpp" to indicate which libraries are currently available on your system. For example, if LAPACK, BLAS (or OpenBLAS), ARPACK and SuperLU present, uncomment the following lines:
#define ARMA_USE_LAPACK
#define ARMA_USE_BLAS
#define ARMA_USE_ARPACK
#define ARMA_USE_SUPERLUIf support for sparse matrices is not required, don't worry about ARPACK or SuperLU.
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Step 3: Configure your compiler to link with LAPACK and BLAS (and optionally ARPACK and SuperLU).
Within the "examples" folder, there is an MSVC project named "example1_win64" which can be used to compile "example1.cpp". The project needs to be compiled as a 64 bit program: the active solution platform must be set to x64, instead of win32.
The MSVC project was tested on Windows 10 (64 bit) with Visual Studio C++ 2019. Adaptations may need to be made for 32 bit systems, later versions of Windows and/or the compiler. For example, options such as ARMA_BLAS_LONG and ARMA_BLAS_UNDERSCORE, defined in "armadillo_bits/config.hpp", may need to be either enabled or disabled.
The folder "examples/lib_win64" contains a copy of lib and dll files obtained from a pre-compiled release of OpenBLAS 0.3.10: https://github.com/xianyi/OpenBLAS/releases/download/v0.3.10/OpenBLAS-0.3.10-x64.zip The compilation was done by a third party. USE AT YOUR OWN RISK.
Caveat: for any high performance scientific/engineering workloads, we strongly recommend using a Linux based operating system:
- Fedora http://fedoraproject.org/
- Ubuntu http://www.ubuntu.com/
- CentOS http://centos.org/
Armadillo can use OpenBLAS or Intel Math Kernel Library (MKL) as high-speed replacements for BLAS and LAPACK. In essence this involves linking with the replacement libraries instead of BLAS and LAPACK.
Minor modifications to include/armadillo_bits/config.hpp may be required to ensure Armadillo uses the same integer sizes and style of function names as used by the replacement libraries. Specifically, the following defines may need to be enabled or disabled:
ARMA_USE_WRAPPER
ARMA_BLAS_CAPITALS
ARMA_BLAS_UNDERSCORE
ARMA_BLAS_LONG
ARMA_BLAS_LONG_LONG
See the documentation for more information on the above defines.
On Linux-based systems, MKL might be installed in a non-standard location such as /opt which can cause problems during linking. Before installing Armadillo, the system should know where the MKL libraries are located. For example, /opt/intel/mkl/lib/intel64/. This can be achieved by setting the LD_LIBRARY_PATH environment variable, or for a more permanent solution, adding the directory locations to /etc/ld.so.conf. It may also be possible to store a text file with the locations in the /etc/ld.so.conf.d directory. For example, /etc/ld.so.conf.d/mkl.conf. If /etc/ld.so.conf is modified or /etc/ld.so.conf.d/mkl.conf is created, /sbin/ldconfig must be run afterwards.
Below is an example of /etc/ld.so.conf.d/mkl.conf where Intel MKL is installed in /opt/intel
/opt/intel/lib/intel64
/opt/intel/mkl/lib/intel64
If MKL is installed and it is persistently giving problems during linking, Support for MKL can be disabled by editing the CMakeLists.txt file, deleting CMakeCache.txt and re-running the CMake based installation. Comment out the line containing:
INCLUDE(ARMA_FindMKL)
Armadillo can use the ATLAS library for faster versions of a subset of LAPACK and BLAS functions. LAPACK should still be installed to obtain full functionality.
Caveat: the minimum recommended version of ATLAS is 3.10; earlier versions (such as 3.6 and 3.8) can produce incorrect results and/or corrupt memory, leading to random crashes.
Armadillo can use OpenMP to automatically speed up computationally expensive element-wise functions such as exp(), log(), cos(), etc. This requires a C++11/C++14 compiler with OpenMP 3.1+ support.
When using gcc or clang, use the following options to enable both C++11 and OpenMP: -std=c++11 -fopenmp
The documentation for Armadillo functions and classes is available at:
http://arma.sourceforge.net/docs.html
The documentation is also in the "docs.html" file in this folder, which can be viewed with a web browser.
Each release of Armadillo has its public API (functions, classes, constants) described in the accompanying API documentation (docs.html) specific to that release.
Each release of Armadillo has its full version specified as A.B.C, where A is a major version number, B is a minor version number, and C is a patch level (indicating bug fixes).
Within a major version (eg. 7), each minor version has a public API that strongly strives to be backwards compatible (at the source level) with the public API of preceding minor versions. For example, user code written for version 7.100 should work with version 7.200, 7.300, 7.400, etc. However, as later minor versions may have more features (API extensions) than preceding minor versions, user code specifically written for version 7.400 may not work with 7.300.
An increase in the patch level, while the major and minor versions are retained, indicates modifications to the code and/or documentation which aim to fix bugs without altering the public API.
We don't like changes to existing public API and strongly prefer not to break any user software. However, to allow evolution, we reserve the right to alter the public API in future major versions of Armadillo while remaining backwards compatible in as many cases as possible (eg. major version 8 may have slightly different public API than major version 7).
CAVEAT: any function, class, constant or other code not explicitly described in the public API documentation is considered as part of the underlying internal implementation details, and may change or be removed without notice. (In other words, don't use internal functionality).
Armadillo has gone through extensive testing and has been successfully used in production environments. However, as with almost all software, it's impossible to guarantee 100% correct functionality.
If you find a bug in the library or the documentation, we are interested in hearing about it. Please make a small and self-contained program which exposes the bug, and then send the program source and the bug description to the developers. The small program must have a main() function and use only functions/classes from Armadillo and the standard C++ library (no other libraries).
The contact details are at:
http://arma.sourceforge.net/contact.html
Further information about Armadillo is on the frequently asked questions page:
http://arma.sourceforge.net/faq.html
The "mex_interface" folder contains examples of how to interface Octave/Matlab with C++ code that uses Armadillo matrices.
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MLPACK: extensive library of machine learning algorithms
http://mlpack.org -
ensmallen: C++ library of numerical optimisation methods
http://ensmallen.org/ -
SigPack: C++ signal processing library
http://sigpack.sourceforge.net -
RcppArmadillo: integration of Armadillo with the R system and environment
http://dirk.eddelbuettel.com/code/rcpp.armadillo.html -
PyArmadillo: linear algebra library for Python
https://pyarma.sourceforge.io