The armadillo from zeno-sole

Armadillo: C++ Library for Linear Algebra & Scientific Computing

http://arma.sourceforge.net

1. Introduction

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

2: Citation Details

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.

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.

3: Distribution License

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.

4: Prerequisites

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.

On Linux-based systems, install the GCC C++ compiler, which is available as pre-built package. The package name might be g++ or gcc-c++ depending on your system.

On macOS systems, a C++ compiler can be obtained by first installing Xcode (version 8 or later) and then running the following command in a terminal window:

xcode-select --install

On Windows systems, the MinGW toolset or Visual Studio C++ 2019 (MSVC) can be used.

The functionality of Armadillo is partly dependent on other libraries: OpenBLAS (or standard BLAS) and LAPACK (for dense matrices), as well as ARPACK and SuperLU (for sparse matrices). Caveat: only SuperLU versions 5.2.x can be used. On macOS, the Accelerate framework can be used for BLAS and LAPACK functions.

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 such as eigen decomposition and system solvers will not be. Matrix multiplication may not be as fast (mainly for large matrices).

5: Linux and macOS: Installation

Armadillo can be installed in several ways: either manually or via cmake, with or without root access. The cmake based installation is preferred. The cmake tool can be downloaded from http://www.cmake.org or (preferably) installed using the package manager on your system; on macOS systems, cmake can be installed through MacPorts or Homebrew.

Before installing Armadillo, first install OpenBLAS and LAPACK, and optionally ARPACK and SuperLU. 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.

5a: Installation via CMake

The cmake based installer detects which relevant libraries are installed on your system (eg. OpenBLAS, LAPACK, SuperLU, ARPACK, etc) and correspondingly modifies Armadillo's configuration. The installer also generates the Armadillo runtime library, which is a wrapper for all the detected libraries, and provides a thread-safe random number generator.

Change into the directory that was created by unpacking the armadillo archive (eg. cd armadillo-10.6.1) and then run cmake using:

cmake .

NOTE: the full stop (.) separated from cmake by a space is important.

On macOS, to enable the detection of OpenBLAS, use the additional ALLOW_OPENBLAS_MACOS option when running cmake:

cmake -DALLOW_OPENBLAS_MACOS=ON .

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 .

By default, cmake assumes that the Armadillo runtime 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

If cmake needs to be re-run, it's a good idea to first delete the CMakeCache.txt file (not CMakeLists.txt).

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.

If you have sudo access (ie. root/administrator/superuser privileges) and didn't use the CMAKE_INSTALL_PREFIX option, run the following command:

sudo make install

If you don't have sudo access, make sure to use the CMAKE_INSTALL_PREFIX option and run the following command:

make install

5b: Manual Installation

Manual installation involves simply copying the include/armadillo header and the associated include/armadillo_bits directory to a location such as /usr/include/ which is searched by your C++ compiler. If you don't have sudo access or don't have write access to /usr/include/, use a directory within your own home directory (eg. /home/blah/include/).

If required, modify include/armadillo_bits/config.hpp to indicate which libraries are currently available on your system. Comment or uncomment the following lines:

#define ARMA_USE_LAPACK  
#define ARMA_USE_BLAS  
#define ARMA_USE_ARPACK  
#define ARMA_USE_SUPERLU

If support for sparse matrices is not needed, ARPACK and SuperLU are not necessary.

Note that the manual installation will not generate the Armadillo runtime library, and hence you will need to link your programs directly with OpenBLAS, LAPACK, etc.

6: Linux and macOS: Compiling and Linking

If you have installed Armadillo via the cmake installer, use the following command to compile your programs:

g++ prog.cpp -o prog -O2 -std=c++11 -larmadillo

If you have installed Armadillo manually, link with OpenBLAS and LAPACK instead of the Armadillo runtime library:

g++ prog.cpp -o prog -O2 -std=c++11 -lopenblas -llapack

If you have manually installed Armadillo in a non-standard location, such as /home/blah/include/, you will need to make sure that your C++ compiler searches /home/blah/include/ by explicitly specifying the directory as an argument/option. For example, using the -I switch in GCC and Clang:

g++ prog.cpp -o prog -O2 -std=c++11 -I /home/blah/include/ -lopenblas -llapack

If you're getting linking issues (unresolved symbols), enable the ARMA_DONT_USE_WRAPPER option:

g++ prog.cpp -o prog -O2 -std=c++11 -I /home/blah/include/ -DARMA_DONT_USE_WRAPPER -lopenblas -llapack

If you don't have OpenBLAS, on Linux change -lopenblas to -lblas; on macOS change -lopenblas -llapack to -framework Accelerate

The examples directory contains a short example program that uses Armadillo.

We recommend that compilation is done with optimisation enabled, in order to make best use of the extensive template meta-programming techniques employed in Armadillo. For GCC and Clang compilers use -O2 or -O3 to enable optimisation.

For more information on compiling and linking, see the Questions page: http://arma.sourceforge.net/faq.html

7: Windows: Installation

The installation is comprised of 3 steps:

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.
Step 2: If required, modify include/armadillo_bits/config.hpp to indicate which libraries are currently available on your system:

#define ARMA_USE_LAPACK
#define ARMA_USE_BLAS
#define ARMA_USE_ARPACK
#define ARMA_USE_SUPERLU

If support for sparse matrices is not needed, ARPACK or SuperLU are not necessary.
Step 3: Configure your compiler to link with LAPACK and BLAS (and optionally ARPACK and SuperLU). Note that OpenBLAS can be used as a high-performance substitute for both LAPACK and BLAS.

8: Windows: Compiling and Linking

Within the examples folder, the MSVC project named example1_win64 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 be required 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 include/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: https://github.com/xianyi/OpenBLAS/releases/
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:

9: Support for OpenBLAS and Intel MKL

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)

10: Support for ATLAS

If OpenBLAS is not available, 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. The minimum recommended version of ATLAS is 3.10.

11: Support for OpenMP

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.

For GCC and Clang compilers, use the following options to enable both C++11 and OpenMP: -std=c++11 -fopenmp

12: Documentation of Functions and Classes

The documentation of Armadillo functions and classes is available at:
http://arma.sourceforge.net/docs.html

The documentation is also in the docs.html file distributed with Armadillo. Use a web browser to view it.

13: API Stability and Versioning

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).

14: Bug Reports and Frequently Asked Questions

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

15: MEX Interface to Octave/Matlab

The mex_interface folder contains examples of how to interface Octave/Matlab with C++ code that uses Armadillo matrices.

16: Related Software Using Armadillo

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

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