# Armadillo **Repository Path**: mirrors/armadillo-code ## Basic Information - **Project Name**: Armadillo - **Description**: C++ Library for Linear Algebra & Scientific Computing - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: 15.0.x - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 5 - **Forks**: 1 - **Created**: 2019-10-22 - **Last Updated**: 2025-10-04 ## Categories & Tags **Categories**: mathlibs **Tags**: None ## README ### Armadillo: C++ Library for Linear Algebra & Scientific Computing https://arma.sourceforge.net Copyright 2008-2025 Conrad Sanderson (https://conradsanderson.id.au) Copyright 2008-2016 National ICT Australia (NICTA) Copyright 2017-2025 Data61 / CSIRO --- ### Quick Links - [download latest stable release](https://arma.sourceforge.net/download.html) - [documentation for functions and classes](https://arma.sourceforge.net/docs.html) - [bug reports & questions](https://arma.sourceforge.net/faq.html) --- ### Contents 1. [Introduction](#1-introduction) 2. [Citation Details](#2-citation-details) 3. [Distribution License](#3-distribution-license) 4. [Prerequisites and Dependencies](#4-prerequisites-and-dependencies) 5. [Linux and macOS: Installation](#5-linux-and-macos-installation) 6. [Linux and macOS: Compiling and Linking](#6-linux-and-macos-compiling-and-linking) 7. [Windows: Installation](#7-windows-installation) 8. [Windows: Compiling and Linking](#8-windows-compiling-and-linking) 9. [Support for OpenBLAS and Intel MKL](#9-support-for-openblas-and-intel-mkl) 10. [Support for OpenMP](#10-support-for-openmp) 11. [Documentation of Functions and Classes](#11-documentation-of-functions-and-classes) 12. [Caveat on use of C++11 auto Keyword](#12-caveat-on-use-of-c11-auto-keyword) 13. [API Stability and Version Policy](#13-api-stability-and-version-policy) 14. [Bug Reports and Frequently Asked Questions](#14-bug-reports-and-frequently-asked-questions) 15. [MEX Interface to Octave/Matlab](#15-mex-interface-to-octavematlab) 16. [Related Software Using Armadillo](#16-related-software-using-armadillo) --- ### 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 - https://conradsanderson.id.au * Ryan Curtin - https://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: An Efficient Framework for Numerical Linear Algebra. International Conference on Computer and Automation Engineering, 2025. * Conrad Sanderson and Ryan Curtin. Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation. Mathematical and Computational Applications, Vol. 24, No. 3, 2019. --- ### 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 and Dependencies The functionality of Armadillo is partly dependent on other libraries: - OpenBLAS (or standard BLAS) - LAPACK - ARPACK - SuperLU Use of OpenBLAS (instead of standard BLAS) is strongly recommended on all systems. On macOS, the Accelerate framework can be used for BLAS and LAPACK functions. If sparse matrices are not needed, ARPACK and SuperLU are not required. Armadillo requires a C++ compiler that supports at least the C++14 standard. On Linux-based systems, install the GCC C++ compiler, which is available as a 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 (at least version 8) 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. Caveats on the use of SuperLU: - SuperLU must be available as a shared library - Only the following SuperLU versions are supported: 5.2.x, 5.3.x, 6.0.x, 7.0.x - SuperLU 6.0.x and 7.0.x must be compiled with default integer size (32 bits) --- ### 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 https://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. 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/user/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++14 -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++14 -lopenblas -llapack If you have manually installed Armadillo in a non-standard location, such as `/home/user/include/`, you will need to make sure that your C++ compiler searches `/home/user/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++14 -I /home/user/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++14 -I /home/user/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: https://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_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/OpenMathLib/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, such as: * Fedora https://fedoraproject.org/ * Ubuntu https://www.ubuntu.com/ * Alma Linux https://almalinux.org/ * Rocky Linux https://rockylinux.org/ --- ### 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_LONG_LONG ARMA_DONT_USE_FORTRAN_HIDDEN_ARGS ARMA_BLAS_UNDERSCORE ARMA_BLAS_CAPITALS 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. Examples: `/opt/intel/oneapi/mkl/latest/lib`, `/opt/intel/mkl/lib/intel64/`. Before installing Armadillo, the system must know where the MKL libraries are located. This can be achieved via several ways: 1. By setting the `LD_LIBRARY_PATH` environment variable. 2. By adding the MKL library directory locations to the `/etc/ld.so.conf` text file, followed by running `/sbin/ldconfig`. 3. By creating a text file named `/etc/ld.so.conf.d/mkl.conf` which contains the MKL library directory locations, followed by running `/sbin/ldconfig`. 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 OpenMP Armadillo can use OpenMP to automatically speed up computationally expensive element-wise functions such as exp(), log(), cos(), etc. This requires a C++ compiler with OpenMP 4.0+ support. For GCC and Clang compilers, use the following option to enable OpenMP: `-fopenmp` --- ### 11: Documentation of Functions and Classes The documentation of Armadillo functions and classes is available at: https://arma.sourceforge.net/docs.html The documentation is also in the `docs.html` file distributed with Armadillo. Use a web browser to view it. --- ### 12: Caveat on use of C++11 auto Keyword Use of the C++11 `auto` keyword is not recommended with Armadillo objects and expressions. Armadillo has a template meta-programming framework which creates short-lived temporaries that are not properly handled by `auto`. --- ### 13: API Stability and Version Policy 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. The version specification has explicit meaning (similar to [Semantic Versioning](https://semver.org/)), as follows: * Within a major version (eg. 10), 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 10.0 should work with version 10.1, 10.2, etc. However, subsequent minor versions may have more features (API additions and extensions) than preceding minor versions. As such, user code _specifically_ written for version 10.2 may not work with 10.1. * 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, the public API in future major versions while remaining backwards compatible in as many cases as possible (eg. major version 11 may have slightly different public API than major version 10). **CAVEAT:** the above policy applies only to the public API described in the documentation. Any functionality within Armadillo which is _not explicitly_ described in the public API documentation is considered as internal implementation detail, and may be changed or removed without notice. --- ### 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: https://arma.sourceforge.net/contact.html Further information about Armadillo is on the frequently asked questions page: https://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 https://mlpack.org * ensmallen: C++ library for numerical optimisation (L-BFGS, SGD, CMA-ES, etc) https://ensmallen.org/ * RcppArmadillo: integration of Armadillo with R https://dirk.eddelbuettel.com/code/rcpp.armadillo.html * CARMA: interface between Armadillo and Python / NumPy https://github.com/RUrlus/carma