Armadillo is a C++ linear algebra library that supports sparse matrices, which are efficient representations of large matrices with mostly zero elements. It implements sparse matrices using the compressed sparse column (CSC) format, where non-zero elements are stored in three arrays: values, row indices, and column pointers.
Armadillo’s implementation allows for various operations on sparse matrices, including arithmetic operations like addition, subtraction, and element-wise multiplication; matrix-vector multiplications; and solving linear systems. Additionally, it provides functions to convert between dense and sparse formats, as well as utility functions such as transposing, reshaping, and resizing.
The library also offers specialized algorithms for sparse matrices, such as iterative solvers (e.g., conjugate gradient method), decomposition methods (e.g., LU, Cholesky), and eigenvalue/eigenvector computations. These algorithms take advantage of the sparsity structure to reduce computational complexity and memory usage compared to their dense counterparts.Armadillo is a C++ linear algebra library that supports sparse matrices, which are efficient representations of large matrices with mostly zero elements. It implements sparse matrices using the compressed sparse column (CSC) format, where non-zero elements are stored in three arrays: values, row indices, and column pointers.
Armadillo’s implementation allows for various operations on sparse matrices, including arithmetic operations like addition, subtraction, and element-wise multiplication; matrix-vector multiplications; and solving linear systems. Additionally, it provides functions to convert between dense and sparse formats, as well as utility functions such as transposing, reshaping, and resizing.
The library also offers specialized algorithms for sparse matrices, such as iterative solvers (e.g., conjugate gradient method), decomposition methods (e.g., LU, Cholesky), and eigenvalue/eigenvector computations. These algorithms take advantage of the sparsity structure to reduce computational complexity and memory usage compared to their dense counterparts.
