The Arnoldi algorithm is a numerical method used to reduce a matrix to a smaller size while preserving its essential features, particularly for computing eigenvalues and eigenvectors. It builds an orthonormal basis for the Krylov subspace, which helps in approximating the action of a matrix on a vector, making it especially useful for large, sparse matrices. This method connects closely to other techniques like the Lanczos algorithm and sparse matrix-vector multiplication, as it leverages these concepts to enhance computational efficiency.
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