Sparse QR decomposition is a numerical method used to factorize a sparse matrix into a product of two matrices, Q and R, where Q is an orthogonal matrix and R is an upper triangular matrix. This technique is particularly useful in solving linear systems and least squares problems when dealing with large, sparse datasets, which are common in various scientific and engineering applications.
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