On Truncated-SVD-like Sparse Solutions to Least-Squares Problems of Arbitrary Dimensions

Christos Boutsidis

We describe two algorithms for computing a sparse solution to a least-squares problem where the coefficient matrix can have arbitrary dimensions. We show that the solution vector obtained by our algorithms is close to the solution vector obtained via the truncated SVD approach.

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