2000 character limit reached
On Truncated-SVD-like Sparse Solutions to Least-Squares Problems of Arbitrary Dimensions
Published 30 Dec 2011 in cs.DS and math.NA | (1201.0073v2)
Abstract: 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.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.