Iteratively reweighted least squares minimization for sparse recovery

Under certain conditions (known as the restricted isometry property, or RIP) on the m × N matrix Φ (where m < N), vectors x ∈ ℝN that are sparse (i.e., have most of their entries equal to 0) can be recovered exactly from y := Φx even though Φ−1(y) is typically an (N − m)—dimensional hyperplane; i...

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