Least-Square-Based Three-Term Conjugate Gradient Projection Method for ℓ1-Norm Problems with Application to Compressed Sensing (original) (raw)
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2007
Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (ℓ2) error term combined with a sparseness-inducing (ℓ1) regularization term. Basis Pursuit, the Least Absolute Shrinkage and Selection Operator (LASSO), waveletbased deconvolution, and Compressed Sensing are a few well-known examples of this approach.
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