Description of the Minimizers of Least Squares Regularized with pweb0 norm. Uniqueness of the Global Minimizer
We have an \sf{M}\times\sf{N} real valued arbitrary matrix $A$ (e.g. a dictionary) with \sf{M} 0$ For several decades, this objective has attracted a ceaseless effort to conceive algorithms approaching a good minimizer. Our theoretical contributions, summarized below, shed new light on the existing algorithms and can help in the conception of innovative numerical schemes. Solving the normal equation associated with any \sf{M} row submatrix of $A$ is equivalent to computing a local minimizer \hat u$ of \mathcal{F}}_d$ (Local) minimizers \hat u$ of \mathcal{F}}_d$ are strict if and only if the submatrix, composed of those columns of $A$ wh.
SIAM J. Imaging Sci.