University of Birmingham > Talks@bham > Optimisation and Numerical Analysis Seminars > Primal and dual re-weighted l1-algorithms of the general sparsity model

Primal and dual re-weighted l1-algorithms of the general sparsity model

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If you have a question about this talk, please contact Sergey Sergeev.

In this talk, we will study a general l0-minimization model, which covers some sparsity models as well as structured sparsity models in many practical applications. We first explore some theoretical properties for the solutions of this model. In the main part of the talk, two types of re-weighted l1-algorithms will be developed from both the perspectives of primal and dual spaces, respectively. The primal re-weighted l1-algorithms will be derived through the 1st order approximation of the so-called merit functions for sparsity. The so-called dual re-weighted l1-algorithms for the general l0-model will be developed based on the reformulation of the general l0-model as a certain bilevel programming problem under the assumption of strict complementarity. Finally, we show the numerical experiments to demonstrate the efficiency of the primal and dual re-weighted l1-algorithms and compare with some existing algorithms.

This talk is part of the Optimisation and Numerical Analysis Seminars series.

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