Sparse Recovery by l0 Penalty

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• Bangti Jin (University College London)
• Wednesday 01 November 2017, 12:00-12:50
• Seminar Room 1, Newton Institute.

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VMVW02 - Generative models, parameter learning and sparsity

Sparsity is one of the powerful tools for signal recovery, and has achieved great success in many practical applications. Conventionally this is realized numerically by imposing an l1 penalty, which is the convex relaxation of the l0 penalty. In this talk, I will discuss our recent efforts in the efficient numerical solution of the l0 problem. I will describe a primal dual active set algorithm, and present some numerical results to illustrate its convergence. This talk is based on joint work with Dr. Yuling Jiao and Dr. Xiliang Lu.

This talk is part of the Isaac Newton Institute Seminar Series series.

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