A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers

Abstract

Regularization techniques are widely employed in optimization-based approaches for solving ill-posed inverse problems in data analysis and scientific computing. These methods are based on augmenting the objective with a penalty function, which is specified based on prior domain-specific expertise to induce a desired structure in the solution. We consider… (More)

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