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2012 NeurIPS NeurIPS 2012

Nonconvex Penalization Using Laplace Exponents and Concave Conjugates

In this paper we study sparsity-inducing nonconvex penalty functions using L´evy processes. We define such a penalty as the Laplace exponent of a subordina- tor. Accordingly, we propose a novel approach for the construction of sparsity- inducing nonconvex penalties. Particularly, we show that the nonconvex logarith- mic (LOG) and exponential (EXP) penalty functions are the Laplace exponents of Gamma and compound Poisson subordinators, respectively. Additionally, we explore the concave conjugate of nonconvex penalties. We find that the LOG and EXP penalties are the concave conjugates of negative Kullback-Leiber (KL) dis- tance functions. Furthermore, the relationship between these two penalties is due to asymmetricity of the KL distance.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Mathematics & Optimization
🐣 Hot Topic Early Bird - sparse optimization

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