Abstract. Quadratic-support functions [Aravkin, Burke, and Pillonetto; J. Mach. Learn. Res. 14(1), 2013] constitute a parametric family of convex functions that includes a range of useful regularization terms found in applications of convex optimization. We show how an interior method can be used to efficiently compute the proximal operator of a quadratic-support function under different metrics. When the metric and the function have the right structure, the proximal map can be computed with cost nearly linear in the input size. We describe how to use this approach to implement quasi-Newton methods for a rich class of nonsmooth problems that arise, for example, in sparse optimization, image denoising, and sparse logistic regression.
In REF , interior point methods are used to solve the proximal mapping efficiently for so-called quadratic support functions.
Efficient evaluation of scaled proximal operators
Electronic Transactions on Numerical Analysis, 46:1-22, 2017
Mathematics Computer Science