# From perspective maps to epigraphical projection

arXiv:2102.06809, 2021

arXiv:2102.06809, 2021

The projection onto the epigraph or a level set of a closed proper convex function can be achieved by ﬁnding a root of a scalar equation that involves the proximal operator as a function of the proximal parameter. This paper develops the variational analysis of this scalar equation. The approach is based on a study of the variational-analytic properties of general convex optimization problems that are (partial) inﬁmal projections of the the sum of the function in question and the perspective map of a convex kernel. When the kernel is the Euclidean norm squared, the solution map corresponds to the proximal map, and thus the variational properties derived for the general case apply to the proximal case. Properties of the value function and the corresponding solution map—including local Lipschitz continuity, directional diﬀerentiability, and semismoothness—are derived. An SC 1 optimization framework for computing epigraphical and level-set projections is thus established. Numerical experiments on 1-norm projection illustrate the eﬀectiveness of the approach as compared with specialized algorithms.