Geometry of Sparsity-Inducing Norms
Jean-Philippe Chancelier (CERMICS UMR 9032), Michel de Lara (CERMICS UMR 9032), Antoine Deza (GALaC), Lionel Pournin (UP13)

TL;DR
This paper explores the geometric structure of sparsity-inducing norms that explicitly promote solutions with a fixed number of nonzero entries, providing conditions and properties that facilitate support identification in sparse optimization.
Contribution
It introduces generalized $k$-support dual norms for controlling sparsity, analyzes their geometric properties, and characterizes the faces of their unit balls, advancing sparse optimization theory.
Findings
Conditions under which $k$-support dual norms promote $k$-sparse solutions
Structural characterization of unit ball faces as hypersimplices
Geometric analysis of $k$-support dual norms for $ ext{l}_p$-norms
Abstract
Sparse optimization seeks an optimal solution with few nonzero entries. To achieve this, it is common to add to the criterion a penalty term proportional to the -norm, which is recognized as the archetype of sparsity-inducing norms. In this approach, the number of nonzero entries is not controlled a priori. By contrast, in this paper, our motivation is to find an optimal solution with at most~ nonzero coordinates (or for short, -sparse vectors), where is a given sparsity threshold (or ``sparsity budget''). For this purpose, we study the class of generalized -support dual~norms that arise from any given so-called source norm. When added as a penalty term, we provide conditions under which such generalized -support dual~norms promote -sparse solutions. The result follows from an analysis of the exposed faces of closed convex sets generated by -sparse vectors,…
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