Compensated Convex Transforms and Geometric Singularity Extraction from Semiconvex Functions
Kewei Zhang, Elaine Crooks, Antonio Orlando

TL;DR
This paper introduces compensated convex transforms to extract geometric singularities from semiconvex functions, providing asymptotic analysis and new landscape functions for understanding the structure of singular sets.
Contribution
It develops a novel framework using compensated convex transforms for analyzing singularities in semiconvex and DC-functions, including asymptotic expansions and limit behaviors.
Findings
Limit of scaled valley transform equals squared radius of minimal bounding sphere.
Gradient of upper transform converges to center of minimal bounding sphere.
Scale 1-edge transform provides lower bounds related to subdifferential radii.
Abstract
We apply upper and lower compensated convex transforms, which are `tight' one-sided approximations of a given function, to the extraction of fine geometric singularities from semiconvex/semiconcave functions and DC-functions in (difference of convex functions). Well-known examples of (locally) semiconcave functions include the Euclidean distance and squared distance functions. For a locally semiconvex function with general modulus, we show that `locally' a point is a singular (non-differentiable) point if and only if it is a scale -valley point, and if is a singular point, then locally the limit of the scaled valley transform exists at every point and , where is the radius of the minimal bounding sphere of the (Fr\'echet) subdifferential and is the valley…
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Taxonomy
TopicsOptimization and Variational Analysis · Sparse and Compressive Sensing Techniques · Numerical methods in inverse problems
