Compensated convex transforms and geometric singularity extraction from semiconvex functions - Zhang, Kewei; Crooks, Elaine; Orlando, Antonio (Zhongguo Kexue Zazhishe (Science China Press), 2016)
The upper and lower compensated convex transforms are `tight' one-sided approximations for a given function. We apply these transforms to the extraction of fine geometric singularities from general semiconvex/semiconcave functions and DC-functions in Rn (difference of convex functions). Well-known geometric examples of (locally) semiconcave functions include the Euclidean distance function and the Euclidean squared-distance function. For a locally semiconvex function f with general modulus, we show that `locally' a point is singular (a non-differentiable point) if and only if it is a scale 1-valley point, hence by using our method we can extract all fine singular points from a given semiconvex function. More precisely, if f is a semiconvex function with general modulus and x is a singular point, then locally the limit of the scaled valley transform exists at every point x and can be calculated as limλ→+∞λVλ(f)(x)=r2x/4, where rx is the radius of the minimal bounding sphere of the (Fréchet) subdifferential ∂−f(x) of the locally semiconvex f and Vλ(f)(x) is the valley transform at x. Thus the limit function V∞(f)(x):=limλ→+∞λVλ(f)(x)=r2x/4 provides a `scale 1-valley landscape function' of the singular set for a locally semiconvex function f. At the same time, the limit also provides an asymptotic expansion of the upper transform Cuλ(f)(x) when λ approaches +∞. For a locally semiconvex function f with linear modulus we show further that the limit of the gradient of the upper compensated convex transform limλ→+∞∇Cuλ(f)(x) exists and equals the centre of the minimal bounding sphere of ∂−f(x). We also show that for a DC-function f=g−h, the scale 1-edge transform, when λ→+∞, satisfies liminfλ→+∞λEλ(f)(x)≥(rg,x−rh,x)2/4, where rg,x and rh,x are the radii of the minimal bounding spheres of the subdifferentials ∂−g and ∂−h of the two convex functions g and h at x, respectively.