Variational Analysis on the Signed Distance Functions

Honglin Luo (), Xianfu Wang () and Brett Lukens
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Honglin Luo: Chongqing Normal University
Xianfu Wang: University of British Columbia
Brett Lukens: University of British Columbia

Journal of Optimization Theory and Applications, 2019, vol. 180, issue 3, No 4, 774 pages

Abstract: Abstract The signed distance function (or oriented distance function) of a set in a metric space determines the distance of a given point from the boundary of the set, with the sign determined by whether the point is in the set or in its complement. The knowledge of signed distance functions is a very valuable information in various fields of applied mathematics such as collision detection, binary classification, shape analysis, fuzzy numbers ranking and level set methods. One distinguished feature of the signed distance function is that it reflects the geometric structure of the set much better than the distance function does. We explore many interesting analytical properties of signed distance functions and use them to construct convex functions with not convex subdifferential domains. Several examples are presented to illustrate most of these fine properties.

Keywords: Boundary projection; Fenchel conjugate; Maximally monotone operator; Nearly convex sets; Not convex subdifferential domain; Paramonotone operator; Signed distance function; Skeleton of a convex set; Subdifferential; 49J53; 47H04; 47N10; 49J52 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-018-1414-2

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