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dc.contributor.authorNaitsat, Alexanderen_US
dc.contributor.authorZeevi, Yehoshua Y.en_US
dc.contributor.editorBommes, David and Huang, Huien_US
dc.date.accessioned2019-07-11T06:16:27Z
dc.date.available2019-07-11T06:16:27Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-094-9
dc.identifier.issn1727-8384
dc.identifier.urihttps://doi.org/10.2312/sgp.20191214
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/sgp20191214
dc.description.abstractWe present a new unified algorithm for optimizing geometric energies and computing positively oriented simplicial mappings. Its major improvements over the state-of-the-art are: adaptive partition of vertices into coordinate blocks with the blended local-global strategy, introduction of new distortion energies for repairing inverted and degenerated simplices, modification of standard rotation-invariant measures, introduction of displacement norm for improving convergence criteria and for controlling the proposed local-global blending. Together these improvements form the basis for Adaptive Block Coordinate Descent (ABCD) algorithm aimed at robust geometric optimization. Our algorithm achieves state-of-the-art results in distortion minimization, even with highly distorted invalid initializations that contain thousands of inverted and degenerated elements. We show over a wide range of 2D and 3D problems that ABCD is more robust than existing techniques in locally injective mappings.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectTheory of computation
dc.subjectNonconvex optimization
dc.subjectComputing methodologies
dc.subjectComputer graphics
dc.titleAdaptive Block Coordinate Descent for Distortion Minimizationen_US
dc.description.seriesinformationSymposium on Geometry Processing 2019- Posters
dc.description.sectionheadersPosters
dc.identifier.doi10.2312/sgp.20191214
dc.identifier.pages3-4


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