Decompositions and boundary coverings of non-convex fat polyhedra

被引:3
作者
de Berg, Mark [1 ]
Gray, Chris [2 ]
机构
[1] TU Eindhoven, Dept Comp Sci, NL-5600 MB Eindhoven, Netherlands
[2] TU Braunschweig, Dept Comp Sci, Braunschweig, Germany
来源
COMPUTATIONAL GEOMETRY-THEORY AND APPLICATIONS | 2010年 / 43卷 / 02期
关键词
Decomposition; Tetrahedralization; Fatness; Realistic input models; OBJECTS; UNION; PARTITIONS; COMPLEXITY; 3-SPACE;
D O I
10.1016/j.comgeo.2009.04.003
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We show that any locally-fat (or (alpha, beta)-covered) polyhedron with convex fat faces can be decomposed into O(n) tetrahedra, where n is the number of vertices of the polyhedron. We also show that the restriction that the faces are fat is necessary: there are locally-fat polyhedra with non-fat faces that require Omega(n(2)) pieces in any convex decomposition. Furthermore, we show that if we want the tetrahedra in the decomposition to be fat themselves, then their number cannot be bounded as a function of n in the worst case. Finally, we obtain several results on the problem where we want to only cover the boundary of the polyhedron, and not its entire interior. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:73 / 83
页数:11
相关论文
共 41 条
[31]   Novel Non-Convex Regularization for Generating Double Threshold Value in Penalized Least Squares Regression [J].
Kittisuwan, Pichid ;
Thaiwirot, Wanwisa .
FLUCTUATION AND NOISE LETTERS, 2022, 21 (06)
[32]   Verified distance computation between non-convex superquadrics using hierarchical space decomposition structures [J].
Kiel, Stefan ;
Luther, Wolfram ;
Dyllong, Eva .
SOFT COMPUTING, 2013, 17 (08) :1367-1378
[33]   S-MAPEL: Monotonic Optimization for Non-Convex Joint Power Control and Scheduling Problems [J].
Qian, Li Ping ;
Zhang, Ying Jun .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2010, 9 (05) :1708-1719
[34]   Learning non-convex abstract concepts with regulated activation networks A hybrid and evolving computational modeling approach [J].
Sharma, Rahul ;
Ribeiro, Bernardete ;
Pinto, Alexandre Miguel ;
Cardoso, F. Amilcar .
ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE, 2020, 88 (11-12) :1207-1235
[35]   Joint Power and Admission Control: Non-Convex Lq Approximation and An Effective Polynomial Time Deflation Approach [J].
Liu, Ya-Feng ;
Dai, Yu-Hong ;
Ma, Shiqian .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (14) :3641-3656
[36]   Salt and pepper noise removal method based on stationary Framelet transform with non-convex sparsity regularization [J].
Chen, Yingpin ;
Huang, Yuming ;
Wang, Lingzhi ;
Huang, Huiying ;
Song, Jianhua ;
Yu, Chaoqun ;
Xu, Yanping .
IET IMAGE PROCESSING, 2022, 16 (07) :1846-1865
[37]   A new development of non-local image denoising using fixed-point iteration for non-convex lp sparse optimization [J].
Cai, Shuting ;
Liu, Kun ;
Yang, Ming ;
Tango, Jianliang ;
Xiong, Xiaoming ;
Xiao, Mingqing .
PLOS ONE, 2018, 13 (12)
[38]   Low-rank tensor recovery via non-convex regularization, structured factorization and spatio-temporal characteristics [J].
Yu, Quan ;
Yang, Ming .
PATTERN RECOGNITION, 2023, 137
[39]   The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification [J].
Zhang, Shaorong ;
Zhu, Zhibin ;
Zhang, Benxin ;
Feng, Bao ;
Yu, Tianyou ;
Li, Zhi .
SENSORS, 2020, 20 (17) :1-29
[40]   High-Fidelity compressive spectral image reconstruction through a novel Non-Convex Non-Local Low-Rank tensor approximation model [J].
Jiang, Heng ;
Xu, Chen ;
Liu, Lilin .
OPTICS AND LASER TECHNOLOGY, 2024, 171