APPROXIMATE VERTEX ENUMERATION

被引:0
作者
Loehne, Andreas [1 ]
机构
[1] Friedrich Schiller Univ Jena, Jena, Germany
关键词
D O I
暂无
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The problem to compute the vertices of a polytope given by affine inequalities is called vertex enumeration. The inverse problem, which is equivalent by polarity, is called the convex hull problem. We introduce 'approximate vertex enumeration' as the problem to compute the vertices of a polytope which is close to the original polytope given by affine inequalities. In contrast to exact vertex enumerations, both polytopes are not required to be combinatorially equivalent.Two algorithms for this problem are introduced. The first one is an approximate variant of Motzkin's double description method. Only under certain strong conditions, which are not acceptable for practical reasons, we were able to prove correctness of this method for polytopes of arbitrary dimension. The second method, called shortcut algorithm, is based on constructing a plane graph and is restricted to polytopes of dimension 2 and 3. We prove correctness of the shortcut algorithm. As a consequence, we also obtain correctness of the approximate double description method, only for dimension 2 and 3 but without any restricting conditions as still required for higher dimensions. We show that for dimension 2 and 3 both algorithm remain correct if imprecise arithmetic is used and the computational error caused by imprecision is not too high. Both algorithms were implemented. The numerical examples motivate the approximate vertex enumeration problem by showing that the approximate problem is often easier to solve than the exact vertex enumeration problem.It remains open whether or not the approximate double description method (without any restricting condition) is correct for polytopes of dimension 4 and higher.
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页码:257 / 286
页数:30
相关论文
共 22 条
  • [1] How good are convex hull algorithms?
    Avis, D
    Bremner, D
    Seidel, R
    [J]. COMPUTATIONAL GEOMETRY-THEORY AND APPLICATIONS, 1997, 7 (5-6): : 265 - 301
  • [2] The Quickhull algorithm for convex hulls
    Barber, CB
    Dobkin, DP
    Huhdanpaa, H
    [J]. ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, 1996, 22 (04): : 469 - 483
  • [3] Berg M. d., 2008, COMPUTATIONAL GEOMET, DOI DOI 10.1007/978-3-540-77974-2
  • [4] Bradford Barber C., Qhull: Quickhull algorithm for computing the convex hull
  • [5] Bronstein Efim M., 2008, J MATH SCI-U TOKYO, V153, P727, DOI [10.1007/s10958-008-9144-x, DOI 10.1007/S10958-008-9144-X]
  • [6] Ciripoi D., 2019, Bensolve tools, version 1.3. Gnu Octave/Matlab toolbox for calculus of convex polyhedra, calculus of polyhedral convex functions, global optimization, vector linear programming
  • [7] Calculus of convex polyhedra and polyhedral convex functions by utilizing a multiple objective linear programming solver
    Ciripoi, Daniel
    Loehne, Andreas
    Weissing, Benjamin
    [J]. OPTIMIZATION, 2019, 68 (10) : 2039 - 2054
  • [8] DIESTEL R., 2017, Grad. Texts Math., V173, DOI [10.1007/978-3-662-53622-3, DOI 10.1007/978-3-662-53622-3]
  • [9] STABLE MAINTENANCE OF POINT SET TRIANGULATIONS IN 2 DIMENSIONS
    FORTUNE, S
    [J]. 30TH ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, 1989, : 494 - 499
  • [10] Hamel A.H., 2015, SET OPTIMIZATION APP, P65