Small-dimensional linear programming and convex hulls made easy

Discrete & Computational Geometry - Tập 6 - Trang 423-434 - 2007
Raimund Seidel1
1Computer Science Division, University of California at Berkeley, Berkeley, USA

Tóm tắt

We present two randomized algorithms. One solves linear programs involvingm constraints ind variables in expected timeO(m). The other constructs convex hulls ofn points in ℝ d ,d>3, in expected timeO(n [d/2]). In both boundsd is considered to be a constant. In the linear programming algorithm the dependence of the time bound ond is of the formd!. The main virtue of our results lies in the utter simplicity of the algorithms as well as their analyses.

Tài liệu tham khảo

[AS] C. R. Aragon and R. G. Seidel, Randomized Search Trees,Proc. 30th IEEE Symp. on Foundations of Computer Science (1989), pp. 540–545. [CK] D. R. Chand and S. S. Kapur, An Algorithm for Convex Polytopes,J. Assoc. Comput. Mach. 17 (1970), 78–86. [C1] K. L. Clarkson, Linear Programming inO(n3d2) Time,Inform. Process. Lett. 22 (1986), 21–24. [C2] K. L. Clarkson, Las Vegas Algorithms for Linear and Integer Programming when the Dimension is Small, Manuscript(Oct. 1989): a preliminary version appeared inProc. 29th IEEE Symp. on Foundations of Computer Science (1988), pp. 452–456. [CS] K. L. Clarkson and P. W. Shor, Applications of Random Sampling in Computational Geometry, II,Discrete Comput. Geom. 4 (1989), 387–422. [D1] M. E. Dyer, Linear Algorithms for Two- and Three-Variable Linear Programs,SIAM J. Comput. 13 (1984), 31–45. [D2] M. E. Dyer, On a Multidimensional Search Technique and Its Applications to the Euclidean One-Centre Problem,SIAM J. Comput. 15 (1986), 725–738. [DF] M. E. Dyer and A. M. Frieze, A Randomized Algorithm for Fixed-Dimensional Linear Programming,Math. Programming 44 (1989), 203–212. [E] H. Edelsbrunner,Algorithms in Combinatorial Geometry, Springer-Verlag, New York (1987). [G] R. L. Graham, An Efficient Algorithm for Constructing the Convex Hull of a Finite Planar Set,Inform. Process. Lett. 1 (1972), 132–133. [K] M. Kallay, Convex Hull Algorithms in Higher Dimensions, Manuscript (1981). [Mc] P. McMullen, The Maximum Number of Faces of a Convex Polytope,Mathematika 17 (1971), 179–184. [M1] N. Megiddo, Linear-Time Algorithms for Linear Programming in ℝ3 and Related Problems,SIAM J. Comput. 12 (1983), 759–776. [M2] N. Megiddo, Linear Programming in Linear Time when the Dimension is Fixed,J. Assoc. Comput. Mach. 31 (1984), 114–127. [PH] F. P. Preparata and S. J. Hong, Convex Hulls of Finite Point Sets in Two and Three Dimensions,Comm. ACM 20 (1977), 87–93. [S1] R. Seidel, A Convex Hull Algorithm Optimal for Point Sets in Even Dimensions, Technical Report 81-14, Department of Computer Science, University of British Columbia (1981). [S2] R. Seidel, Constructing Higher-Dimensional Convex Hulls at Logarithmic Cost per Face,Proc. 18th ACM Symp. on Theory of Computing (1986), pp. 404–413. [S3] R. Seidel, Backwards Analysis of Randomized Geometric Algorithms (Manuscript). [Sw] G. Swart, Finding the Convex Hull Facet by Facet,J. Algorithms 6 (1985), 17–48. [T] R. E. Tarjan,Data Structures and Network Algorithm, Society for Industrial and Applied Mathematics, Philadelphia, PA (1983).