Paper by Erik D. Demaine
- Ilya Baran and Erik D. Demaine, “Optimal Adaptive Algorithms for Finding the Nearest and Farthest Point on a Parametric Black-Box Curve”, International Journal of Computational Geometry and Applications, volume 15, number 4, 2005, pages 327–350. Special issue of selected papers from the 20th Annual ACM Symposium on Computational Geometry, 2004.
We consider a general model for representing and manipulating
parametric curves, in which a curve is specified by a black box
mapping a parameter value between 0 and 1 to a point in Euclidean
d-space. In this model, we consider the
nearest-point-on-curve and farthest-point-on-curve
problems: given a curve C and a point p, find
a point on C nearest to p or farthest from p.
In the general black-box model,
no algorithm can solve these problems. Assuming a known bound on the
speed of the curve (a Lipschitz condition), the answer can be
estimated up to an additive error of ε using O(1/ε)
samples, and this bound is tight in the worst case. However, many
instances can be solved with substantially fewer samples, and we give
algorithms that adapt to the inherent difficulty of the particular
instance, up to a logarithmic factor. More precisely, if
OPT(C, p, ε) is the minimum number of samples
of C that
every correct algorithm must perform to achieve tolerance ε,
then our algorithm performs
O(OPT(C, p, ε) log (ε-1 / OPT(C, p, ε)))
samples. Furthermore, any
Ω(k log (ε-1/k))
samples for some
instance C' with OPT(C', p, ε) = k;
except that, for the
nearest-point-on-curve problem when the distance between C and p
is less than ε, OPT is 1 but the upper and lower bounds
on the number of samples are both Θ(1/ε). When bounds
on relative error are desired, we give algorithms that perform
O(OPT ⋅ log (2 + (1+ε-1) ⋅ m-1 / OPT))
samples (where m is the exact minimum or maximum distance from p
to C) and prove that Ω(OPT ⋅ log (1/ε)) samples are
necessary on some problem instances.
- This paper is also available from WorldSciNet. This paper is also available as arXiv:cs.CG/0307005 of the Computing Research Repository (CoRR).
- The paper is 24 pages.
- The paper is available in PostScript (5273k), gzipped PostScript (439k), and PDF (0k).
- See information on file formats.
- [Google Scholar search]
- Related papers:
- Curves_SoCG2004 (Optimal Adaptive Algorithms for Finding the Nearest and Farthest Point on a Parametric Black-Box Curve)
See also other papers by Erik Demaine.
These pages are generated automagically from a
Last updated November 14, 2019 by