Searcharxiv⌕ Search

arXiv · cs/9811019

Locked and Unlocked Polygonal Chains in 3D

Abstract

In this paper, we study movements of simple polygonal chains in 3D. We say that an open, simple polygonal chain can be straightened if it can be continuously reconfigured to a straight sequence of segments in such a manner that both the length of each link and the simplicity of the chain are maintained throughout the movement. The analogous concept for closed chains is convexification: reconfiguration to a planar convex polygon. Chains that cannot be straightened or convexified are called locked. While there are open chains in 3D that are locked, we show that if an open chain has a simple orthogonal projection onto some plane, it can be straightened. For closed chains, we show that there are unknotted but locked closed chains, and we provide an algorithm for convexifying a planar simple polygon in 3D with a polynomial number of moves.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T. Biedl, E. Demaine, M. Demaine, S. Lazard, A. Lubiw, J. O'Rourke, M. Overmars, S. Robbins, I. Streinu, G. Toussaint, S. Whitesides. 1998-11-11. Locked and Unlocked Polygonal Chains in 3D. https://arxiv.org/abs/cs/9811019

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Algorithms for the Shortest Vector Problem in $2$-dimensional Lattices, Revisited

Efficiently solving the Shortest Vector Problem (SVP) in two-dimensional lattices holds practical significance in cryptography and computational geometry. While simpler than its high-dimensional counterpart, two-dimensional SVP motivates scalable solutions for high-dimensional lattices and benefits applications like sequence cipher cryptanalysis involving large integers. In this work, we first propose a novel definition of reduced bases and develop an efficient adaptive lattice reduction algorithm \textbf{CrossEuc} that strategically applies the Euclidean algorithm across dimensions. Building on this framework, we introduce \textbf{HVec}, a vectorized generalization of the Half-GCD algorithm originally defined for integers, which can efficiently halve the bit-length of two vectors and may have independent interest. By iteratively invoking \textbf{HVec}, our optimized algorithm \textbf{HVecSBP} achieves a reduced basis in $O(\log n M(n) )$ time for arbitrary input bases with bit-length $n$, where $M(n)$ denotes the cost of multiplying two $n$-bit integers. Compared to existing algorithms, our design is applicable to general forms of input lattices, eliminating the cost of pre-converting input bases to Hermite Normal Form (HNF). The comprehensive experimental results demonstrate that for the input lattice bases in HNF, the optimized algorithm \textbf{HVecSBP} achieves at least a $13.5\times$ efficiency improvement compared to existing methods. For general-form input lattice bases, converting them to HNF before applying \textbf{HVecSBP} offers only marginal advantages in extreme cases where the two basis vectors are nearly degenerate. However, as the linear dependency between input basis vectors decreases, directly employing \textbf{HVecSBP} yields increasingly significant efficiency gains, outperforming hybrid approaches that rely on prior \textbf{HNF} conversion.

cs.CG↗

A Two-Count Baseline for Change Detection on Dynamic Networks

Change-point detectors for dynamic networks aim to find changes in how a network is connected. They are evaluated on real networks with labelled events, but many of these events also change how active the network is: how many vertices take part and how many edges are present. A detector can therefore score well by responding to activity alone, without detecting any change in structure. We test whether detectors from the literature beat a simple baseline that tracks only the number of active vertices and the number of edges in each snapshot, a baseline that we motivate with a standard statistical test for a change in activity. On ten datasets, four of them from the papers of published detectors, none of thirteen detectors ranks the labelled events significantly better than the baseline when the results are pooled (AUC $0.75$, or $0.73$ with scales taken from early snapshots only, against $0.66$ for the best detector). The baseline is best on only two datasets, however, and on the four datasets with the most events three detectors that track individual vertices score higher. When we rewire networks while keeping the degree of every vertex fixed, most detectors that ignore vertex identities barely respond, but several that track individual vertices do. New detectors should be compared with this baseline and tested at fixed degree.

cs.CG↗

Quadratic Complexity of Voronoi Diagrams in $\mathbb{R}^3$ for Lines in a Single Ruling of a Regulus

We study nearest and farthest Voronoi diagrams of lines in three-dimensional Euclidean space, which partition space according to the closest and farthest input lines, respectively. For arbitrary lines, a gap remains between the known quadratic lower bound and near-cubic upper bounds for the complexity of the nearest diagram. We consider input lines chosen from one of two continuous families of straight lines on a smooth surface defined by a quadratic equation, with one line from each family passing through every point of the surface. Under the geometric assumptions stated in the paper, we prove quadratic upper bounds on the total number of vertices, edges, faces, and connected three-dimensional regions in both diagrams. We also construct quadratically many nearest vertices using lines from a single family on one fixed surface, establishing a tight quadratic worst-case bound. The farthest diagram has quadratic total complexity for every input satisfying our assumptions. Our proof uses a parametrization that arranges the input lines in a cyclic order and expresses tangency to a sphere by a polynomial equation of degree four. At a Voronoi vertex, the four tangent input lines give four distinct roots. The sign changes between these roots, together with the nearest or farthest condition, force two intervening parameter intervals to contain no other input parameters. Thus, only quadratically many choices of four lines can define a vertex, and each choice yields at most eight isolated candidate centers.

cs.CG↗