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Dianna Xu

Publications and source records attributed to Dianna Xu.

2 recordsLinked to original sources

Hamiltonian Cycle Construction in Rectangular Grid Graphs with Three-Stacked Triangular Holes

The Hamiltonian cycle problem is NP-complete for general grid graphs, while several structured classes admit efficient construction algorithms. In this paper, we investigate Hamiltonian cycle construction in rectangular grid graphs containing a three-stacked right-triangular hole. We explore two complementary approaches based on the geometric structure of the hole and the parity properties of rectangular subgraphs. The first approach constructs a Hamiltonian path in the complementary reversed staircase region, extends it into an inner Hamiltonian cycle, and merges it with a cycle in the surrounding graph. The second approach partitions the graph into six rectangular subgraphs and constructs a global Hamiltonian cycle by merging local cycles along compatible boundaries. When odd-sized rectangular subgraphs prevent direct cycle construction, we develop parity-based repartitioning techniques that transform selected configurations into even-sized rectangles. We provide constructions for adjacent and nonadjacent odd-sized configurations and investigate reductions for cases involving multiple odd-sized regions. These results establish a constructive framework for Hamiltonian cycles in grid graphs with staircase-shaped holes and identify the remaining cases requiring further analysis.

cs.CG↗

Convex Hull for Probabilistic Points

We analyze the correctness of an O(n log n) time divide-and-conquer algorithm for the convex hull problem when each input point is a location determined by a normal distribution. We show that the algorithm finds the convex hull of such probabilistic points to precision within some expected correctness determined by a user-given confidence value. In order to precisely explain how correct the resulting structure is, we introduce a new certificate error model for calculating and understanding approximate geometric error based on the fundamental properties of a geometric structure. We show that this new error model implies correctness under a robust statistical error model, in which each point lies within the hull with probability at least that of the user-given confidence value, for the convex hull problem.

cs.CG↗