##### Abstract

Given a finite set of points in a plane, a triangulation is a maximal set of non-intersecting line segments connecting the points. The weight of a triangulation is the sum of the Euclidean lengths of its line segments. Given a set of points in a plane, the minimum weight triangulation problem is to find a triangulation whose weight is minimal. No polynomial time algorithm is known to solve this problem, and it is unknown whether the problem is NP-hard. The current best polynomial time approximation algorithm produces a triangulation that can be 0(log n) times the weight of the optimal triangulation. We propose an algorithm that triangulates a set P, of n points in a plane in 0(n-cubed) time and that never does worse than the greedy triangulation. The algorithm produces an optimal triangulation if the points P are the vertices of a convex polygon. The algorithm has the flavor of a heuristic proposed by Lingas and analysis similar to his can be performed for our algorithm also, but experimental results indicate that our algorithm performs much better than the heuristic of Lingas. The results comparing the optimal triangulation with the performance of our algorithm, the heuristic of Lingas, and the greedy algorithm are within 0(1) of an optimal triangulation. We investigate issues of local optimality pertaining to known triangulation algorithms. We define the notion of k-optimality which suggests an interesting new approach to studying triangulation algorithms. We restate the minimum weight triangulation problem as a graph problem and show that NP-hardness of a closely related graph problem. Finally, we show that the constrained problem of computing the minimum weight of triangulation, given a set of points in a plane and enough edges to form a triangulation, is NP-hard. These results are an advance towards a proof that the minimum weight triangulation problem is NP-hard.