Hard in Theory, Easy in Practice: Solving Large Minimum-Weight Triangulation Instances to Provable Optimality - Non-random Instance Dataset

The non-random point sets used in our work on Minimum Weight Triangulation. Among instances generated to have multiple components in their LMT skeleton and other constructed instances, contains instances from various sources (but potentially brought into a different format), including the points from TSPLIB instances (http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/), VLSI instances (https://www.math.uwaterloo.ca/tsp/vlsi/index.html) and some point sets of Salzburg polygon database polygons (https://zenodo.org/records/3784789).