Convex hull overlap is a measure of the agreement between a predicted convex hull and a reference convex hull.
Convex hull overlap measures how closely a predicted convex hull agrees with a reference convex hull. It evaluates geometric output alongside model accuracy. section 11.9
Use the metric to:
Compare the hull formed from predicted point indices with the true convex hull. section 11.9
A perfect match
For a 50-point test sample, the predicted indices exactly match the true indices, producing 100.00% hull overlap. Across test sizes, overlap ranges from 43.7% for 5 points to 92.7% for 20 points.
How Optimization Algorithms: AI techniques for design, planning, and control problems covers it
How deep it goes
Measures reported overlap and supports visual comparison, but does not define the underlying geometric calculation.
Examples
Uses varying point counts, reported overlap percentages, matching predicted and true indices, and SciPy-versus-ConvexNet plots.
What it assumes
Assumes predicted point sequences, reference hull indices, and basic model evaluation with validation and test datasets.
What you can do after
Evaluate pointer-network convex-hull predictions across sample sizes and inspect their geometric agreement with reference hulls.
How it connects
Connects convex hulls with pointer networks, model accuracy, validation, and test-set evaluation.
This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.
