Convex Hull

convex_hull(points; predicates::AbstractPredicateKernel=AdaptiveKernel(), IntegerType::Type{I}=Int) where {I} -> ConvexHull

Computes the convex hull of points. The monotone chain algorithm is used.

Arguments

• points: The set of points.

Keyword Arguments

• IntegerType=Int: The integer type to use for the vertices.
• predicates::AbstractPredicateKernel=AdaptiveKernel(): Method to use for computing predicates. Can be one of FastKernel, ExactKernel, and AdaptiveKernel. See the documentation for a further discussion of these methods.

Output

• ch: The ConvexHull.

convex_hull!(tri::Triangulation; reconstruct=has_boundary_nodes(tri), predicates::AbstractPredicateKernel=AdaptiveKernel())

Updates the convex_hull field of tri to match the current triangulation.

Arguments

• tri::Triangulation: The Triangulation.

Keyword Arguments

• reconstruct=has_boundary_nodes(tri): If true, then the convex hull is reconstructed from scratch, using convex_hull on the points. Otherwise, computes the convex hull using the ghost triangles of tri. If there are no ghost triangles but reconstruct=true, then the convex hull is reconstructed from scratch.
• predicates::AbstractPredicateKernel=AdaptiveKernel(): Method to use for computing predicates. Can be one of FastKernel, ExactKernel, and AdaptiveKernel. See the documentation for a further discussion of these methods.

convex_hull!(ch::ConvexHull{P,I}; predicates::AbstractPredicateKernel=AdaptiveKernel()) where {P,I}

Using the points in ch, computes the convex hull in-place.

The predicates keyword argument determines how predicates are computed, and should be one of ExactKernel, FastKernel, and AdaptiveKernel (the default). See the documentation for more information about these choices.

See also convex_hull.