@@ -325,7 +325,11 @@ For more details on how to use Adaptive, we recommend reading the tutorial insid
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@@ -325,7 +325,11 @@ For more details on how to use Adaptive, we recommend reading the tutorial insid
# Possible extensions
# Possible extensions
#### Anisotropic triangulation would improve the algorithm.
#### Anisotropic triangulation would improve the algorithm.
[@dyn1990data]
The current implementation of choosing the candidate point inside a simplex (triangle for 2D) with the highest loss, for the `LearnerND`, works by either picking a point (1) in the center of the simplex or (2) by picking a point on the longest edge of the simplex.
The choice depends on the shape of the simplex, where the algorithm tries to create regular simplices.
Alternatively, a good strategy is choosing points somewhere on the edge of a triangle such that the simplex aligns with the gradient of the function; creating an anisotropic triangulation[@dyn1990data].
This is a similar approach to the anisotropic meshing techniques mentioned in the literature review.
We have started to implement this, however, there are still some unsolved problems.
#### Learning stochastic functions is a promising direction.
#### Learning stochastic functions is a promising direction.
