Lecture I:
Introduction: distance matrices, scattered data interpolation, basic
MATLAB routines, approximation in high dimensions and using different
designs
Lecture II:
RBF interpolation and MLS approximation: approximate approximation and
the connection between the two methods via residual iteration
Lecture III:
Dealing with Ill-Conditioned RBF Systems: various preconditioning and
regularization ideas
Lecture IV:
``Optimal" shape parameters for RBF approximation methods
Lecture V:
RBF collocation and polynomial pseudospectral methods
Lecture VI:
Nonlinear Problems: Nash iteration and implicit smoothing