%-------------------------------------------------------------------------------
% SuiteSparse Matrix Collection, Tim Davis
% https://sparse.tamu.edu/Hardesty/Hardesty2
% name: Hardesty/Hardesty2
% [surface fitting problem (smaller version)]
% id: 2832
% date: 2015
% author: S. Hardesty
% ed: T. Davis
% fields: name title A b id date author ed kind notes
% kind: computer graphics/vision problem
%-------------------------------------------------------------------------------
% notes:
% Surface fitting problem for visualization, Sean Hardesty              
%                                                                       
% Visualization of 3D structures in the earth                           
%                                                                       
% The Hardesty3 matrix is an interpolation matrix stacked above a       
% weighted Laplacian, to to fit a surface z(x,y) to a set of points     
% in R^3 subject to a smoothness constraint enforced via regularization.
% Hardesty2 is a smaller version of this problem.                       
%                                                                       
% For the big matrix (Hardesty/Hardesty3), sparse QR (via SuiteSparseQR,
% or SPQR) finds an R factor and a set of Householder vectors (Q.H) with
% about 150 million nonzeros.  Sparse LU factorization (with UMFPACK    
% v5.7.1) sees very high fillin (about 2.5 billion nonzeros in L+U).    
%                                                                       
% The Hardesty1 matrix is a simple discretization of a 2D biharmonic    
% operator with some Lagrange multiplier constraints used for smoothing.
%-------------------------------------------------------------------------------
