vi_gen_reduced_data.m

function reduced_data = vi_gen_reduced_data(model,detailed_data)
%function reduced_data = vi_gen_reduced_data(model,detailed_data)
%
% function generating all parameter independent reduced data for
% the RB-Method for Parametrized Variational Inequalities.
%
% Generated fields of reduced_data:
%  AN_comp: components of matrix AN
%  fN_comp: components of vector fN
%  gN_comp: components of vector gN
%  BN: this matrix needs no decomposition

% I.Maier 31.05.2011

reduced_data = [];

% due to lack of full offline-online for error estimator, large
% data is required...
if model.enable_error_estimator
  reduced_data = detailed_data;
%  reduced_data.X = detailed_data.X;
%  reduced_data.dx = detailed_data.dx;
%  reduced_data.RB_U = detailed_data.RB_U;
%  reduced_data.RB_L = detailed_data.RB_L;
end;

model.decomp_mode = 1;

[A_comp,B,f_comp,g_comp] = model.operators(model,detailed_data);

Q_A = length(A_comp);
Q_f = length(f_comp);
Q_g = length(g_comp);

reduced_data.AN_comp = cell(1,Q_A);
for q = 1:Q_A
  AN_comp_q = detailed_data.RB_U' * A_comp{q} * ...
      detailed_data.RB_U;
  % symmetrize
  reduced_data.AN_comp{q} = 0.5*(AN_comp_q + AN_comp_q');  
end;

reduced_data.fN_comp = cell(1,Q_f);
for q = 1:Q_f
  reduced_data.fN_comp{q} = detailed_data.RB_U' * f_comp{q};
end;

reduced_data.BN = detailed_data.RB_U' * B * detailed_data.RB_L;

reduced_data.gN_comp = cell(1,Q_g);
for q = 1:Q_g
  reduced_data.gN_comp{q} = detailed_data.RB_L' * g_comp{q};
end;

reduced_data.N = size(detailed_data.RB_U,2);