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422234dc3   daniau   git-svn-id: https... 
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  umfpack_demo
  
  Enter the printing level for UMFPACK's output statistics:
  0: none, 1: errors only, 2: statistics, 4: print some outputs
  5: print all output [default is 1]: 
  
  --------------------------------------------------------------
  Factor and solve a small system, Ax=b, using default parameters
  Solving Ax=b via UMFPACK:
  Solving Ax=b via MATLAB:
  Difference between UMFPACK and MATLAB solution: 0
  
  --------------------------------------------------------------
  
  Factorizing [L, U, P, Q, R] = umfpack2 (A)
  
  P * (R\A) * Q - L*U should be zero:
  norm (P*(R\A)*Q - L*U, 1) = 2.77556e-16 (exact) 1.21864e-16 (estimated)
  
  Solution to Ax=b via UMFPACK factorization:
  x = Q * (U \ (L \ (P * (R \ b))))
  
  UMFPACK flop count: 2453
  
  Factorizing [L, U, P] = lu (A (:, q))
  If you are using a version of MATLAB prior to V6.0, then the
  following statement (q = colamd (A)) may fail.  Either download
  colamd from http://www.cise.ufl.edu/research/sparse, upgrade to
  MATLAB V6.0 or later, or replace the statement with
  q = colmmd (A) ;
  
  Solution to Ax=b via MATLAB factorization:
  x = U \ (L \ (P * b)) ; x (q) = x ;
  Difference between UMFPACK and MATLAB solution: 5.55112e-15
  
  MATLAB LU flop count: 3160
  
  --------------------------------------------------------------
  Solve A'x=b:
  Solving A'x=b via UMFPACK:
  Solving A'x=b via MATLAB:
  Difference between UMFPACK and MATLAB solution: 1.77636e-15
  
  --------------------------------------------------------------
  Compute C = A', and compute the LU factorization of C.
  Factorizing A' can sometimes be better than factorizing A itself
  (less work and memory usage).  Solve C'x=b; the solution is the
  same as the solution to Ax=b for the original A.
  
  P * (R\C) * Q - L*U should be zero:
  norm (P*(R\C)*Q - L*U, 1) = 1.17961e-16 (exact) 5.60533e-17 (estimated)
  
  Solution to Ax=b via UMFPACK, using the factors of C:
  x = R \ (P' * (L' \ (U' \ (Q' * b)))) ;
  Solution to Ax=b via MATLAB:
  Difference between UMFPACK and MATLAB solution: 3.55271e-15
  
  --------------------------------------------------------------
  
  Solve AX=B, where B is n-by-10, and sparse
  Difference between UMFPACK and MATLAB solution: 6.3926e-14
  
  --------------------------------------------------------------
  
  Solve AX=B, where B is n-by-10, and sparse, using umfpack_btf
  Difference between UMFPACK and MATLAB solution: 4.41347e-14
  
  --------------------------------------------------------------
  
  Solve A'X=B, where B is n-by-10, and sparse
  Difference between UMFPACK and MATLAB solution: 8.90054e-14
  
  --------------------------------------------------------------
  det(A): -4.07453e-05  UMFPACK determinant: -4.07453e-05
  diary off