sp\\: is A triangular?  yes.\nsp\\: do a triangular solve.\n  no.\nsp\\: is A morally triangular?  yes.\nsp\\: permute and solve.\n  no.\nsp\\: is A a candidate for Cholesky (symmetric, real positive or negative diagonal)? sp\\: A is not positive definite.\n  no.\n sp\\: is UMFPACK's numeric LU factorization successful?  yes.\n sp\\: original LU factor and solve two triangular systems.\n sp\\: column minimum degree.\n sp\\: sprealloc in sptsolve: {0} {1} {2}  no,\n tridiagonal solve failed at pivot {0}.\n sp\\: is LAPACK's banded solver successful? sp\\: bandwidth = {0}+1+{1}.\n sp\\: is band density ({0}) ]]> bandden ({1}) to try banded solver? sp\\: is it ok to solve real tridiagonal A without pivoting? sp\\: is A diagonal?  yes.\nsp\\: do a diagonal solve.\n sp\\: is CHOLMOD's symbolic Cholesky factorization (with automatic reordering) successful? sp\\: is CHOLMOD's symbolic Cholesky factorization (without reordering) successful? sp\\: is CHOLMOD's numeric Cholesky factorization successful?  yes.\nsp\\: is CHOLMOD's triangular solve successful? sp\\: use Unsymmetric MultiFrontal PACKage sp\\: UMFPACK's factorization was successful.\n sp\\: UMFPACK's solve was successful.\n sp\\: UMFPACK was NOT successful.\n sp\\: Sparse QR solution via SuiteSparseQR.\n sp\\: But A is real symmetric; try MA57.\n with automatic reordering.\n sp\\: However, UMFPACK may have failed to produce a correct factorization\n possibly due to aggressive pivot tolerances or because the problem\n is ill-conditioned.\n sp\\: These tolerances were:\n \tPivot Tolerance: {0}\n \tDiagonal Pivot Tolerance: {0}\n We will factor the matrix again but use standard partial pivoting.\n CHOLMOD's symbolic Cholesky factorization.\n CHOLMOD's numeric Cholesky factorization.\n Extract L' from CHOLMOD.\n Extract permutation from CHOLMOD.\n infinite matrix (column {0}): diagonal entry {0} {1} tolerance\n predicted nonzeros: {0}\n predicted flops: {0}\n predicted height: {0}\n predicted front size: {0}\n Extract c from SuiteSparseQR.\n Compute QR factorization via SuiteSparseQR.\n Extract Q from SuiteSparseQR.\n Extract permutation from SuiteSparseQR.\n Extract R from SuiteSparseQR.\n spmlrcm: root = {0}\n spmlrcm: component size = {0}, levels = {1}\n spmlrcm: this is a bug, dimension is n = {0} but component sizes add to num = {0}\n components: {0}\n bandwidth: {0}\n profile: {0}\n pseudodiameter: {0}\n rootls: {0} levels\n colmmd: withholding {0} dense rows.\n stage 0:   \n stage {0}: amalgamated   {0}   reduced eliminated threshold {0} selected mmd: threshold = {0} * mindegree + {1};\n \tusing exact degrees in A'*A;\n \tusing approximate degrees in A'*A;\n \tsupernode amalgamation initially and every {0} stages;\n \tsupernode amalgamation initially only;\n \tsupernode amalgamation after every {0} stages;\n \tno supernode amalgamation;\n \trow reduction initially and every {0} stages;\n \trow reduction initially only;\n \trow reduction after every {0} stages;\n \tno row reduction;\n \twithhold rows at least {0} dense in colmmd;\n \tno row withholding in colmmd;\n mmd: {0} stages.\n drop tolerance: {0}\n replace zero diagonals of U: modified incomplete LU: pivot threshold: {0}\n sprealloc in lu(V): {0} {1} {2}  {0}\n sprealloc in lu(L): {0} {1} {2} sprealloc in lu(U): {0} {1} {2} Pruned nzs: {0}\n L nonzeros: {0}\n U nonzeros: {0}\n bug in spchol: analyze didn't allocate enough storage\n sp\\: However, UMFPACK may have failed to solve with a desired backward error\n possibly due to aggressive pivot tolerances or because the problem\n is ill-conditioned.\n sp\\: The achieved backward errors were:\n \tsparse backward error omega1: {0}\n \tsparse backward error omega2: {0}\n