mirror of
https://gitlab.com/libeigen/eigen.git
synced 2026-04-10 11:34:33 +08:00
committed by
Rasmus Munk Larsen
parent
002229ce47
commit
9939a4c6e3
@@ -118,7 +118,7 @@ void SparseLUImpl<Scalar, StorageIndex>::panel_bmod(const Index m, const Index w
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Index isub = lptr + no_zeros;
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Index off = u_rows - segsize;
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for (Index i = 0; i < off; i++) U(i, u_col) = 0;
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for (Index i = 0; i < off; i++) U(i, u_col) = Scalar(0);
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for (Index i = 0; i < segsize; i++) {
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Index irow = glu.lsub(isub);
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U(i + off, u_col) = dense_col(irow);
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@@ -163,14 +163,14 @@ void SparseLUImpl<Scalar, StorageIndex>::panel_bmod(const Index m, const Index w
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for (Index i = 0; i < segsize; i++) {
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Index irow = glu.lsub(isub++);
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dense_col(irow) = U.coeff(i + off, u_col);
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U.coeffRef(i + off, u_col) = 0;
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U.coeffRef(i + off, u_col) = Scalar(0);
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}
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// Scatter l into SPA dense[]
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for (Index i = 0; i < nrow; i++) {
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Index irow = glu.lsub(isub++);
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dense_col(irow) -= L.coeff(i, u_col);
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L.coeffRef(i, u_col) = 0;
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L.coeffRef(i, u_col) = Scalar(0);
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}
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u_col++;
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}
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@@ -401,10 +401,10 @@ void SparseQR<MatrixType, OrderingType>::factorize(const MatrixType& mat) {
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*/
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RealScalar pivotThreshold;
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if (m_useDefaultThreshold) {
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RealScalar max2Norm = 0.0;
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RealScalar max2Norm = RealScalar(0.0);
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for (int j = 0; j < n; j++) max2Norm = numext::maxi(max2Norm, m_pmat.col(j).norm());
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if (max2Norm == RealScalar(0)) max2Norm = RealScalar(1);
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pivotThreshold = 20 * (m + n) * max2Norm * NumTraits<RealScalar>::epsilon();
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pivotThreshold = RealScalar(20 * (m + n)) * max2Norm * NumTraits<RealScalar>::epsilon();
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} else {
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pivotThreshold = m_threshold;
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}
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@@ -497,7 +497,7 @@ void SparseQR<MatrixType, OrderingType>::factorize(const MatrixType& mat) {
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} // End update current column
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Scalar tau = RealScalar(0);
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RealScalar beta = 0;
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RealScalar beta = RealScalar(0);
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if (nonzeroCol < diagSize) {
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// Compute the Householder reflection that eliminate the current column
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@@ -505,16 +505,16 @@ void SparseQR<MatrixType, OrderingType>::factorize(const MatrixType& mat) {
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Scalar c0 = nzcolQ ? tval(Qidx(0)) : Scalar(0);
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// First, the squared norm of Q((col+1):m, col)
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RealScalar sqrNorm = 0.;
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RealScalar sqrNorm = RealScalar(0.);
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for (Index itq = 1; itq < nzcolQ; ++itq) sqrNorm += numext::abs2(tval(Qidx(itq)));
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if (sqrNorm == RealScalar(0) && numext::imag(c0) == RealScalar(0)) {
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beta = numext::real(c0);
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tval(Qidx(0)) = 1;
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tval(Qidx(0)) = Scalar(1);
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} else {
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using std::sqrt;
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beta = sqrt(numext::abs2(c0) + sqrNorm);
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if (numext::real(c0) >= RealScalar(0)) beta = -beta;
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tval(Qidx(0)) = 1;
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tval(Qidx(0)) = Scalar(1);
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for (Index itq = 1; itq < nzcolQ; ++itq) tval(Qidx(itq)) /= (c0 - beta);
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tau = numext::conj((beta - c0) / beta);
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}
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