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Use EISPACK's strategy re max number of iters in Schur decomposition (bug #479).
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@ -336,6 +336,7 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
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Index iu = m_matT.cols() - 1;
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Index il;
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Index iter = 0; // number of iterations we are working on the (iu,iu) element
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Index totalIter = 0; // number of iterations for whole matrix
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while(true)
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{
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@ -350,9 +351,10 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
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// if iu is zero then we are done; the whole matrix is triangularized
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if(iu==0) break;
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// if we spent too many iterations on the current element, we give up
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// if we spent too many iterations, we give up
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iter++;
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if(iter > m_maxIterations) break;
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totalIter++;
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if(totalIter > m_maxIterations * m_matT.cols()) break;
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// find il, the top row of the active submatrix
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il = iu-1;
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@ -382,7 +384,7 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
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}
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}
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if(iter <= m_maxIterations)
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if(totalIter <= m_maxIterations * m_matT.cols())
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m_info = Success;
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else
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m_info = NoConvergence;
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@ -220,8 +220,9 @@ RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const MatrixType& matrix,
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// Rows il,...,iu is the part we are working on (the active window).
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// Rows iu+1,...,end are already brought in triangular form.
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Index iu = m_matT.cols() - 1;
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Index iter = 0; // iteration count
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Scalar exshift(0); // sum of exceptional shifts
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Index iter = 0; // iteration count for current eigenvalue
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Index totalIter = 0; // iteration count for whole matrix
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Scalar exshift(0); // sum of exceptional shifts
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Scalar norm = computeNormOfT();
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if(norm!=0)
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@ -251,14 +252,15 @@ RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const MatrixType& matrix,
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Vector3s firstHouseholderVector(0,0,0), shiftInfo;
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computeShift(iu, iter, exshift, shiftInfo);
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iter = iter + 1;
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if (iter > m_maxIterations) break;
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totalIter = totalIter + 1;
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if (totalIter > m_maxIterations * matrix.cols()) break;
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Index im;
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initFrancisQRStep(il, iu, shiftInfo, im, firstHouseholderVector);
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performFrancisQRStep(il, im, iu, computeU, firstHouseholderVector, workspace);
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}
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}
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}
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if(iter <= m_maxIterations)
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if(totalIter <= m_maxIterations * matrix.cols())
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m_info = Success;
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else
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m_info = NoConvergence;
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