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protect calls to min and max with parentheses to make Eigen compatible with default windows.h
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@@ -569,7 +569,7 @@ void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Index rows, Index cols, u
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"JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
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"Use the ColPivHouseholderQR preconditioner instead.");
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}
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m_diagSize = std::min(m_rows, m_cols);
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m_diagSize = (std::min)(m_rows, m_cols);
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m_singularValues.resize(m_diagSize);
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m_matrixU.resize(m_rows, m_computeFullU ? m_rows
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: m_computeThinU ? m_diagSize
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@@ -619,8 +619,8 @@ JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsig
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// notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
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// keep us iterating forever.
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using std::max;
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if(max(internal::abs(m_workMatrix.coeff(p,q)),internal::abs(m_workMatrix.coeff(q,p)))
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> max(internal::abs(m_workMatrix.coeff(p,p)),internal::abs(m_workMatrix.coeff(q,q)))*precision)
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if((max)(internal::abs(m_workMatrix.coeff(p,q)),internal::abs(m_workMatrix.coeff(q,p)))
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> (max)(internal::abs(m_workMatrix.coeff(p,p)),internal::abs(m_workMatrix.coeff(q,q)))*precision)
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{
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finished = false;
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@@ -689,7 +689,7 @@ struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
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// A = U S V^*
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// So A^{-1} = V S^{-1} U^*
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Index diagSize = std::min(dec().rows(), dec().cols());
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Index diagSize = (std::min)(dec().rows(), dec().cols());
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typename JacobiSVDType::SingularValuesType invertedSingVals(diagSize);
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Index nonzeroSingVals = dec().nonzeroSingularValues();
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