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Fixed bug in diagonal*vector product

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This commit is contained in:
babrodtk 2015-09-01 14:10:18 +02:00
parent 9855d7340f
commit 4c82e9abc7
1 changed files with 14 additions and 27 deletions
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41
opm/autodiff/AutoDiffMatrix.hpp
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@ -84,6 +84,7 @@ namespace Opm
diag_(d.diagonal().array().data(), d.diagonal().array().data() + d.rows()),
sparse_()
{
assert(rows_ == cols_);
}
@ -145,9 +146,9 @@ namespace Opm
case I:
return addII(*this, rhs);
case D:
return rhs + (*this);
return addDI(rhs, *this);
case S:
return rhs + (*this);
return addSI(rhs, *this);
default:
OPM_THROW(std::logic_error, "Invalid AutoDiffMatrix type encountered: " << rhs.type_);
}
@ -160,7 +161,7 @@ namespace Opm
case D:
return addDD(*this, rhs);
case S:
return rhs + (*this);
return addSD(rhs, *this);
default:
OPM_THROW(std::logic_error, "Invalid AutoDiffMatrix type encountered: " << rhs.type_);
}
@ -189,18 +190,7 @@ namespace Opm
case Z:
return AutoDiffMatrix(rows_, rhs.cols_);
case I:
switch (rhs.type_) {
case Z:
return rhs;
case I:
return rhs;
case D:
return rhs;
case S:
return rhs;
default:
OPM_THROW(std::logic_error, "Invalid AutoDiffMatrix type encountered: " << rhs.type_);
}
return rhs;
case D:
switch (rhs.type_) {
case Z:
@ -251,7 +241,7 @@ namespace Opm
AutoDiffMatrix& operator-=(const AutoDiffMatrix& rhs)
{
*this = *this + rhs * -1.0;
*this = *this + (rhs * -1.0);
return *this;
}
@ -341,7 +331,10 @@ namespace Opm
case I:
return rhs;
case D:
return Eigen::Map<const Eigen::VectorXd>(diag_.data(), rows_) * rhs;
{
const Eigen::VectorXd d = Eigen::Map<const Eigen::VectorXd>(diag_.data(), rows_);
return d.cwiseProduct(rhs);
}
case S:
return sparse_ * rhs;
default:
@ -393,11 +386,8 @@ namespace Opm
{
assert(lhs.type_ == S);
assert(rhs.type_ == I);
AutoDiffMatrix retval;
retval.type_ = S;
retval.rows_ = lhs.rows_;
retval.cols_ = rhs.cols_;
retval.sparse_ = lhs.sparse_ + spdiag(Eigen::VectorXd::Ones(lhs.rows_));;
AutoDiffMatrix retval = lhs;
retval.sparse_ += spdiag(Eigen::VectorXd::Ones(lhs.rows_));;
return retval;
}
@ -405,11 +395,8 @@ namespace Opm
{
assert(lhs.type_ == S);
assert(rhs.type_ == D);
AutoDiffMatrix retval;
retval.type_ = S;
retval.rows_ = lhs.rows_;
retval.cols_ = rhs.cols_;
retval.sparse_ = lhs.sparse_ + spdiag(rhs.diag_);
AutoDiffMatrix retval = lhs;
retval.sparse_ += spdiag(rhs.diag_);
return retval;
}