本文整理汇总了C++中nox::abstract::MultiVector::multiply方法的典型用法代码示例。如果您正苦于以下问题：C++ MultiVector::multiply方法的具体用法？C++ MultiVector::multiply怎么用？C++ MultiVector::multiply使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nox::abstract::MultiVector的用法示例。
在下文中一共展示了MultiVector::multiply方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1:
void
LOCA::TurningPoint::MooreSpence::ExtendedGroup::lTransNorm(
const NOX::Abstract::MultiVector& n,
NOX::Abstract::MultiVector::DenseMatrix& result) const
{
n.multiply(1.0 / lengthVec->length(), *lengthMultiVec, result);
}示例2: if
void
LOCA::BorderedSolver::HouseholderQR::applyCompactWY(
const NOX::Abstract::MultiVector::DenseMatrix& Y1,
const NOX::Abstract::MultiVector& Y2,
const NOX::Abstract::MultiVector::DenseMatrix& T,
NOX::Abstract::MultiVector::DenseMatrix& X1,
NOX::Abstract::MultiVector& X2,
bool isZeroX1, bool isZeroX2,
bool useTranspose) const
{
if (isZeroX1 && isZeroX2) {
X1.putScalar(0.0);
X2.init(0.0);
return;
}
int m = Y2.numVectors();
Teuchos::ETransp T_flag;
if (useTranspose)
T_flag = Teuchos::TRANS;
else
T_flag = Teuchos::NO_TRANS;
NOX::Abstract::MultiVector::DenseMatrix tmp(m, X2.numVectors());
// Compute Y1^T*X1 + Y2^T*X2
if (!isZeroX2)
X2.multiply(1.0, Y2, tmp);
// Opportunity for optimization here since Y1 is a lower-triangular
// matrix with unit diagonal
if (!isZeroX2 && !isZeroX1)
tmp.multiply(Teuchos::TRANS, Teuchos::NO_TRANS, 1.0, Y1, X1, 1.0);
else if (!isZeroX1)
tmp.multiply(Teuchos::TRANS, Teuchos::NO_TRANS, 1.0, Y1, X1, 0.0);
// Compute op(T)*(Y1^T*X1 + Y2^T*X2)
dblas.TRMM(Teuchos::LEFT_SIDE, Teuchos::UPPER_TRI, T_flag,
Teuchos::NON_UNIT_DIAG, tmp.numRows(), tmp.numCols(), 1.0,
T.values(), T.numRows(), tmp.values(), tmp.numRows());
// Compute X1 = X1 + Y1*op(T)*(Y1^T*X1 + Y2^T*X2)
// Opportunity for optimization here since Y1 is a lower-triangular
// matrix with unit diagonal
if (isZeroX1)
X1.multiply(Teuchos::NO_TRANS, Teuchos::NO_TRANS, 1.0, Y1, tmp, 0.0);
else
X1.multiply(Teuchos::NO_TRANS, Teuchos::NO_TRANS, 1.0, Y1, tmp, 1.0);
// Compute X2 = X2 + Y1*op(T)*(Y1^T*X1 + Y2^T*X2)
if (isZeroX2)
X2.update(Teuchos::NO_TRANS, 1.0, Y2, tmp, 0.0);
else
X2.update(Teuchos::NO_TRANS, 1.0, Y2, tmp, 1.0);
}示例3: getDX
NOX::Abstract::Group::ReturnType
LOCA::MultiContinuation::ConstraintInterfaceMVDX::multiplyDX(
double alpha,
const NOX::Abstract::MultiVector& input_x,
NOX::Abstract::MultiVector::DenseMatrix& result_p) const
{
if (!isDXZero()) {
const NOX::Abstract::MultiVector* dgdx = getDX();
input_x.multiply(alpha, *dgdx, result_p);
}
else
result_p.putScalar(0.0);
return NOX::Abstract::Group::Ok;
}示例4: u
void
LOCA::BorderedSolver::HouseholderQR::applyHouseholderVector(
const NOX::Abstract::MultiVector::DenseMatrix& V1,
const NOX::Abstract::MultiVector& V2,
double beta,
NOX::Abstract::MultiVector::DenseMatrix& A1,
NOX::Abstract::MultiVector& A2)
{
int nColsA = A2.numVectors();
// Compute u = V2^T * A2
NOX::Abstract::MultiVector::DenseMatrix u(1, nColsA);
A2.multiply(1.0, V2, u);
// Compute u = u + V1^T * A_P
u.multiply(Teuchos::TRANS, Teuchos::NO_TRANS, 1.0, V1, A1, 1.0);
// Compute A1 = A1 - b*V1*u
A1.multiply(Teuchos::NO_TRANS, Teuchos::NO_TRANS, -beta, V1, u, 1.0);
// Compute A2 = A2 - b*V2*u
A2.update(Teuchos::NO_TRANS, -beta, V2, u, 1.0);
}示例5: alpha
//.........这里部分代码省略.........
Teuchos::RCP<NOX::Abstract::MultiVector> tmp =
result_null.clone(NOX::ShapeCopy);
status = group->computeDwtJnDxMulti(result_null, *nullVector, *tmp);
finalStatus =
globalData->locaErrorCheck->combineAndCheckReturnTypes(status,
finalStatus,
callingFunction);
// compute [F 0 0] - (Jv)_x^T[A B u]
tmp->update(1.0, input_x, -1.0);
// verify underlying Jacobian is valid
if (!group->isJacobian()) {
status = group->computeJacobian();
finalStatus =
globalData->locaErrorCheck->combineAndCheckReturnTypes(status,
finalStatus,
callingFunction);
}
// Solve |J^T v||C D E| = |F - (Jv)_x^T A -(Jv)_x^T B -(Jv)_x^T u|
// |u^T 0||c d e| | 0 0 0 |
status =
transposeBorderedSolver->applyInverseTranspose(params,
tmp.get(),
NULL,
result_x,
tmp_mat_2);
finalStatus =
globalData->locaErrorCheck->combineAndCheckReturnTypes(status, finalStatus,
callingFunction);
Teuchos::RCP<NOX::Abstract::MultiVector> C =
result_x.subView(index_input);
Teuchos::RCP<NOX::Abstract::MultiVector> D =
result_x.subView(index_dp);
Teuchos::RCP<NOX::Abstract::MultiVector> E =
result_x.subView(index_null);
double d = tmp_mat_2(0, m);
double e = tmp_mat_2(0, m+1);
// compute (Jv)_p^T*[A B u]
NOX::Abstract::MultiVector::DenseMatrix t1(1,m+2);
result_null.multiply(1.0, *dJndp, t1);
// compute f_p^T*[C D E]
NOX::Abstract::MultiVector::DenseMatrix t2(1,m+2);
result_x.multiply(1.0, *dfdp, t2);
// compute f_p^T*u
double fptu = uVector->innerProduct((*dfdp)[0]);
// Fill coefficient arrays
double M[9];
M[0] = st; M[1] = -e; M[2] = t1(0,m+1) + t2(0,m+1);
M[3] = 0.0; M[4] = st; M[5] = fptu;
M[6] = -b; M[7] = -d; M[8] = t1(0,m) + t2(0,m);
// Compute RHS
double *R = new double[3*m];
for (int i=0; i<m; i++) {
R[3*i] = tmp_mat_1(0,i);
R[3*i+1] = tmp_mat_2(0,i);
R[3*i+2] = result_param(0,i) - t1(0,i) - t2(0,i);
}
// Solve M*P = R
int three = 3;
int piv[3];
int info;
Teuchos::LAPACK<int,double> L;
L.GESV(three, m, M, three, piv, R, three, &info);
if (info != 0) {
globalData->locaErrorCheck->throwError(
callingFunction,
"Solve of 3x3 coefficient matrix failed!");
return NOX::Abstract::Group::Failed;
}
NOX::Abstract::MultiVector::DenseMatrix alpha(1,m);
NOX::Abstract::MultiVector::DenseMatrix beta(1,m);
for (int i=0; i<m; i++) {
alpha(0,i) = R[3*i];
beta(0,i) = R[3*i+1];
result_param(0,i) = R[3*i+2];
}
// compute A = A + B*z + alpha*u (remember A is a sub-view of result_null)
A->update(Teuchos::NO_TRANS, 1.0, *B, result_param, 1.0);
A->update(Teuchos::NO_TRANS, 1.0, *uMultiVector, alpha, 1.0);
// compute C = C + D*z + alpha*E + beta*u
// (remember C is a sub-view of result_x)
C->update(Teuchos::NO_TRANS, 1.0, *D, result_param, 1.0);
C->update(Teuchos::NO_TRANS, 1.0, *E, alpha, 1.0);
C->update(Teuchos::NO_TRANS, 1.0, *uMultiVector, beta, 1.0);
delete [] R;
return finalStatus;
}