| /external/eigen/doc/ |
| I02_HiPerformance.dox | 30 alpha is a scalar value, and op1, op2 can be transpose, adjoint, conjugate, or the identity.
37 \code m1.noalias() -= s4 * (s1 * m2.adjoint() * (-(s3*m3).conjugate()*s2)) \endcode
39 \code m1.noalias() += (s1*s2*conj(s3)*s4) * m2.adjoint() * m3.conjugate() \endcode
76 m1.noalias() += (m2 * m3).adjoint(); \endcode</td>
79 m1 += temp.adjoint(); \endcode</td>
81 m1.noalias() += m3.adjoint()
82 * m2.adjoint(); \endcode</td>
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| C02_TutorialMatrixArithmetic.dox | 94 The transpose \f$ a^T \f$, conjugate \f$ \bar{a} \f$, and adjoint (i.e., conjugate transpose) \f$ a^* \f$ of a matrix or vector \f$ a \f$ are obtained by the member functions \link DenseBase::transpose() transpose()\endlink, \link MatrixBase::conjugate() conjugate()\endlink, and \link MatrixBase::adjoint() adjoint()\endlink, respectively.
105 For real matrices, \c conjugate() is a no-operation, and so \c adjoint() is equivalent to \c transpose().
107 As for basic arithmetic operators, \c transpose() and \c adjoint() simply return a proxy object without doing the actual transposition. If you do <tt>b = a.transpose()</tt>, then the transpose is evaluated at the same time as the result is written into \c b. However, there is a complication here. If you do <tt>a = a.transpose()</tt>, then Eigen starts writing the result into \c a before the evaluation of the transpose is finished. Therefore, the instruction <tt>a = a.transpose()</tt> does not replace \c a with its transpose, as one would expect:
160 \b Note: for BLAS users worried about performance, expressions such as <tt>c.noalias() -= 2 * a.adjoint() * b;</tt> are fully optimized and trigger a single gemm-like function call.
164 For dot product and cross product, you need the \link MatrixBase::dot() dot()\endlink and \link MatrixBase::cross() cross()\endlink methods. Of course, the dot product can also be obtained as a 1x1 matrix as u.adjoint()*v.
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| TopicLinearAlgebraDecompositions.dox | 128 <td>Self-adjoint</td>
202 <td>Self-adjoint</td>
238 More generally, a matrix \f$ A \f$ is selfadjoint if and only if it is equal to its adjoint \f$ A^* \f$. The adjoint is also called the \em conjugate \em transpose. </dd>
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| QuickReference.dox | 347 transposition \n adjoint \matrixworld</td><td>\code
349 mat1 = mat2.adjoint(); mat1.adjointInPlace();
355 scalar = col1.adjoint() * col2;
356 scalar = (col1.adjoint() * col2).value();\endcode
548 <a href="#" class="top">top</a>\section QuickRef_DiagTriSymm Diagonal, Triangular, and Self-adjoint matrices
615 m3 += s1 * m1.adjoint().triangularView<Eigen::UnitUpper>() * m2
616 m3 -= s1 * m2.conjugate() * m1.adjoint().triangularView<Eigen::Lower>() \endcode
625 L1.triangularView<Eigen::Lower>().adjoint().solveInPlace(M3)
650 m3 -= s1 * m3.adjoint() * m1.selfadjointView<Eigen::Lower>();\endcode
658 M1.selfadjointView<Eigen::Lower>().rankUpdate(M2.adjoint(),-1); \endcod
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| /external/eigen/Eigen/src/Householder/ |
| BlockHouseholder.h | 34 triFactor.col(i).head(i).noalias() = -hCoeffs(i) * vectors.block(i, 0, rs, i).adjoint()
58 VectorsType::MaxColsAtCompileTime,MatrixType::MaxColsAtCompileTime> tmp = V.adjoint() * mat;
60 tmp = T.template triangularView<Upper>().adjoint() * tmp;
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| HouseholderSequence.h | 46 * A.applyOnTheRight(H.adjoint()); // A = A * H^*
47 * A.applyOnTheLeft(H.adjoint()); // A = H^* * A
50 * In addition to the adjoint, you can also apply the inverse (=adjoint), the transpose, and the conjugate operators.
217 /** \brief Adjoint (conjugate transpose) of the Householder sequence. */
218 ConjugateReturnType adjoint() const function in class:Eigen::HouseholderSequence
223 /** \brief Inverse of the Householder sequence (equals the adjoint). */
224 ConjugateReturnType inverse() const { return adjoint(); }
372 /* Necessary for .adjoint() and .conjugate() */
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| /external/eigen/test/ |
| selfadjoint.cpp | 32 VERIFY_IS_APPROX(m3, m3.adjoint());
37 VERIFY_IS_APPROX(m3, m3.adjoint());
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| eigensolver_selfadjoint.cpp | 34 MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
39 MatrixType symmB = b.adjoint() * b + b1.adjoint() * b1;
102 // FIXME tridiag.matrixQ().adjoint() does not work
103 VERIFY_IS_APPROX(MatrixType(symmA.template selfadjointView<Lower>()), tridiag.matrixQ() * tridiag.matrixT().eval() * MatrixType(tridiag.matrixQ()).adjoint());
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| upperbidiagonalization.cpp | 26 MatrixType c = ubd.householderU() * b * ubd.householderV().adjoint();
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| eigensolver_generic.cpp | 32 MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
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| product_trsolve.cpp | 47 VERIFY_TRSM(cmLhs.adjoint() .template triangularView<Lower>(), cmRhs);
51 VERIFY_TRSM(cmLhs.adjoint() .template triangularView<Upper>(), rmRhs);
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| sparse_solvers.cpp | 19 refMat = refMat * refMat.adjoint();
23 refMat += aux * aux.adjoint();
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| dontalign.cpp | 39 v = a.adjoint() * v;
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| triangular.cpp | 73 VERIFY(v2.isApprox(m3.adjoint() * (m1.adjoint().template triangularView<Lower>().solve(v2)), largerEps));
83 VERIFY(m2.isApprox(m3.adjoint() * (m1.adjoint().template triangularView<Lower>().solve(m2)), largerEps));
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| cholesky.cpp | 46 symmCpy += sigma * vec * vec.adjoint();
78 SquareMatrixType symm = a0 * a0.adjoint();
83 symm += a1 * a1.adjoint();
210 RealMatrixType symm = a0 * a0.adjoint();
215 symm += a1 * a1.adjoint();
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| sparse_product.cpp | 137 // test self adjoint products
151 refLo = refUp.adjoint();
152 mLo = mUp.adjoint();
162 VERIFY_IS_APPROX(refS.adjoint(), refS);
163 VERIFY_IS_APPROX(mS.adjoint(), mS);
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| /external/eigen/test/eigen2/ |
| eigen2_eigensolver.cpp | 35 MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
39 MatrixType symmB = b.adjoint() * b + b1.adjoint() * b1;
118 MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
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| eigen2_sparse_solvers.cpp | 19 refMat = refMat * refMat.adjoint();
23 refMat += aux * aux.adjoint();
124 refMat2 += refMat2.adjoint();
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| /external/eigen/lapack/ |
| cholesky.cpp | 62 A.triangularView<Upper>().adjoint().solveInPlace(B);
68 A.triangularView<Lower>().adjoint().solveInPlace(B);
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| lu.cpp | 81 lu.triangularView<Upper>().adjoint().solveInPlace(B);
82 lu.triangularView<UnitLower>().adjoint().solveInPlace(B);
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| /external/eigen/Eigen/src/Core/products/ |
| SelfadjointMatrixMatrix_MKL.h | 29 * Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM.
84 b_tmp = rhs.adjoint(); \
148 b_tmp = rhs.adjoint(); \
210 b_tmp = lhs.adjoint(); \
273 b_tmp = lhs.adjoint(); \
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| /external/eigen/Eigen/src/Core/ |
| SelfAdjointView.h | 104 /** Efficient self-adjoint matrix times vector/matrix product */
114 /** Efficient vector/matrix times self-adjoint matrix product */
129 * a adjoint expression without any overhead. Only the meaningful triangular
143 * call this function with u.adjoint().
173 m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.adjoint();
183 m_matrix.const_cast_derived().template triangularView<OtherPart>() = other.toDenseMatrix().adjoint();
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| /external/eigen/unsupported/test/ |
| mpreal_support.cpp | 26 MatrixXmp S = A.adjoint() * A;
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| /external/eigen/Eigen/src/Eigenvalues/ |
| MatrixBaseEigenvalues.h | 127 return internal::sqrt((m_eval*m_eval.adjoint())
139 * This function computes the L2 operator norm of a self-adjoint matrix. For a
140 * self-adjoint matrix, the operator norm is the largest eigenvalue.
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| /external/eigen/Eigen/src/Eigen2Support/ |
| SVD.h | 551 if(unitary) *unitary = m_matU * m_matV.adjoint();
552 if(positive) *positive = m_matV * m_sigma.asDiagonal() * m_matV.adjoint();
569 if(unitary) *unitary = m_matU * m_matV.adjoint();
570 if(positive) *positive = m_matU * m_sigma.asDiagonal() * m_matU.adjoint();
587 Scalar x = (m_matU * m_matV.adjoint()).determinant(); // so x has absolute value 1
590 if(scaling) scaling->lazyAssign(m_matV * sv.asDiagonal() * m_matV.adjoint());
595 rotation->lazyAssign(m * m_matV.adjoint());
613 Scalar x = (m_matU * m_matV.adjoint()).determinant(); // so x has absolute value 1
616 if(scaling) scaling->lazyAssign(m_matU * sv.asDiagonal() * m_matU.adjoint());
621 rotation->lazyAssign(m * m_matV.adjoint());
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