Lines Matching full:matrix
3 /** \eigenManualPage TutorialMatrixArithmetic Matrix and vector arithmetic
12 Eigen offers matrix/vector arithmetic operations either through overloads of common C++ arithmetic operators such as +, -, *,
14 For the Matrix class (matrices and vectors), operators are only overloaded to support
15 linear-algebraic operations. For example, \c matrix1 \c * \c matrix2 means matrix-matrix product,
41 \li binary operator * as in \c matrix*scalar
42 \li binary operator * as in \c scalar*matrix
43 \li binary operator / as in \c matrix/scalar
44 \li compound operator *= as in \c matrix*=scalar
45 \li compound operator /= as in \c matrix/=scalar
81 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.
116 \section TutorialArithmeticMatrixMul Matrix-matrix and matrix-vector multiplication
118 Matrix-matrix multiplication is again done with \c operator*. Since vectors are a special
119 case of matrices, they are implicitly handled there too, so matrix-vector product is really just a special
120 case of matrix-matrix product, and so is vector-vector outer product. Thus, all these cases are handled by just
135 aliasing issues, be reassured for now: Eigen treats matrix multiplication as a special case and takes care of
141 If you know your matrix product can be safely evaluated into the destination matrix without aliasing issue, then you can use the \link MatrixBase::noalias() noalias()\endlink function to avoid the temporary, e.g.:
151 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.
166 Eigen also provides some reduction operations to reduce a given matrix or vector to a single value such as the sum (computed by \link DenseBase::sum() sum()\endlink), product (\link DenseBase::prod() prod()\endlink), or the maximum (\link DenseBase::maxCoeff() maxCoeff()\endlink) and minimum (\link DenseBase::minCoeff() minCoeff()\endlink) of all its coefficients.
177 The \em trace of a matrix, as returned by the function \link MatrixBase::trace() trace()\endlink, is the sum of the diagonal coefficients and can also be computed as efficiently using <tt>a.diagonal().sum()</tt>, as we will see later on.
207 v = m * v; // Run-time assertion failure here: "invalid matrix product"
