如果想进行矩阵的element-wise计算,可以把矩阵当作array来计算,通过调用array()方法 // Square each element of the matrix cout << M1 . array () . square () << endl ; // Multiply two matrices element - wise cout << M1 . array () * Matrix4f :: Identity () . array () << endl ; /...
不过这种优化仅限于element-wise的操作,矩阵乘法就不行了。所以eigen对于乘法做了2件事,第一是把常见的乘法操作提取出来,提取为: 单独进行优化。然后因为矩阵乘法需要得到中间变量,所以为了更好的利用expression template,eigen用tree optimizer把矩阵乘法运算往后移。tree optimizer主要就是这样的2条运算顺序调整: // c...
MatrixBase第二和第三个选项是行列的长度，有一项是Dynamic就会用动态内存分配。所以在已知矩阵大小时应尽...
id="matrix-matrix-multiplication">Matrix-Matrix Multiplication Element-wise Product Outer Product
Eigen::Matrix<T, Eigen::Dynamic,1> positive_ordered_free(constEigen::Matrix<T, Eigen::Dynamic,1>& y) {usingEigen::Matrix;usingEigen::Dynamic;usingstan::math::index_type;typedeftypenameindex_type<Matrix<T, Dynamic,1> >::type size_type; ...
(and their internal optimisations) cannot be exploited here; you have to construct your operations with the basic building blocks. These provide matrix creation, I/O, cellwise operations, reduction, multiplication, transformation, decomposition (LU, Householder, Choleski, and Jacobi SVD; these ...
Our subroutine PDSCMM is efficient since the execution time is divided by at most 2 compared with the full multiplication. Moreover, it insures a perfect symmetry of the result matrix. Thus, it can be used in a computation chain without rounding errors problems. Furthermore, the theoretical ...
如果想进行矩阵的element-wise计算,可以把矩阵当作array来计算,通过调用array()方法 代码语言:javascript 复制 // Square each element of the matrixcout<<M1.array().square()<<endl;// Multiply two matrices element - wisecout<<M1.array()*Matrix4f::Identity().array()<<endl;// All relational operato...
(The sequence could be combined into a single rotation by associativity of matrix multiplication.) Let $\psi_f$ be the final state of the quantum computer after all the rotations have been applied. Like any other state, $\psi_f$ is a linear combination of eigenstates. \begin{equa...
不过这种优化仅限于element-wise的操作，矩阵乘法就不行了。所以eigen对于乘法做了2件事，第一是把常见...