A tensor is a multi-dimensional array, and in Eigen, the Tensor
class can be used to represent a tensor of any rank (number of dimensions). Here are some examples of operations on a 4th-order tensor (tensor of rank 4) in Eigen:
1. Creating a 4th-order tensor:
#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main() {
Tensor<double, 4> A(2, 3, 4, 5);
A.setRandom(); // Set random values for the elements of A
cout << "A = " << endl << A << endl;
// Output: A is a 2 x 3 x 4 x 5 tensor with random values for its elements
return 0;
}
2. Accessing elements of a 4th-order tensor:
#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main() {
Tensor<double, 4> A(2, 3, 4, 5);
A.setRandom();
// Access element at index (i, j, k, l) of the tensor
double element = A(i, j, k, l);
return 0;
}
3. Performing element-wise operations on a 4th-order tensor:
#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main() {
Tensor<double, 4> A(2, 3, 4, 5);
A.setRandom();
Tensor<double, 4> B(2, 3, 4, 5);
B.setRandom();
// Element-wise addition
Tensor<double, 4> C = A + B;
cout << "C = " << endl << C << endl;
// Element-wise subtraction
Tensor<double, 4> D = A - B;
cout << "D = " << endl << D << endl;
// Element-wise multiplication
Tensor<double, 4> E = A * B;
cout << "E = " << endl << E << endl;
return 0;
}
Inner product and cross product are operations that are typically defined for vectors and matrices, rather than for higher-order tensors. However, there are some ways in which you can define inner product and cross product for tensors of any rank in Eigen.
1. Inner product:
One way to define an inner product for tensors is to use the contract
function, which performs a contraction over one or more dimensions of the tensor. For example, the inner product of two tensors A
and B
can be defined as the tensor C
obtained by contracting A
and B
over all dimensions:
#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
int main() {
Tensor<double, 4> A(2, 3, 4, 5);
A.setRandom();
Tensor<double, 4> B(2, 3, 4, 5);
B.setRandom();
// Inner product (contraction over all dimensions)
Tensor<double, 0> C = A.contract(B, {0, 1, 2, 3});
cout << "C = " << C(0) << endl;
return 0;
}
2. Cross product:
The cross product is a binary operation that takes two vectors as input and produces a third vector as output. Since a tensor of rank 4 is not a vector, there is no direct way to define a cross product for two 4th-order tensors. However, you can define a cross product for a pair of 3rd-order tensors by treating them as “vectors” of matrices, and then using the matrix cross product to compute the result.
Here is an example of how you can define a cross product for a pair of 3rd-order tensors in Eigen:
#include <Eigen/Dense>
#include <iostream>
using namespace Eigen;
using namespace std;
Tensor<double, 3> tensorCrossProduct(Tensor<double, 3> A, Tensor<double, 3> B) {
Tensor<double, 3> C(A.dimension(0), A.dimension(1), A.dimension(2));
for (int i = 0; i < A.dimension(0); i++) {
for (int j = 0; j < A.dimension(1); j++) {
MatrixXd a = A.chip(i, 0).chip(j, 1);
MatrixXd b = B.chip(i, 0).chip(j, 1);
MatrixXd c = a.cross(b);
C.chip(i, 0).chip(j, 1) = c;
}
}
return C;
}
int main() {
Tensor<double, 3> A(2, 3, 3);
A.setRandom();
Tensor<double, 3> B(2, 3, 3);
B.setRandom();
// Cross product
Tensor<double, 3> C = tensorCrossProduct(A, B);
cout << "C = " << endl << C << endl;
return 0;
}
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