The eigenvalue of matrix A is a scalar λ such that Av = λv, where v is a non-zero vector. The eigenvalue of A^m is λ^m, where m is a positive integer. This is because if v is an eigenvector of A with eigenvalue λ, then A^m v = λ^m v. Therefore, the eigenvalue of A^m is the eigenvalue of A raised to the power of m. **

To calculate the eigenvalue decomposition of a matrix, first find the eigenvalues of the matrix by solving the characteristic equation det(A - λI) = 0, where A is the matrix, λ is the eigenvalue, and I is the identity matrix. Once the eigenvalues are found, for each eigenvalue, solve the equation (A - λI)v = 0 to find the corresponding eigenvector v. Then, construct the matrix P using the eigenvectors as columns, and the diagonal matrix Λ using the eigenvalues on the diagonal. The eigenvalue decomposition is then given by A = PΛP^(-1), where P^(-1) is the inverse of matrix P. **

Eigenspaces with double eigenvalues are the subspaces of the vector space corresponding to the eigenvectors associated with the double eigenvalue. In other words, they are the set of all vectors that are mapped to a scalar multiple of themselves when the linear transformation is applied. These eigenspaces are important in understanding the behavior of the linear transformation and can help in diagonalizing the matrix representing the transformation. **

No, the minimal polynomial does not directly indicate the geometric multiplicity of an eigenvalue. The geometric multiplicity of an eigenvalue is the dimension of the eigenspace corresponding to that eigenvalue, while the minimal polynomial is the smallest degree monic polynomial that the matrix satisfies. However, the geometric multiplicity of an eigenvalue is always less than or equal to the algebraic multiplicity of the eigenvalue, which is the multiplicity of the eigenvalue as a root of the characteristic polynomial. Therefore, the minimal polynomial can indirectly provide some information about the geometric multiplicity of an eigenvalue. **

No, 0 can be an eigenvalue for a matrix even if it has full rank. A matrix can have 0 as an eigenvalue if it is singular, meaning it does not have an inverse. In this case, the null space of the matrix is nontrivial, and 0 is an eigenvalue with a corresponding eigenvector in the null space. Therefore, 0 can be an eigenvalue for a matrix regardless of its rank. **

If a player scores an own goal, it will count against his team's overall statistics but not against his personal goal statistics. Own goals are not credited to any specific player, so it does not affect his individual goal tally. However, it does impact the team's goals conceded statistic. **

The maximum minimum eigenvalue of Q is 1. This is because the matrix Armxn has orthonormal columns, which means that the dot product of any two columns is 0 if they are different and 1 if they are the same. Additionally, the condition mn and q2aat implies that the matrix Q is a projection matrix onto the subspace spanned by the columns of A. As a result, the maximum minimum eigenvalue of Q is 1, as it represents the maximum amount of variance captured by the projection onto the subspace. **

PSG's goals are scored by a variety of players, as they have a talented and diverse squad. Some of their top goal scorers include Kylian Mbappe, Neymar, and Mauro Icardi. These players are known for their attacking prowess and ability to find the back of the net. Additionally, PSG's midfielders and defenders also contribute to the team's goal-scoring efforts. Overall, PSG's goals come from a combination of their star forwards, as well as contributions from other areas of the team. **