Why does diagonalization simplify computing powers of a matrix?

A matrix is diagonalizable if and only if it possesses a full set of linearly independent eigenvectors (an eigenbasis). The primary benefit is computational efficiency: calculating high powers of the matrix (e.g., $A^{100}$) becomes trivial because you simply raise the diagonal entries (eigenvalues) to that power, rather than performing repeated matrix multiplication.

Conditions: Matrix has $n$ linearly independent eigenvectors in $n$-dimensional space; Change of basis matrix $P$ formed by eigenvectors is invertible

To compute the $k$-th power of a diagonal matrix, you raise each diagonal entry to the power of $k$. This avoids the complexity of repeated general matrix multiplication.

Conditions: The matrix is diagonal.; The exponent $k$ is a positive integer.

The formula $A^k = PD^kP^{-1}$ represents a sequence of three geometric operations: first, a change of basis using $P^{-1}$ to move to the eigenbasis; second, a diagonal scaling using $D^k$ which stretches or shrinks along the eigenvector directions; and third, a reverse change of basis using $P$ to return to the original coordinate system.

Conditions: $A = PDP^{-1}$ is a valid diagonalization.; $k$ is a positive integer.