How do you compute the k-th power of a diagonal 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

Diagonalization simplifies matrix powers because raising a diagonal matrix to a positive integer power only requires raising each diagonal entry to that power, which is computationally simpler than repeated general matrix multiplication. By expressing a matrix $A$ as $PDP^{-1}$, the power $A^k$ becomes $PD^kP^{-1}$, where the adjacent $P^{-1}P$ factors cancel out, reducing the complexity of the calculation.

Conditions: The matrix $A$ is diagonalizable, meaning $A = PDP^{-1}$ with $P$ invertible.; 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.