Aⁿ means multiplying A by itself n times. Set the exponent above, press A^n, and the result appears; the steps panel shows each intermediate product, which is usually what you need when checking work by hand.
What powers are used for
Only square matrices can be raised to a power — A·A requires the columns of A to match its own rows. The operation shows up wherever a process repeats: in a Markov chain, if P holds the transition probabilities for one step, then Pⁿ holds them for n steps, and the entry in row i, column j is the probability of moving from state i to state j in exactly n moves.
Adjacency matrices work the same way. If A records which vertices of a graph are connected, the entry (i, j) of Aⁿ counts the walks of length n from i to j. Two conventions catch people out: A⁰ is the identity matrix, not a matrix of zeros, and A¹ is A itself.
Worked example
| 1 | 1 |
| 0 | 1 |
| 1 | 2 |
| 0 | 1 |
| 1 | n |
| 0 | 1 |
Squaring is not entrywise: the top-right entry is 1·1 + 1·1 = 2, not 1² = 1. The calculator uses exponentiation by squaring, so A¹⁶ costs four multiplications rather than fifteen — the reason a large exponent returns as quickly as a small one.
Common mistakes
- Raising each entry to the power. A² is A·A, not the matrix of squared entries. The two agree only for diagonal matrices.
- Assuming (A·B)ⁿ = Aⁿ·Bⁿ. True only when A and B commute, which is rare.
- Expecting A⁰ to be zero. It is the identity, by the same convention that makes x⁰ = 1.
- Asking for a negative power of a singular matrix. A⁻ⁿ means (A⁻¹)ⁿ, so it exists only when the inverse does.