ELIAS EBNER

  • Home
  • Blog
  • Courses
  • About

Linear Algebra

  1. Vectors
    1. Scalars
    2. Vectors vs Sets
    3. Addition and Subtraction
    4. Scalar Multiplication
    5. Zero Vectors
    6. Linear Combinations
    7. Real Dot Product
    8. Length of a Vector
    9. Orthogonal Vectors
    10. Parallel Vectors
    11. Angle Between Vectors
    12. Unit Vectors
  2. Matrices
    1. Notation
    2. Indexing
    3. Submatrices
    4. Matrix-by-Vector Product
    5. Addition and Subtraction
    6. Scalar Multiplication
    7. Transpose
    8. Symmetries
    9. Matrix Multiplication
    10. Identity Matrix
    11. Non-Negative Integer Powers
    12. Reverse Order Law of Transposition
  3. Linear Systems
    1. Inverse Matrices
    2. Singular Matrices
    3. Linear Dependence
    4. Solutions
  4. Planes
    1. Vector Cross Product
  5. Gaussian Elimination
Linear Algebra β€ΊMatrices β€ΊNon-Negative Integer Powers

Non-Negative Integer Powers of Matrices

Just like with scalars, non-negative integer powers of matrices are defined in terms of multiplication.

That is

An=AAβ‹―A⏟nΒ times.A^n = \underbrace{AA \cdots A}_{n \text{ times}}.An=nΒ timesAAβ‹―A​​.

Just like any scalar aaa to the power of 000 is defined to be the multiplicative identity (111), that is also the case with matrices. A matrix to the 000th power always equals the identity matrix of that size.

That is

AnΓ—n0=In.A_{n \times n}^0 = I_n.AnΓ—n0​=In​.

It is important to note that powers of non-square matrices are not defined, since conformability would not be satisfied. If you wanted to multiply a matrix of size AmΓ—nA_{m \times n}AmΓ—n​ with itself, this would only be defined when m=nm = nm=n. Therefore, AAA must be a square matrix.

Example

To illustrate, here is a concrete numerical example. Take the matrix

A=[1234].A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix}.A=[13​24​].

We compute

AA=[1234][1234]=[1β‹…1+2β‹…31β‹…2+2β‹…43β‹…1+4β‹…33β‹…2+4β‹…4]=[7101522].\begin{align*} AA &= \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} \begin{bmatrix} 1 & 2 \\ 3 & 4 \end{bmatrix} \\ &= \begin{bmatrix} 1 \cdot 1 + 2 \cdot 3 & 1 \cdot 2 + 2 \cdot 4 \\ 3 \cdot 1 + 4 \cdot 3 & 3 \cdot 2 + 4 \cdot 4 \end{bmatrix} \\ &= \begin{bmatrix} 7 & 10 \\ 15 & 22 \end{bmatrix}. \end{align*}AA​=[13​24​][13​24​]=[1β‹…1+2β‹…33β‹…1+4β‹…3​1β‹…2+2β‹…43β‹…2+4β‹…4​]=[715​1022​].​

Properties of Powers of Matrices

The common rules for powers and exponents known from scalar algebra also hold for matrix powers.

Grouping of Exponents

Because matrix multiplication is associative, you can group the powers however you want:

A4=AAAA=A2A2=A3A=AA3.A^4 = AAAA = A^2 A^2 = A^3 A = A A^3.A4=AAAA=A2A2=A3A=AA3.

Multiplication of Powers

Just like with scalars, multiplying to powers with the same basis is the same as adding the exponents:

ArAs=Ar+s.A^r A^s = A^{r + s}.ArAs=Ar+s.

Powers of Powers

Similarly, the power of a power of a matrix is the same as multiplying the exponents:

(Ar)s=Ars(A^r)^s = A^{rs}(Ar)s=Ars

An Important Warning

Keep in mind that, since matrix multiplication is not commutative, when expanding

(A+B)2=(A+B)(A+B)=(A+B)A+(A+B)B=A2+BA+AB+B2,\begin{align*} (A + B)^2 &= (A + B)(A + B) \\ &= (A + B)A + (A + B)B \\ &= A^2 + BA + AB + B^2, \end{align*}(A+B)2​=(A+B)(A+B)=(A+B)A+(A+B)B=A2+BA+AB+B2,​

this is not the same as

A2+2AB+B2.A^2 + 2AB + B^2.A2+2AB+B2.

In fact, generally speaking

(A+B)2β‰ A2+2AB+B2,(A + B)^2 \ne A^2 + 2AB + B^2,(A+B)2ξ€ =A2+2AB+B2,

because in general AB+BA≠2ABAB + BA \ne 2ABAB+BA=2AB , since AB≠BAAB \ne BAAB=BA.

A Final Remark

Fractional and negative exponents can also be defined for matrices. However, with the content covered so far, we aren’t yet equipped to explore them in detail.

Previous
Identity Matrix
Next
Reverse Order Law for the Transposition of Matrices
This website does not collect personal data, does not use cookies, and does not perform any tracking.