A symmetric matrix is the matrix form of a linear transformation whose action is balanced with respect to the Euclidean [inner product](/page/Inner%20Product).
text
admin
When a matrix $A$ is used in expressions such as $\langle Ax,y\rangle$, $x^\top Ay$, or $x^\top Ax$, symmetry determines whether the two vector inputs can be exchanged without changing the scalar measurement.
text
admin
This is why symmetric matrices appear across linear algebra, quadratic forms, [inner product spaces](/page/Inner%20Product%20Space), multivariable calculus, probability, and optimization.
text
admin
A general real square matrix may rotate, shear, and stretch space in coupled ways.
text
admin
A real symmetric matrix has a much more rigid geometry: it admits perpendicular eigenvector directions and real eigenvalues.
text
admin
That geometric rigidity is the reason symmetric matrices are central to the spectral theorem, positive definiteness, Hessian tests, and covariance matrices.
text
admin
This page works over the [real numbers](/page/Real%20Numbers). Over complex vector spaces, the closely related analogue is Hermitian symmetry, where transpose is replaced by conjugate transpose; confusing the two loses the inner-product interpretation.
text
admin
## Definition
h2
admin
The central condition is that entries mirror across the main diagonal. This is the matrix-level way to say that the interaction from coordinate $i$ to coordinate $j$ is the same as the interaction from coordinate $j$ to coordinate $i$.
text
admin
[definition: Symmetric Matrix]
Let $n \in \mathbb{N}$. A matrix $A \in \mathbb{R}^{n \times n}$ with entries $A_{ij}$ is symmetric if
\begin{align*}
A_{ij}=A_{ji}
\end{align*}
for all $1 \le i,j \le n$.
[/definition]
definition
admin
This definition is deliberately entrywise: it can be checked without choosing any extra language beyond the matrix entries themselves. Most of the theory, however, is cleaner once the row-column reflection operation has a name.
text
admin
## Basic Notation and Related Definitions
h2
admin
The compact test for symmetry is $A^\top=A$, so the transpose operation must be fixed before it is used in later characterisations. Entrywise conditions are good for checking a matrix by hand, but they become awkward once matrices are added, multiplied, or used to represent linear maps. Naming the operation gives a reusable language for every comparison between a matrix and its reflected version.
text
admin
[definition: Transpose of a Matrix]
Let $m,n \in \mathbb{N}$. The transpose map is the function
\begin{align*}
T: \mathbb{R}^{m \times n} \to \mathbb{R}^{n \times m}, \qquad A \mapsto A^\top
\end{align*}
such that, if $A$ has entries $A_{ij}$, then $A^\top$ has entries satisfying
\begin{align*}
(A^\top)_{ij}=A_{ji}.
\end{align*}
[/definition]
definition
admin
With this notation, a real square matrix is symmetric exactly when $A^\top=A$.
text
admin
Once the equation $A^\top=A$ is fixed, it is useful to ask where all matrices satisfying that equation live. They are not scattered examples: adding two such matrices or scaling one preserves the same transpose equation, so the symmetric matrices of a fixed size form a natural ambient space for later projections, bases, and dimension counts.
text
admin
[definition: Space of Symmetric Matrices]
For $n \in \mathbb{N}$, the space of real symmetric $n \times n$ matrices is
\begin{align*}
\operatorname{Sym}_n(\mathbb{R}) := \{A \in \mathbb{R}^{n \times n} : A^\top=A\}.
\end{align*}
[/definition]
definition
admin
Symmetry is only one way a matrix can interact simply with transposition. To separate the part unchanged by transpose from the part that cancels under transpose, we also need the opposite sign behavior; these matrices will be invisible to quadratic expressions of the form $x^\top Ax$.
text
admin
[definition: Skew-Symmetric Matrix]
Let $n \in \mathbb{N}$. A matrix $A \in \mathbb{R}^{n \times n}$ is skew-symmetric if
\begin{align*}
A^\top=-A.
\end{align*}
[/definition]
definition
admin
In entries, symmetry says $A_{ij}=A_{ji}$ for all $1 \le i,j \le n$.