We use a covariance matrix to denote covariances of a multivariate normal distribution, and it looks like this:

\Sigma = \begin{bmatrix}
\sigma_1^2 & \sigma_{12} & \cdots & \sigma_{1n} \\
\sigma_{21} & \sigma_2^2 & \cdots & \sigma_{2n} \\
\vdots & \vdots & \ddots & \vdots \\
\sigma_{n1} & \sigma_{n2} & \cdots & \sigma_n^2
\end{bmatrix}$$
The diagonal contains the variance for each variable, and the off-diagonal elements contain the covariance between the $i^{th}$ and $j^{th}$ variables. So $\sigma_3^2$ is the variance of the third variable, and $\sigma_{13}$ is the covariance between the first and third variables.
The covariance matrix is a symmetric matrix.
### Related
[[notes/CMA-ES|CMA-ES]]