*This summary of the video was created by an AI. It might contain some inaccuracies.*

## 00:00:00 – 00:08:14

The video discusses the process of orthogonally diagonalizing a matrix, using a 2×2 symmetric matrix example. Key points include understanding eigenvalues, the need for a Q matrix that satisfies Q^TAQ = D, and transforming eigenvectors into orthonormal vectors. Criteria for matrix diagonalizability, importance of orthogonal matrices, and the significance of symmetric matrices are highlighted. The procedure involves finding eigenvalues, eigenvectors, constructing the Q matrix, and ensuring orthonormality. Transforming eigenvectors into orthonormal vectors and aligning the columns correctly for the diagonal matrix representation are crucial steps in the process.

### 00:00:00

In this segment of the video, the focus is on how to orthogonally diagonalize a matrix, using a 2×2 symmetric matrix example. The speaker mentions the importance of understanding repeated eigenvalues and the potential need for Gram-Schmidt to find values for the Q matrix. The definitions of diagonalization and orthogonally diagonalizing a matrix are discussed. It is highlighted that not all matrices are orthogonally diagonalizable, but every symmetric matrix is. The criteria for a matrix to be diagonalizable and the significance of an orthogonal matrix in orthogonally diagonalizing are emphasized.

### 00:03:00

In this segment of the video, the speaker explains the process of finding a Q matrix that satisfies the equation Q^TAQ = D. The first step is to find the eigenvalues of the given 2×2 matrix, resulting in lambda1 = 3 and lambda2 = 1. Since there are two distinct eigenvalues, there are two distinct eigenvectors. The speaker proceeds to find the eigenvectors corresponding to lambda1 and lambda2, which are (1; 1) and (1; -1) respectively. These eigenvectors are then used to construct the Q matrix.

### 00:06:00

In this segment of the video, the speaker discusses the process of transforming given eigenvectors into orthonormal vectors. They emphasize the importance of normalizing the eigenvectors and explain the steps to achieve orthonormality. The final answer consists of columns of orthonormal vectors derived from the eigenvalues of matrix B, arranged in correspondence with the diagonal matrix representation. The correct order of the columns is crucial in aligning with the eigenvalue order.