# 17. Orthogonal Matrices and Gram-Schmidt | Summary and Q&A

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September 24, 2019
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MIT OpenCourseWare
17. Orthogonal Matrices and Gram-Schmidt

## TL;DR

The lecture discusses the concepts of orthogonality and orthonormality, introducing orthogonal vectors, subspaces, bases, and matrices, as well as the Graham-Schmidt process for transforming non-orthogonal bases into orthonormal ones.

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### Q: What is the main difference between orthogonal and orthonormal vectors?

Orthogonal vectors are those that are perpendicular to each other, while orthonormal vectors not only satisfy the criterion of orthogonality but are also of unit length.

### Q: What is the significance of having an orthonormal basis?

Having an orthonormal basis simplifies calculations and numerical linear algebra, as the vectors in the basis are at right angles to each other and have lengths of 1, making calculations easier to handle and preventing issues with overflow or underflow.

### Q: How is an orthonormal matrix different from an orthogonal matrix?

An orthonormal matrix refers to a matrix with orthonormal columns, while an orthogonal matrix is specifically a square matrix whose inverse is equal to its transpose. Thus, all orthogonal matrices are orthonormal, but not all orthonormal matrices are orthogonal.

### Q: What are the properties of a projection matrix?

A projection matrix is symmetric and idempotent, meaning that when multiplied with itself, it doesn't change. The projection matrix onto a column space simplifies to the identity matrix if the orthogonal basis is square, but otherwise takes the form of Q(Q^T).

## Summary & Key Takeaways

• The lecture begins by introducing orthogonal vectors and the concept of orthogonality in a basis, emphasizing the importance of orthonormal bases.

• The Graham-Schmidt process is then explained as a method for transforming a set of independent vectors into an orthonormal basis.

• The lecture highlights the benefits of orthonormal bases in terms of numerical calculations, as well as the relationship between orthogonal matrices and square matrices.

• Examples of orthogonal matrices and the projection matrix onto a column space are provided to further illustrate the concepts.

• The lecture concludes with the observation that orthonormal matrices can simplify calculations and the connection between the original matrix and the orthonormal matrix is represented by a triangular matrix.