-
Notifications
You must be signed in to change notification settings - Fork 66
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Exercise 2.76 #5
Comments
Thanks for this solution. |
Unless I'm mistaken, the proof given in the book already works for the case of different dimensions, if you use the general SVD that also applies to non-square matrices: https://en.wikipedia.org/wiki/Singular_value_decomposition |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
I'm not sure about the correctness of the formalism, but this is my main idea: extend the smaller bases with null coefficients in the expansion in order to reach a situation similar to that in the standard proof.
The text was updated successfully, but these errors were encountered: