a) What is a benefit of requiring eigenvectors to be normalised?

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
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a) What is a benefit of requiring eigenvectors to be normalised?
b) Can a rectangular matrix of dimension M x N, where M ‡ N, possess eigenvectors?
Briefly justify your answer.
c)
What is the difference in the terms 'self-adjoint' and 'hermitian' when applied to an operator
in a function space?
d)
If necessary, correct the underlined parts of this statement: The eigenvalues of a unitary
matrix are either +1 or -1 and those of an orthogonal matrix are imaginary.
e)
If necessary, correct the underlined parts of this statement: The set of 2×2 singular matrices
does not form a vector space because the set is not closed under addition.
Transcribed Image Text:a) What is a benefit of requiring eigenvectors to be normalised? b) Can a rectangular matrix of dimension M x N, where M ‡ N, possess eigenvectors? Briefly justify your answer. c) What is the difference in the terms 'self-adjoint' and 'hermitian' when applied to an operator in a function space? d) If necessary, correct the underlined parts of this statement: The eigenvalues of a unitary matrix are either +1 or -1 and those of an orthogonal matrix are imaginary. e) If necessary, correct the underlined parts of this statement: The set of 2×2 singular matrices does not form a vector space because the set is not closed under addition.
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