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Unformatted text preview: Repeated Eigenvalues Recall We are now in the position that we can find (at least) as many linearlyindependent solutions to the homogeneous equation ˙ y = A y as there are distinct eigenvalues of A (real or complex). This is because for each real eigenvalue λ we can try to find as many linearlyindependent eigenvectors v as we can by solving ( A λI ) v = 0 and for each one we find we have a solution of the form y = e λx v . Similarly, for each complex eigenvalue λ = α + iβ we find, we can find an eigenvector v = v real + i v im by solving ( A λI ) v = 0 and this produces two linearlyindependent solutions y 1 = e αx cos( βx ) v real sin( βx ) v im and y 2 = e αx sin( βx ) v real + cos( βx ) v im . Thus we have (at least) as many linearlyindependent solutions as we have distinct eigenvalues. But it may be that the eigenvalues are repeated so that we can only find k linearlyindependent solutions from eigenvectors in the manner described above. Since we need n linearlyindependent solutions y 1 , y 2 ,..., y n to write down the general solution y ( x ) = c 1 y ! + c 2 y 2 + ··· + c n y n we need to find another n k linearlyindependent solutions. Generalized Eigenvectors Before we discuss a way of producing more solutions, we need a few definitions. Definition The matrix exponential function e X : M n ( R ) → M n ( R ) is a function that takes an n × n matrix X as an input and outputs a new n × n matrix denoted e X . The matrix e X is defined by substituting X into the Taylor series for e x , which gives e X = I + X + X 2 2! + X 3 3! + ··· where I is the identity matrix. The series e X converges for all n × n matrices X ....
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This note was uploaded on 03/03/2012 for the course MATH 4430 at Colorado.
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