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![Eigenvalues and Eigenvectors • Definition ○ If T:V→V is linear and V is a vector space ○ Then v∈V is an eignevector of T with eigenvalue λ if § v≠0 § Tv=λv • Theorem ○ Linear transformation T:Rn→Rn (or ℂ^n→ℂ^n) ○ matrix(T)=T=[■8(t_11&⋯&t_1n@⋮&⋱&⋮@t_n1&⋯&t_nn )] ○ Then λ is an eigenvalue of T if ○ det(T−λI)=0 • Characteristic Polynomial ○ det(T−λI) is the called characteristic polynomial of T ○ f(λ)=det(T−λI)=|■8(t_11−λ&t_12&⋯&t_1n@t_21&t_22−λ&…&t_2n@⋮&⋮&⋱&⋮@t_n1&t_n2&⋯&t_nn−λ)| ○ =(−λ)^n+c_1 (−λ)^(n−1)+…+c_(n−1) (−λ)+c_n • How to Find Eigenvalues ○ Solve det(T−λI)=0 ○ Get roots λ_1,…,λ_n (possibly repeated) • How to Find Eigenvectors ○ Solve (T−λI)v=0 ○ For λ=λ_1,λ=λ_2,…,λ=λ_n ○ (T−λI)v=0 is n equations with n unknowns ○ Typically v=0 is the only solution for some λ=λ_i ○ Then det(T−λI)=0, and there is a solution v≠0 • Coefficients of Characteristic Polynomial ○ By definition § f(λ)=(−λ)^n+c_1 (−λ)^(n−1)+…+c_(n−1) (−λ)+c_n ○ By Fundamental Theorem of Algebra § f(λ)=a(λ_1−λ)(λ_2−λ)⋯(λ_n−λ) ○ Comparing the coefficient of (−λ)^n, we get § a=1 ○ Setting λ=0 to both polynomials we get § c_n=detT=λ_1 λ_2…λ_n ○ By Vieta](https://shawnzhong.com/wp-content/uploads/2017/12/img_5a2b731f3f050.png)
![Eigenvalues and Eigenvectors • Definition ○ T:V→V linear, for {█(x∈V@λ∈ℂ)┤, (x≠0) ○ We say x is an eigentvector for T with eigenvalue λ if Tx=λx • Note ○ Tx=λx ○ ⇒Tx−λx=0 ○ ⇒(T−λI)x=0 ○ ⇒x∈N(T−λI) Find all eigenvalues and eigenvectors • T=I ○ Tx=1x, ∀x∈V ○ Eigenvalue = 1 with eigenvectors of all elements in V • T=0 ○ Tx=0x, ∀x∈V ○ Eigenvalue = 0 with eigenvectors of all elements in V • T=[■(c_1&&@&⋱&@&&c_n )], (c_i≠c_j for i=j) ○ [■(c_1&&@&⋱&@&&c_n )] e_i=c_i e_i ○ Eigenvalue = c_i with eigenvector of te_i, (t∈R, t≠0) • T=[■8(1&2@2&1) ○ det(T−λI)=0 § |■8(1−λ&2@2&1−λ)|=0 § (λ−3)(λ+1)=0 ○ λ=3 § [■8(1−3&2@2&1−3)][█(x@y)]=[█(0@0)]⇒x=y § Eigenvector: [█(t@t)](t∈R, t≠0) ○ λ=−1 § [■8(2&2@2&2)][█(x@y)]=[█(0@0)]⇒x+y=0 § Eigenvector: t[█(1@−1)](t∈R, t≠0) • T=[■8(0&−1@1&0) ○ det(T−λI)=0 § |■8(−λ&−1@1&−λ)|=0 § λ^2+1=0 ○ λ=i § [■8(−i&−1@1&−i)][█(x@y)]=[█(0@0)]⇒y=−ix § Eigenvector: t[█(1@−i)](t∈ℂ, t≠0) ○ λ=−i § [■8(i&−1@1&i)][█(x@y)]=[█(0@0)]⇒y=ix § Eigenvector: t[█(1@i)](t∈ℂ, t≠0) Multiplicity of Eigenvalues • T=[■(3&&@&3&@&&4)] ○ Eigenvalues: λ=3 or λ=4 ○ dim〖N(T−λI)={■8(2&λ=3@1&λ=4@0&otherwise)┤〗](https://shawnzhong.com/wp-content/uploads/2017/12/img_5a2b7365d72ee.png)
