Unitary, Orthogonal
Definition 1. Let T∈L(V)T∈L(V) where VV is a finite-dimensional inner product space over FF. If ||T(x)||=||x||,∀x∈V||T(x)||=||x||,∀x∈V, we call TT a unitary operator if F=CF=C and call TT an orthogonal operator if F=RF=R.
유한차원의 경우 unitary, 혹은 othogonal, 즉 유니터리 혹은 직교 연산자라고 부르며, 무한차원의 경우 metric을 보존한다는 점을 강조하기 위해 isometry라고 부른다.
자명하게 선형 연산자 TT가 unitary 혹은 orthogonal일 조건은 [T]β[T]β가 unitary 혹은 orthogonal일 조건과 동치이다. (ββ는 orthonormal basis)
Lemma
Lemma. Let UU be a hermitian operator on a finite-dimensional inner product space VV. If ⟨x,U(x)⟩=0,∀x∈V⟨x,U(x)⟩=0,∀x∈V, then U=T0U=T0.
Proof. Let ββ be an orthonormal basis for VV consisting of eigenvectors. Then ∀x∈β∀x∈β, U(x)=λxU(x)=λx for some λ∈Fλ∈F. Thus ⟨x,U(x)⟩=¯λ||x||=0⟨x,U(x)⟩=¯¯¯λ||x||=0. Thus λ=0λ=0, so U(x)=0=T0(x),∀x∈βU(x)=0=T0(x),∀x∈β. This means that U=T0.U=T0. ◼■
Theorem 1
Theorem 1. Let T∈L(V)T∈L(V) where VV is a finite-dimensional inner product space. Then the following statements are equivalent.
(a) TT∗=T∗T=ITT∗=T∗T=I.
(b) ⟨T(x),T(y)⟩=⟨x,y⟩,∀x,y∈V⟨T(x),T(y)⟩=⟨x,y⟩,∀x,y∈V.
(c) If ββ is an orthonormal basis for VV, then T(β)T(β) is an orthonormal basis for VV.
(d) There exists an orthonormal basis ββ for VV such that T(β)T(β) is an orthonormal basis for VV.
(e) ||T(x)||=||x||,∀x∈V||T(x)||=||x||,∀x∈V.
Proof. (a) ⟹⟹ (b)
∀x,y∈V∀x,y∈V, ⟨T(x),T(y)⟩=⟨x,T∗T(y)⟩=⟨x,y⟩⟨T(x),T(y)⟩=⟨x,T∗T(y)⟩=⟨x,y⟩.
(b) ⟹⟹ (b)
Let β={v1,...,vn}β={v1,...,vn}. Then ⟨T(vi),T(vj)⟩=⟨vi,vj⟩=δij⟨T(vi),T(vj)⟩=⟨vi,vj⟩=δij. Thus T(β)T(β) is orthonormal basis for VV.
(c) ⟹⟹ (d)
Clear.
(d) ⟹⟹ (e)
Let β={v1,...,vn}β={v1,...,vn}. Then ∀x∈V∀x∈V, x=∑ni=1⟨x,vi⟩vix=∑ni=1⟨x,vi⟩vi. Thus we have ||T(x)||2=⟨T(x),T(x)⟩=⟨n∑i=1⟨x,vi⟩T(vi),n∑j=1⟨x,vj⟩T(vj)⟩=n∑i=1⟨x,vi⟩¯⟨x,vi⟩=⟨x,x⟩=||x||2||T(x)||2=⟨T(x),T(x)⟩=⟨n∑i=1⟨x,vi⟩T(vi),n∑j=1⟨x,vj⟩T(vj)⟩=n∑i=1⟨x,vi⟩¯¯¯¯¯¯¯¯¯¯¯¯¯¯⟨x,vi⟩=⟨x,x⟩=||x||2 by Parseval's Identity. Thus ||T(x)||=||x||,∀x∈V||T(x)||=||x||,∀x∈V.
(e) ⟹⟹ (a)
Note that ⟨x,x⟩=||x||2=||T(x)||2=⟨T(x),T(x)⟩=⟨x,T∗T(x)⟩,∀x∈V⟨x,x⟩=||x||2=||T(x)||2=⟨T(x),T(x)⟩=⟨x,T∗T(x)⟩,∀x∈V. Then ⟨x,x−T∗T(x)⟩=⟨x,(I−T∗T)(x)⟩=0,∀x∈V⟨x,x−T∗T(x)⟩=⟨x,(I−T∗T)(x)⟩=0,∀x∈V.
Since (I−T∗T)∗=I−T∗T(I−T∗T)∗=I−T∗T, by Lemma, I−T∗T=T0I−T∗T=T0, so T∗T=IT∗T=I.
Let A=[T∗]βA=[T∗]β and B=[T]βB=[T]β. Then AB=IAB=I, so BA=I=[TT∗]βBA=I=[TT∗]β. Thus T∗T=TT∗=IT∗T=TT∗=I. ◼■
즉 unitary 혹은 orthogonal operator는 놈과 내적을 보존하므로 자명하게 orthonormal basis도 보존한다.
Orthonormal basis의 벡터는 놈의 값이 1이고, unitary 혹은 orthogoanl operator는 놈을 보존하므로 자명하게 모든 고유값의 절댓값의 크기는 1임을 알 수 있다.
Corollary 1
Corollary 1. Let T∈L(V)T∈L(V) where VV is a finite-dimensional real inner product space. Then VV has an orthonormal basis of eigenvectors of TT with corresponding eigenvalues of absolute value 1 ⟺⟺ TT is both hermitian and orthogonal.
Proof. (⟹⟹)
By Theorem 1, TT is hermitian. We have TT∗(v)=T(¯λv)=λ¯λv=|λ|2v=v,∀v∈βTT∗(v)=T(¯¯¯λv)=λ¯¯¯λv=|λ|2v=v,∀v∈β. Then TT∗=ITT∗=I. In similar way, we can show that T∗T=IT∗T=I. Thus by Theorem 1, TT is orthogonal.
(⟸⟸) Since TT is hermitian, by Theorem 1, there exists an orthonormal basis ββ for VV consisting of eigenvectors. Then ||T(v)||=||λv||=|λ|||v||=|λ|=||v||=1,∀v∈β||T(v)||=||λv||=|λ|||v||=|λ|=||v||=1,∀v∈β. Thus every eigenvalue corresponding to v∈βv∈β has absolute value 1. ◼■
Corollary 2
Corollary 2. Let T∈L(V)T∈L(V) where VV is a finite-dimensional complex inner product space. Then VV has an orthonormal basis of eigenvectors of TT with corresponding eigenvalues of absolute value 1 ⟺⟺ TT is unitary.
Proof. We can prove in similar manner to Corollary 1. ◼