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The Tangent Space on an n-Dimensional Differentiable Manifold is an n-Dimensional Vector Space 📂Geometry

The Tangent Space on an n-Dimensional Differentiable Manifold is an n-Dimensional Vector Space

Overview

Let MM be a nn-dimensional differential manifold, and let TpMT_{p}M be the tangent space at point pMp\in M. The tangent space becomes a vector space, specifically, a nn-dimensional vector space. The following set becomes the basis of the tangent space, which is very useful in the study of differential manifolds.

B={xip:1in} \mathcal{B} = \left\{ \left. \dfrac{\partial }{\partial x_{i}} \right|_{p} : 1 \le i \le n \right\}

Theorem 11

TpMT_{p}M is a R\mathbb{R}-vector space.

Proof

The conditions for being a vector space are as follows, of which we will prove only a few:

For u,v,wV\mathbf{u}, \mathbf{v}, \mathbf{w} \in V and k,lFk, l \in \mathbb{F},

(A1) If u,v\mathbf{u}, \mathbf{v} is an element of VV, then u+v\mathbf{u}+\mathbf{v} is also an element of VV.

(A2) u+v=v+u\mathbf{u} + \mathbf{v} = \mathbf{v} + \mathbf{u}

(A3) (u+v)+w=u+(v+w)(\mathbf{u}+\mathbf{v})+\mathbf{w}=\mathbf{u}+(\mathbf{v}+\mathbf{w})

(A4) For all u\mathbf{u} in VV, there exists 0\mathbf{0} in VV such that u+0=0+u=u\mathbf{u} + \mathbf{0} = \mathbf{0} + \mathbf{u} = \mathbf{u}.

(A5) For all u\mathbf{u} in VV, there exists v\mathbf{v} in VV such that u+v=v+u=0\mathbf{u} + \mathbf{v} = \mathbf{v} + \mathbf{u} = \mathbf{0}.

(M1) If u\mathbf{u} is an element of VV, then kuk \mathbf{u} is also an element of VV.

(M2) k(u+v)=ku+kvk(\mathbf{u} + \mathbf{v})=k\mathbf{u} + k\mathbf{v}

(M3) (k+l)u=ku+lu(k+l)\mathbf{u}=k\mathbf{u}+ l\mathbf{u}

(M4) k(lu)=(kl)(u)k(l\mathbf{u})=(kl)(\mathbf{u})

(M5) For 1F1\in \mathbb{F}, 1u=u1\mathbf{u} = \mathbf{u}

Let it be X,YTpM\mathbf{X}, \mathbf{Y} \in T_{p}M. Let the set of differentiable functions on pp be D\mathcal{D}.

D:={f:MRfunctions on Mthat are differentiable at p} \mathcal{D} := \left\{ f : M \to \mathbb{R} | \text{functions on } M \text{that are differentiable at } p \right\}

For being a vector space, the addition between elements and scalar multiplication must be defined. Let’s define the addition and scalar multiplication as follows.

(X+Y)(f):=Xf+Yf(rX)(f):=rXf,rR (\mathbf{X} + \mathbf{Y}) (f) := \mathbf{X}f + \mathbf{Y}f \\ (r \cdot \mathbf{X}) (f) := r \cdot \mathbf{X}f,\quad r \in \mathbb{R}

(A1) Since X\mathbf{X} and Y\mathbf{Y} are functions in DR\mathcal{D} \to \mathbb{R}, Xf,YfR\mathbf{X}f, \mathbf{Y}f \in \mathbb{R} holds. The sum of two real numbers is a real number, so X+Y:DR\mathbf{X} + \mathbf{Y} : \mathcal{D} \to \mathbb{R} holds. Therefore, X+YTpM\mathbf{X} + \mathbf{Y} \in T_{p}M holds.

(M1) Since XfR\mathbf{X}f \in \mathbb{R} and rRr \in \mathbb{R}, rXfRr \cdot \mathbf{X} f \in \mathbb{R} holds. Therefore, rX:DRr\mathbf{X} : \mathcal{D} \to \mathbb{R} holds, and rXTpMr\mathbf{X} \in T_{p}M is true.

(A4) Let’s define 0:DR\mathbf{0} : \mathcal{D} \to \mathbb{R} as 0f=0(fD)\mathbf{0} f = 0 (\forall f \in \mathcal{D}). Then

(X+0)(f)=Xf+0f=Xf (\mathbf{X} + \mathbf{0})(f) = \mathbf{X}f + \mathbf{0}f = \mathbf{X}f

(A5) If we define X-\mathbf{X} as (X)(f):=(1)Xf(-\mathbf{X})(f) := (-1) \cdot \mathbf{X}f, by (M1), (1)XTpM(-1)\mathbf{X} \in T_{p}M holds, and X+(X)=0\mathbf{X} + (-\mathbf{X}) = \mathbf{0} is satisfied.


Theorem 2

TpMT_{p}M is a nndimensional vector space. In particular, let x:UM\mathbf{x} : U \to M be the coordinate system for point pMp \in M. Then the set

B={xip:1in} \mathcal{B} = \left\{ \left. \dfrac{\partial }{\partial x_{i}} \right|_{p} : 1 \le i \le n \right\}

is the basis of the tangent space TpMT_{p}M. In this case, xip:DR\left. \dfrac{\partial }{\partial x_{i}} \right|_{p} : \mathcal{D} \to \mathbb{R} is defined as follows.

xipf:=(fx)uip,fD \left. \dfrac{\partial }{\partial x_{i}} \right|_{p} f := \left. \dfrac{\partial (f\circ \mathbf{x})}{\partial u_{i}} \right|_{p},\quad f \in \mathcal{D}

(u1,,un)(u_{1}, \dots, u_{n}) are the coordinates of R\mathbb{R}.

Proof

According to the definition of basis, we need to show that B\mathcal{B} is linearly independent, and generates TpMT_{p}M.

  • Part 1. Linear independence

    For the coordinate system x:URnM\mathbf{x} : U \subset \mathbb{R}^{n} \to M, the coordinate functions xi:MRx_{i} : M \to \mathbb{R} are as follows:

    x(u)=px1(p)=(x1(p),,xn(p))=(u1,,un) \mathbf{x}(\mathbf{u}) = p \\ \mathbf{x}^{-1}(p) = (x_{1}(p), \dots, x_{n}(p)) = (u_{1}, \dots, u_{n})

    If we let f=xjf = x_{j},

    fx(u)=xjx(u)=xj(x(u))=xj(p)=uj f \circ \mathbf{x}(\mathbf{u}) = x_{j} \circ \mathbf{x}(\mathbf{u}) = x_{j}(\mathbf{x}(\mathbf{u})) = x_{j}(p) = u_{j}

    Therefore,

    xipxj=(xjx)uip=ujui=δij \left. \dfrac{\partial }{\partial x_{i}} \right|_{p} x_{j} = \left. \dfrac{\partial (x_{j} \circ \mathbf{x})}{\partial u_{i}} \right|_{p} = \dfrac{\partial u_{j}}{\partial u_{i}} = \delta_{ij}

    Here, δij\delta_{ij} is the Kronecker delta.

    Looking at equation cixip=0c_{i}\left. \dfrac{\partial }{\partial x_{i}} \right|_{p} = \mathbf{0}, if for all ii, ci=0c_{i} = 0 holds, then it is linearly independent. By substituting any xjx_{j} for 1jn1 \le j \le n,

    0=0(xj)=cixipxj=ciδij=cj 0 = \mathbf{0}(x_{j}) = c_{i}\left. \dfrac{\partial }{\partial x_{i}} \right|_{p} x_{j} = c_{i}\delta_{ij} = c_{j}

    Therefore, for all jj, cj=0c_{j} = 0 holds, and no other solution exists. Hence, B\mathcal{B} is linearly independent.

  • Part 2. Generation

    Let’s denote fDf \in \mathcal{D}, a=x1(p)\mathbf{a} = \mathbf{x}^{-1} (p), F=fxF = f \circ \mathbf{x}. Then F:RnRF : \mathbb{R}^{n} \to \mathbb{R} holds.

    Taylor’s theorem for multivariable functions

    F(x)=F(a)+iFxi(a)(xiai)+i,jhij(x)(xiai)(xjaj) F(\mathbf{x}) = F(\mathbf{a}) + \sum_{i} \dfrac{\partial F}{\partial x_{i}}(\mathbf{a})(x_{i} - a_{i}) + \sum_{i,j}h_{ij}(\mathbf{x})(x_{i} - a_{i}) (x_{j} - a_{j})

    Applying the above Taylor’s theorem to FF, the following holds. For mMm \in M,

    f(m)= Fx1(m)=F(x1(m))= F(a)+iFui(a)(xi(m)ai)+i,jhij(x1(m))(xi(m)ai)(xj(m)aj)= Fx1(p)+iFui(x1(p))(xi(m)ai)+hij(x1(m))(xi(m)ai)(xj(m)aj)= f(p)+i(xipf)(xi(m)ai)+hij(x1(m))(xi(m)ai)(xj(m)aj) \begin{align*} f(m) =&\ F \circ \mathbf{x}^{-1}(m) = F ( \mathbf{x}^{-1}(m)) \\ =&\ F (\mathbf{a}) + \sum_{i} \dfrac{\partial F}{\partial u_{i}}(\mathbf{a})(x_{i}(m) - a_{i}) + \sum_{i,j}h_{ij}(\mathbf{x}^{-1}(m))(x_{i}(m) - a_{i}) (x_{j}(m) - a_{j}) \\ =&\ F \circ \mathbf{x}^{-1}(p) + \sum_{i} \frac{\partial F}{\partial u_{i}}(\mathbf{x}^{-1}(p)) \left(x_{i}(m)-a_{i}\right) +\sum h_{i j}(\mathbf{x}^{-1}(m))\left(x_{i}(m)-a_{i}\right)\left(x_{j}(m)-a_{j}\right) \\ =&\ f(p) + \sum_{i} \left( \left.\frac{\partial }{\partial x_{i}}\right|_{p} f \right) \left(x_{i}(m)-a_{i}\right) +\sum h_{i j}(\mathbf{x}^{-1}(m))\left(x_{i}(m)-a_{i}\right)\left(x_{j}(m)-a_{j}\right) \end{align*}

    Therefore, ff is the following mapping:

    f=f(p)+i(xipf)(xiai)+(hijx1)(xiai)(xjaj) f = f(p) + \sum_{i} \left( \left.\frac{\partial }{\partial x_{i}}\right|_{p} f \right) \left(x_{i}-a_{i}\right) +\sum (h_{i j} \circ \mathbf{x}^{-1})\left(x_{i}-a_{i}\right)\left(x_{j}-a_{j}\right)

    When applied to the tangent vector X\mathbf{X},

    X(f)=X(f(p))+i(xipf)X(xiai)+X[(hijx1)(xiai)(xjaj)] \mathbf{X}(f) = \mathbf{X}(f(p)) + \sum_{i} \left( \left.\frac{\partial }{\partial x_{i}}\right|_{p} f \right) \mathbf{X}\left(x_{i}-a_{i}\right) +\sum \mathbf{X}\left[ (h_{i j} \circ \mathbf{x}^{-1})\left(x_{i}-a_{i}\right)\left(x_{j}-a_{j}\right) \right]

    Since the tangent vector is defined as a differential operator, for the constant function cc, X(c)=0\mathbf{X}(c) = 0 holds. Therefore, the first term in the equation above is 00.

    Xp(fg)=f(p)Xp(g)+g(p)Xp(f) \mathbf{X}_{p}(fg) = f(p)\mathbf{X}_{p}(g) + g(p)\mathbf{X}_{p}(f)

    Therefore, if f(p)=g(p)=0f(p)=g(p)=0,

    Xp(fg)=0 \mathbf{X}_{p}(fg) = 0

    Also, by setting f=(hijx1)(xiai)f = (h_{i j} \circ \mathbf{x}^{-1})\left(x_{i}-a_{i}\right), g=(xjaj)g=\left(x_{j}-a_{j}\right), and applying the above lemma, xi(p)=aix_{i}(p) = a_{i} holds, so the third term is also concluded to be 00. Thus, the following is obtained.

    X(f)= i(xipf)X(xiai)= i(xipf)(X(xi)X(ai))= i(xipf)X(xi)= iX(xi)xipf \begin{aligned} \mathbf{X}(f) =&\ \sum_{i} \left( \left.\frac{\partial }{\partial x_{i}}\right|_{p} f \right) \mathbf{X}\left(x_{i}-a_{i}\right) \\ =&\ \sum_{i} \left( \left.\frac{\partial }{\partial x_{i}}\right|_{p} f \right) \left(\mathbf{X}(x_{i}) - \mathbf{X}(a_{i})\right) \\ =&\ \sum_{i} \left( \left.\frac{\partial }{\partial x_{i}}\right|_{p} f \right) \mathbf{X}(x_{i}) \\ =&\ \sum_{i} \mathbf{X}(x_{i}) \left.\frac{\partial }{\partial x_{i}}\right|_{p} f \end{aligned}

        X=iX(xi)xip \implies \mathbf{X} = \sum_{i} \mathbf{X}(x_{i}) \left.\frac{\partial }{\partial x_{i}}\right|_{p}

    Therefore, since any X\mathbf{X} can be expressed as a linear combination of B\mathcal{B}, TpMT_{p}M is generated by B\mathcal{B}.


  1. Richard S. Millman and George D. Parker, Elements of Differential Geometry (1977), p214 ↩︎