Why is the derivative of a function at a point is a linear functional on the tangent space

differential-geometrymanifoldssurfacestangent-spaces

My apologies if this question has been asked before but I haven't been able to find a satisfying answer.

Whenever I look into the definition of tangent spaces, it's always in the context of manifolds or differential geometry which are two topics I do not know about a lot. The reason I am asking about it is because I have seen in some definition, that the derivative of a function at a point $p$, let's say $f: \mathbb{R}^n \rightarrow \mathbb{R}$ is actually a linear functional acting on the tangent space of $\mathbb{R}^n$ at that point. I find this definition very interesting but I am not sure I am grasping the intuition geometrically because I do not think I understand what the tangent space represents. If I try to visualize it, how would it relate to the tangent plane of a surface at a point in this?

Best Answer

It might be easier to see if we put this in the context of single variable calculus.

In calculus, given a functions $f:\mathbb R\to\mathbb R$, we can take the derivative at a single point $p\in\mathbb R$. This gives us the line $$f'(p)=\frac{y-f(p)}{x-p}\implies y-f(p)=f'(p)(x-p)$$ as the tangent to the curve at the point $(p,f(p))$.

Let $dx=x-p$ and let $dy=y-f(p)$. Then the tangent line becomes $$dy=f'(p)dx$$ and this is the map of the tangent space of $\mathbb R$ at $x=p$ to the tangent space of $\mathbb R$ at $y=f(p)$.

The elements of the tangent space at $x=p$ are the $dx$s and are the changes we can make in any direction in $\mathbb R$. $\mathbb R$ is one dimensional so there is only one direction of change and that is along the $x$ axis.

The same analysis can be done with a function $g:\mathbb R^n\to\mathbb R$. Consider a point $q=(q_1,\dots,q_n)\in\mathbb R^n$.

We have $y=g(x_1,\dots\,x_n)$ and $$dy=\frac{\partial y}{\partial x_1}(q_1,\dots,q_n)dx_1+\dots +\frac{\partial y}{\partial x_n}(q_1,\dots,q_n)dx_n=\nabla g(q_1,\dots,q_n)\cdot d\vec x=\nabla g(q_1,\dots,q_n)\begin{pmatrix}dx_1\\ \vdots\\ dx_n\end{pmatrix}$$.

The derivative is represented by the gradient which is a linear functional from the tangent space of $\mathbb R^n$ at $q=(q_1,\dots,q_n)$ to the tangent space of $\mathbb R$ at $y=g(q_1,\dots,q_n)$. Note that $dx_i$ is the change along the $x_i$ axis in $\mathbb R^n$.