Definition Suppose $C \subseteq \mathbb{R}^2$ is a curve and $p \in C$. The tangent space to $C$ at $p$, $T_pC$ is the set of all vectors tangent to $C$ at $p$.
Example
Let us say we have a curve \(y = f(x)\), with a point \(p = (a, f(a))\). A tangent vector at that point can be given by \(\vec v = \langle 1, f^{\prime}(a) \rangle\) where \(\langle \cdot, \cdot \rangle\) is an inner product. Then we can formulate the tangent space
\[T_pC = \text{span} \{ \langle 1, f^{\prime} \rangle \} = \{ \langle c, c f^{\prime}(a) | c \in \mathbb{R} \}.\]
The following code made the figure in the previous example.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_axes((0.1, 0.2, 0.8, 0.7)) ax.spines[['top', 'right']].set_visible(False) ax.set_xticks([]) ax.set_yticks([]) ax.set_ylim([-30, 15]) # Plot of function x = np.linspace(-5, 5, num=100) y = x ** 3 - 10 * x ax.plot(x, y, color='y') # Unit tangent vector ax.plot([2,3], [-12]*2, '--', color='tab:blue') ax.plot([3,3], [-11, -12], '--', color='tab:blue') # Tangent space ax.plot(x, 2 * x - 16, color='m') ax.text(3, -12, r'$T_pC$', color='m') ax.scatter(2, -12, color='m') ax.text(2, -14, r'$p$', color='m') ax.annotate(r'slope$=f^{\prime}(a)$', xy=(4, -8), xytext=(4, 0), arrowprops=dict(facecolor='black', shrink=0.05), color='grey') # Pullbacks ax.plot([2]*2, [-30,-12], '--', color='m', alpha=0.5) ax.text(2, -30, r'$a$', color='m') ax.plot([x.min(), 2], [-12]*2, '--', color='m', alpha=0.5) ax.text(x.min(), -12, 'f(a)', color='m') plt.savefig('first_example_tangent_space.png', transparent=True, dpi=300) plt.close()
How do we differentiate between points on $C \subseteq \mathbb{R}^2$ and vectors in $T_p C \subseteq \mathbb{R}^2$?
We can create a coordinate system on $C$
\[(x,y): C \mapsto \mathbb{R}^2\] \[(x,y)(p) = (x(p), y(p))\]and a coordinate system on $T_p C$:
\[\langle dx, dy \rangle : T_p C \mapsto \mathbb{R}^r\] \[\langle dx, dy \rangle = \langle dx(v), dy(v) \rangle\]Thus $x: C \mapsto \mathbb{R}$ and $y: C \mapsto \mathbb{R}$ are combined to get a coordinate vector in the ambient space.
Similarly, $dx: T_pC \mapsto \mathbb{R}$ and $dy: T_pC \mapsto \mathbb{R}$ are combined to get a coordinate vector in the tangent space.
Example Let $y = x^2$, then the coordinate vector is $(x,y)(p) = (a, a^2) \in C$. Similarly $\langle dx, dy \rangle (v) = \langle 1, 2a \rangle \in T_pC$ where $v$ is a tangent vector to the function.
The equation $\langle dx, dy \rangle (v) = \langle 1, 2a \rangle$ implies familiar result that $dy = 2a \cdot 1 = 2 a dx \implies \frac{dy}{dx} = 2a = f^{\prime}(a).$
The following code made the figure in the previous example.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots() ax.spines[['top', 'right']].set_visible(False) ax.set_xticks([]) ax.set_yticks([]) ax.set_ylim([-5, 15]) # Plot of function x = np.linspace(-5, 5, num=100) y = x**2 ax.plot(x, y, color='y') # Unit tangent vector ax.plot([2,2], [1,3], '--', color='tab:blue') ax.plot([1,2], [1, 1], '--', color='tab:blue') # Tangent space ax.plot(x, 2 * x - 1 , color='m') ax.text(2, 1, r'$T_pC$', color='m') ax.scatter(1, 1, color='m') ax.text(1, 0, r'$p$', color='m') # Pullbacks ax.plot([1]*2, [-5,1], '--', color='m', alpha=0.5) ax.text(1, -5, r'$a$', color='m') ax.plot([-5, 1], [1]*2, '--', color='m', alpha=0.5) ax.text(-5, 1, 'f(a)', color='m') plt.tight_layout() plt.savefig('second_example_tangent_space.png', transparent=True, dpi=300) plt.close()
Example Note that \(\mathbb{R}^2 = \text{span} \{ (1,0), (0,1) \} = \{ (x,y) | x,y \in \mathbb{R} \}.\)
That means that \(T_p \mathbb{R}^2 = \text{span} \{ \langle 1, 0 \rangle, \langle 0, 1 \rangle \} = \{ \langle dx, dy \rangle | dx, dy \in \mathbb{R} \}.\)
If you want to keep track of the tangent space at different points, you can denote $\langle dx, dy \rangle_p$ for the tangent space at point $p$.
Example Suppose we have $\langle 1, 2 \rangle_{(1,1)}$, we can think of the differentials as another set of axes as visualized below.
The following code made the figure in the previous example.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots() ax.spines[['top', 'right']].set_visible(False) ax.set_xticks([]) ax.set_yticks([]) ax.set_xlim([0,3]) ax.set_ylim([0,3]) ax.annotate("", xy=(2,2), xytext=(1, 1), arrowprops=dict(color='tab:blue')) plt.text(2, 2, r'$\langle c,d \rangle_{(a,b)}$', color='gray') ax.annotate("", xy=(1,2.5), xytext=(1, 0.9), arrowprops=dict(color='m')) ax.annotate("", xy=(2.5,1), xytext=(0.9, 1), arrowprops=dict(color='m')) ax.text(2.5, 1, 'dx', color='grey') ax.text(1, 2.5, 'dy', color='grey') ax.text(2, 1, 'c', color='grey') ax.text(1, 2, 'd', color='grey') ax.text(2, 0, 'a', color='grey') ax.text(0, 2, 'b', color='grey') plt.tight_layout() plt.savefig('third_example_tangent_space.png', transparent=True, dpi=300) plt.close()
Definition A 1-form is a linear function $\omega : T_p \mathbb{R}^n \mapsto \mathbb{R}$.
Proposition For a 1-form $\omega : T_p \mathbb{R}^n \mapsto \mathbb{R}$ it holds that $\omega \in (T_p \mathbb{R})^{*}$ where $(T_p \mathbb{R})^{*}$ is the dual space of the tangent space $T_p \mathbb{R}$.
Example Given $\mathbb{R}^2$ and $T_p \mathbb{R}^2$ and $\omega : T_p \mathbb{R}^2 \mapsto \mathbb{R}$ be linear:
\[\implies \omega (\langle dx, dy \rangle) = adx + bdx = \langle a, b \rangle \cdot \langle dx, dy \rangle\]which also equals
\[\vert| \langle a, b \rangle \vert| \operatorname{scalar\_projection}_{\langle a,b \rangle} \langle dx, dy \rangle .\]This leads to an intuition that a 1-form is a multiple of the scalar projection on to the same line.
Example Let $\omega : T_p \mathbb{R}^n \mapsto \mathbb{R}$ then
\[\omega ( \langle dx_1, \ldots, dx_n \rangle ) = \sum_{i=1}^n a_i dx_i\]
Example Define $\omega (\langle dx, dy \rangle) = 3dx + 2dy.$ What line does $\omega$ project vectors onto?
- The line is in the direction $\langle 3, 2 \rangle$ b/c $\omega ( \langle dx, dy \rangle ) = \langle 3,2 \rangle \cdot \langle dx, dy \rangle$
Notice that $\langle 3,2 \rangle$ is parallel to $\langle 1, \frac{2}{3} \rangle$ which entails that $dy = \frac{2}{3} x.$
Example Suppose $\omega$ scalar projects onto the line $dy = 2dx$ with length of 3. Find $\omega .$
We know that $\omega (\langle dx, dy \rangle) = \langle a,b \rangle \cdot \langle dx, dy \rangle .$ We need $\langle a, b \rangle$ to be parallel to $\langle 1, 2 \rangle$ because $\langle 1, 2 \rangle$ is the vector in the direction of the line $dy = 2dx$.
So $\langle a, b \rangle = \langle a, 2a \rangle$ which needs that have \(\vert| \langle a, 2a \rangle \vert| = 3\) .
So \(\vert| \langle a, 2a \rangle \vert| = \sqrt{a^2 + 4 a^2} = 3 \implies a \sqrt{5} = 3 \implies a = \frac{3}{\sqrt{5}}\) . So $b=\frac{6}{\sqrt{5}}$
\[\implies \omega (\langle dx, dy \rangle) = \frac{3}{\sqrt{5}} dx + \frac{6}{\sqrt{5}} dy\]
Example Supposing $n=2$ and $\omega_1 \wedge \omega_2 : T_p \mathbb{R}^2 \times T_p \mathbb{R}^2 \mapsto \mathbb{R}$ then what is $\omega_1 \wedge \omega_2 (v_1, v_2)$ where $v_1, v_2 \in \mathbb{R}^2$ ?