# 22. Riemannian and pseudo-Riemannian manifolds¶

This notebook is part of the Introduction to manifolds in SageMath by Andrzej Chrzeszczyk (Jan Kochanowski University of Kielce, Poland).

In :
version()

Out:
'SageMath version 9.6, Release Date: 2022-05-15'

Let $M$ be a smooth manifold and $g$ a covariant tensor field of rank two: $g\in T^{(0,2)}M.$ We call $g$ symmetric if $$g_p(X_p,Y_p)=g_p(Y_p,X_p)\quad \text{for } X_p,Y_p\in T_pM,\ p\in M.$$

We call $g$ positive definite if $$g_p(X_p,X_p)\geq 0\quad \text{for all}\ X_p\in T_pM,\ p\in M$$ and $$g_p(X_p,X_p)=0\quad \text{implies } X_p=0_p\in T_pM.$$

The tensor field $g$ is non-singular if for all $p\in M$

$$g_p(X_p,Y_p)=0\quad \text{for all } Y_p\in T_pM\quad \text{implies } X_p=0_p\in T_pM.$$

Note that the positive-definiteness implies the non-singularity:
if $g_p(X_p,Y_p)=0\quad \text{for all } Y_p\in T_pM,\$ then $\ g_p(X_p,X_p)=0\$ and consequently $X_p=0_p$.

A pseudo-Riemannian manifold is a smooth manifold $M$ with a non-singular, symmetric, smooth tensor field $g\in T^{(0,2)}M$, called metric of $M$.

If the metric is positive definite we use the name Riemannian manifold.

Example 22.1

Metric method in SageMath:

In :
                       # Riemannian manifold M
M = Manifold(2, 'M', structure='Riemannian'); M

Out:
2-dimensional Riemannian manifold M
In :
g = M.metric(); g       # metric on M

Out:
Riemannian metric g on the 2-dimensional Riemannian manifold M

In a Riemannian manifold, $M$, with a metric $g$, the bilinear form $g_p(\cdot,\cdot)$ is an inner product on $T_pM$. The norm or length of a tangent vector $X_p ∈ T_p M$ is defined by

\begin{equation} \|X_p\| = \sqrt{g_p (X_p , X_p )}, \tag{22.1} \end{equation}

and the length of a curve $\gamma : [a, b] → M$ is defined by

\begin{equation} L_\gamma=\int_a^b\|\gamma'_t\|dt. \tag{22.2} \end{equation}

If $M$ is a Riemannian manifold and $(x^1,\ldots,x^n)$ are local coordinates, then the metric is given by

\begin{equation} g = g_{i j} dx^i ⊗ dx^j, \tag{22.3} \end{equation}

where $g_{ij}=g(\frac{\partial}{\partial x^i},\frac{\partial}{\partial x^j})$ (this is a special case of (13.7) ).

The standard metric on $R^n$ with Cartesian coordinates is defined by

\begin{equation} g = \delta_{ij}dx^i\otimes dx^j=dx^1 ⊗ dx^1 + · · · + dx^n ⊗ dx^n , \tag{22.4} \end{equation}

Example 22.2

Let us start from the standard metric in 2-dimensional Euclidean space

In :
E.<x,y> = EuclideanSpace()  # Euclidean space E^2
g = E.metric()              # standard metric on E^2
g.disp()

Out:
g = dx⊗dx + dy⊗dy

and use (22.2) to compute the length of the curve c defined in Cartesian coordinates by

$$x(t)=\sin(t),\quad y(t)=\sin(2t)/2,\quad t\in (0,2\pi),$$

(in simple cases the exact integral can be computed in SageMath, but not in the present case).

In :
E.<x,y> = EuclideanSpace()  # Euclidean space E^2
t = var('t')                # symbolic variable for c
c = E.curve({E.cartesian_coordinates():[sin(t), sin(2*t)/2]},
(t, 0, 2*pi), name='c') # define curve in Cart. coord.
v=c.tangent_vector_field()  # vector field of tangent vect. to c
w=v.norm().expr()           # norm of v
numerical_integral(w,0,2*pi)  # numerical version of (22.2)

Out:
6.097223470104982

If the curve is simple, for example

$$x(t)=\sin(2t),\quad y(t)=\cos(2t),\quad t\in (0,2\pi),$$

then we don't need numerical tools:

In :
%display latex
E.<x,y> = EuclideanSpace()  # Euclidean space E^2
t = var('t')                # symbolic variable for c
c = E.curve({E.cartesian_coordinates():[sin(2*t), cos(2*t)]},
(t, 0, 2*pi), name='c') # define curve in Cart. coord.
v=c.tangent_vector_field()  # vector field of tangent vect. to c
w=v.norm().expr()           # norm of v
w.integral(t,0,2*pi)        # exact version of (22.2)

Out:
$\displaystyle 4 \, \pi$

Example 22.3

The standard metric in 4-dimensional Euclidean space is predefined.

In :
%display latex
E=EuclideanSpace(4)         # Euclidean space E^4
E.metric().disp()           # standard metric on E^4

Out:
$\displaystyle g = \mathrm{d} {x_{1}}\otimes \mathrm{d} {x_{1}}+\mathrm{d} {x_{2}}\otimes \mathrm{d} {x_{2}}+\mathrm{d} {x_{3}}\otimes \mathrm{d} {x_{3}}+\mathrm{d} {x_{4}}\otimes \mathrm{d} {x_{4}}$

If we need upper indices and more general manifolds, then we can use the commands:

In :
N = 3                       # dimension of manifold
# variables with superscripts:
M = Manifold(N, 'M')        # manifold M
X = M.chart(' '.join(['x'+str(i)+':x^{'+str(i)+'}' for i in range(N)]))  # chart on M
g = M.metric('g')           # metric g on M
g[0,0], g[1,1], g[2,2] = 1, 1, 1  # components of g
g.disp()                    # show g

Out:
$\displaystyle g = \mathrm{d} {x^{0}}\otimes \mathrm{d} {x^{0}}+\mathrm{d} {x^{1}}\otimes \mathrm{d} {x^{1}}+\mathrm{d} {x^{2}}\otimes \mathrm{d} {x^{2}}$

### Determinant of of $[g_{ij}]$¶

If in the implication $$g_p(X_p,Y_p)=0\ \text{for all } Y_p\quad \Rightarrow X_p=0,$$

we put $X_p=a^i\frac{\partial}{\partial x^i}\big|_p, \quad Y_p=\frac{\partial}{\partial x^j}\big|_p$, then we obtain

$$g_{i j} ( p)\, a^i = 0,\quad j=1,\ldots,n\quad \Rightarrow a^i=0, \ i=1,\ldots,n,$$

which means that the homogeneous system of linear equations for the unknowns $a^i$ admits only the zero solutions i.e.,

$$\det [g_{i,j}](p)\not=0,$$

for all $p$ in the the coordinate domain.

### Pullback of a metric¶

If $M$ is a Riemannian manifold with a positive definite metric tensor $g$ and $ψ :N → M$ is a smooth map such that for all $p ∈ N ,\ dψ_p$ has maximal rank, that is,

$$dψ_p X_p = 0_{ψ( p)}\quad \text{implies}\ \ X_p = 0_p,$$

then the pullback $ψ^∗ g$ is a positive definite metric tensor in $N$.

In fact since $(ψ^∗ g)_p (X_p , Y_p ) ≡ g_{ψ( p)} (dψ_p X_p , dψ_p Y_p )$, and $g$ is symmetric then $\psi^*g$ is symmetric. Furthermore, if $(ψ^*g)_p (X_p , Y_p ) = 0$ for all $Y_p ∈ T_p N$ , from the definition of $ψ^*g$, we have $g_{ψ( p)} (dψ_p X_p , dψ_p Y_p ) = 0$ for all $Y_p ∈ T_p N$, In particular taking $X_p = Y_p$ and using the fact that $g$ is positive definite we see that $dψ_p X_p = 0_{\psi(p)}$, and therefore $X_p = 0_p$ .

### Immersions and embeddings¶

A smooth mapping with maximal rank is called an immersion (in other words, $ψ : N → M$ is an immersion if for all $p ∈ N$, the rank of the linear mapping $dψ_p$ is equal to the dimension of $N$ ).
The smooth map $\ \psi: N\to M\$ is an embedding if it is one-to one immersion and the image $\psi(N)$ with the subspace topology is homeomorphic to $N$ under $\psi$.

Example 22.4 The metric on the standard sphere $S^2$:

In :
%display latex
M = Manifold(3, 'R^3')       # manifold M=R^3
c_xyz.<x,y,z> = M.chart()    # Cartesian coordinates
N = Manifold(2, 'N')         # manifold N=S^2
# chart on N:
c_sph.<theta,phi>=N.chart(r'th:(0,pi):\theta ph:(0,2*pi):\phi')
psi = N.diff_map(M, (sin(theta)*cos(phi),
sin(theta)*sin(phi),cos(theta)),name='psi',
latex_name=r'\psi')      # embedding S^2 -> R^3
g=M.metric('g')              # standard metric on R^3
g[0,0],g[1,1],g[2,2]=1,1,1   # components of g
plb=psi.pullback(g)          # pullback of g
plb.display()                # show metric on S^2

Out:
$\displaystyle {\psi}^*g = \mathrm{d} {\theta}\otimes \mathrm{d} {\theta} + \sin\left({\theta}\right)^{2} \mathrm{d} {\phi}\otimes \mathrm{d} {\phi}$

It is easy to check that $\psi$ is of maximal rank:

In :
psi.jacobian_matrix().rank()

Out:
$\displaystyle 2$

Example 22.5

The metric on the paraboloid:

In :
M = Manifold(3, 'R3')        # manifold M=R^3
c_xyz.<x,y,z> = M.chart()    # Cartesian coordinates
N=Manifold(2,name='R2')      # manifold N; paraboloid
c_uv.<u,v>=N.chart()         # coordinates on N
g = M.metric('g');           # metric in R^3
g[:]=[[1,0,0],[0,1,0],[0,0,1]];     # components of g
psi = N.diff_map(M, (u, v,u^2+v^2),
name='psi',latex_name=r'\psi')  # embedding N->R^3
plb=psi.pullback(g)          # pullback of g
plb.display()                # show metric on paraboloid

Out:
$\displaystyle {\psi}^*g = \left( 4 \, u^{2} + 1 \right) \mathrm{d} u\otimes \mathrm{d} u + 4 \, u v \mathrm{d} u\otimes \mathrm{d} v + 4 \, u v \mathrm{d} v\otimes \mathrm{d} u + \left( 4 \, v^{2} + 1 \right) \mathrm{d} v\otimes \mathrm{d} v$
In :
# check if psi is of maximal rank
J = psi.jacobian_matrix()
J.rank()

Out:
$\displaystyle 2$

Example 22.6

The metric on the hyperboloid:

In :
M = Manifold(3, 'R3')        # manifold M=R^3
c_xyz.<x,y,z> = M.chart()    # Cartesian coordinates
N=Manifold(2,name='R2')      # manifold N; hyperboloid
c_uv.<u,v>=N.chart()         # coordinates on N
g = M.metric('g');           # metric in R^3
g[:]=[[1,0,0],[0,1,0],[0,0,1]];     # components of g
psi = N.diff_map(M, (u, v,u^2-v^2),
name='psi',latex_name=r'\psi')  # embedding N->R^3
plb=psi.pullback(g)          # pullback of g
plb.display()                # show metric on hyperboloid

Out:
$\displaystyle {\psi}^*g = \left( 4 \, u^{2} + 1 \right) \mathrm{d} u\otimes \mathrm{d} u -4 \, u v \mathrm{d} u\otimes \mathrm{d} v -4 \, u v \mathrm{d} v\otimes \mathrm{d} u + \left( 4 \, v^{2} + 1 \right) \mathrm{d} v\otimes \mathrm{d} v$
In :
# check if psi is of maximal rank
J=psi.jacobian_matrix()
J.rank()

Out:
$\displaystyle 2$

### Levi-Civita connection¶

If $M$ is a Riemannian manifold, then there exists a unique connection $∇$ with vanishing torsion and such that $∇_X g = 0$ for all $X ∈ \mathfrak{X}(M)$. Connections with this properties are called Riemannian or Levi-Civita connections.

From the definition (21.13) of torsion ($T (X, Y) = ∇_X Y − ∇_Y X − [X, Y]$) it follows that the torsion vanishes iff \begin{equation} [X, Y] = ∇_X Y − ∇_Y X. \tag{22.5} \end{equation}

Recall that connections satisfying (22.5) are called torsion free or symmetric.

From the rule of covariant differentiation of covariant tensor fields (21.14) it follows that

$$(∇_X g)(Y,Z)=X (g (Y,Z))-g(∇_X Y,Z)-g(Y,∇_X Z),$$

so $∇_X g$ vanishes iff

\begin{equation} X (g(Y, Z)) = g(∇_X Y, Z) + g(Y, ∇_X Z). \tag{22.6} \end{equation}

A connection $\ \nabla\$ in a Riemannian manifold $M$ is said to be compatible with the metric if (22.6) is true for all $X,Y,Z\in \mathfrak{X}(M)$.

One can prove that:

On any Riemannian manifold $M$ there exists a unique connection for which (22.5),(22.6) hold for $X, Y, Z ∈ \mathfrak{X}(M).$

Let us assume that (22.5),(22.6) hold true. We have

$$X( g(Y, Z)) +Y( g(Z, X)) − Z (g(X, Y))\\ = g(∇_X Y, Z) + g(Y, ∇_X Z) + g(∇_Y Z, X) + g(Z, ∇_Y X) − g(∇_Z X, Y) − g(X, ∇_Z Y)\\ = g(∇_X Y + ∇_Y X, Z) + g(Y, ∇_X Z − ∇_Z X) + g(X, ∇_Y Z − ∇_Z Y)\\ = g(∇_X Y + ∇_X Y+(\nabla_Y X-\nabla_X Y), Z) + g(Y, ∇_X Z − ∇_Z X) + g(X, ∇_Y Z − ∇_Z Y)\\ = g(∇_X Y + ∇_X Y + [Y, X], Z) + g(Y, [X, Z]) + g(X, [Y, Z])\\ = 2g(∇_X Y, Z) + g(Z, [Y, X]) + g(Y, [X, Z]) + g(X, [Y, Z]).$$

The obtained equality implies the following Koszul formula

\begin{equation} 2g(∇_X Y, Z) = X (g(Y, Z)) + Y (g(Z, X)) − Z (g(X, Y))\\ − g (Z, [Y, X]) − g (Y, [X, Z]) − g( X, [Y, Z]). \tag{22.7} \end{equation}

Since $g$ is non-singular if such a $g$ exists, it defines the connection $∇_X Y$ in an unique manner.

Now let us assume that $\nabla_X Y$ is defined by (22.7). We have

$$2g(\nabla_XY,Z)-2g(\nabla_Y X,Z)=\\ X(g(Y,Z))+Y(g(Z,X))-Z(g(X,Y)) -g(Z,[Y,X])-g(Y,[X,Z])-g(X,[Y,Z])\\ -Y(g(X,Z))-X(g(Z,Y))+Z(g(Y,X)) +g(Z,[X,Y])+g(X,[Y,Z])+g(Y,[X,Z])\\ =2g([X,Y],Z),$$

thus (22.5) is fulfilled.

To check (22.6) let us note that

$$2g(\nabla_XY,Z)+2g(Y,\nabla_XZ)=2g(\nabla_XY,Z)+2g(\nabla_XZ,Y)\\ =X(g(Y,Z))+Y(g(Z,X))-Z(g(X,Y))-g(Z,[Y,X])-g(Y,[X,Z])-g(X,[Y,Z])\\ +X(g(Z,Y))+Z(g(Y,X))-Y(g(X,Z))-g(Y,[Z,X])-g(Z,[X,Y])-g(X,[Z,Y])\\ =2X(g(Y,Z)).$$

### Levi-Civita connection in components¶

Let $g_{ij}=g\big(\frac{\partial}{\partial x^i},\frac{\partial}{\partial x^j}\big)$. Recall from the notebook 12 that $[\frac{\partial}{\partial x^i},\frac{\partial}{\partial x^j}]=0.$

If we put in (22.7) . $$X=\frac{\partial}{\partial x^i},\quad Y=\frac{\partial}{\partial x^j},\quad Z=\frac{\partial}{\partial x^k},$$ then

$$2g\Big(\nabla_{\frac{\partial}{\partial x^i}} \frac{\partial}{\partial x^j},\frac{\partial}{\partial x^k}\Big)\\ =\frac{\partial}{\partial x^i} \big(g(\frac{\partial}{\partial x^j},\frac{\partial}{\partial x^k}\big)\big) + \frac{\partial}{\partial x^j} \big(g(\frac{\partial}{\partial x^k},\frac{\partial}{\partial x^i}\big)\big) - \frac{\partial}{\partial x^k} \big(g(\frac{\partial}{\partial x^i},\frac{\partial}{\partial x^j}\big)\big)\\ =\frac{\partial g_{jk}}{\partial x^i}+ \frac{\partial g_{ki}}{\partial x^j}- \frac{\partial g_{ij}}{\partial x^k}.$$

Using the formula $\nabla_{\frac{\partial}{\partial x^i}} \frac{\partial}{\partial x^j}=\Gamma^m_{ji}\frac{\partial}{\partial x^m}$ we obtain

$$2\Gamma^m_{ji}g_{mk}= \frac{\partial g_{jk}}{\partial x^i}+ \frac{\partial g_{ki}}{\partial x^j}- \frac{\partial g_{ij}}{\partial x^k},$$

and finally \begin{equation} \Gamma^m_{ji}=\frac{1}{2}g^{km}\Big( \frac{\partial g_{jk}}{\partial x^i}+ \frac{\partial g_{ki}}{\partial x^j}- \frac{\partial g_{ij}}{\partial x^k} \Big), \tag{22.8} \end{equation}

where $[g^{km}]$ is the matrix inverse to $[g_{mk}]$.

Note that from (22.8) it follows that $\Gamma_{ij}^k=\Gamma_{ji}^m$.

Note that a symmetric $n\times n$ matrix has $n(n+1)/2$ independent elements and $\Gamma^m_{ij}$ defines $n$ such matrices, so the Riemannian manifolds have $n^2(n+1)/2$ independent Christoffel symbols.

### Geodesics in Riemannian manifolds¶

In the case of Riemannian connections SageMath offers integrated_geodesic, a numerical method of finding geodesics

Example 22.7

Use integrated_geodesic method to find the geodesics on Poincaré half-plane passing through the point with coordinates $(0,1)$.

Let us start from the geodesic with tangent vector at $(0,1)$ parallel to $x$-axis.

In :
M = Manifold(2,'M',structure='Riemannian') # Riemannian manifold M
X.<x,y> = M.chart('x y:(0,+oo)')           # Poincare half-plane
g = M.metric()                             # metric on M
g[0,0], g[1,1] = 1/y^2, 1/y^2              # nonzero components of the metric
p = M((0,1), name='p')                     # point p
t = var('t')                               # symbolic variable for geodesic
v = M.tangent_space(p)((1,0), name='v')    # tang. vect. at p, comp.: (1,0)
v1 = M.tangent_space(p)((-1,0), name='v1') # tang. vect. at p, comp.: (-1,0)
c = M.integrated_geodesic(g, (t, 0, 2), v, name='c')    # geodesic with init.vect. v
c1 = M.integrated_geodesic(g, (t, 0, 2), v1, name='c1') # geodesic with init.vect. v1
sol = c.solve()                            # find the geodesic facing right
interp = c.interpolate()                   # interpolate the result
p0=c.plot_integrated(thickness=2)          # plot the geodesic c
sol1=c1.solve()                            # find the geodesic facing left
interp1 = c1.interpolate()                 # interpolate the result
p1=c1.plot_integrated(thickness=2)         # plot the geodesic c1
(p0+p1).show(aspect_ratio=1,figsize=[4,4]) # combine plots Now let us show how the other geodesics through the same point may look.

First we repeat the six commands defining the Poincaré half-plane.

In :
M = Manifold(2, 'M', structure='Riemannian')
X.<x,y> = M.chart('x y:(0,+oo)')
g = M.metric()
g[0,0], g[1,1] = 1/y^2, 1/y^2
p = M((0,1), name='p')
t = var('t')


Next we plot 6 geodesics facing right, 6 geodesics facing left, one geodesic facing up and one geodesic pointing down.

In :
Gr=[]                                        # list of plots

for y in range(-1,5):                        # 6 geodesics pointing facing *right*
v = M.tangent_space(p)((1,y),name='v')   # initial tangent vector
c = M.integrated_geodesic(g,(t,0,2),v,name='c')   # define geodesic
sol = c.solve()                          # find the geodesic points
interp = c.interpolate()                 # interpolate the result
gr=c.plot_integrated(thickness=2)        # plot the geodesic
Gr=Gr+[gr]                               # add the plot to the list of plots

for y in range(-1,5):                        # 6 geodesics pointing facing *left*
v = M.tangent_space(p)((-1,y), name='v')
c = M.integrated_geodesic(g,(t,0,2),v,name='c')   # comments as above
sol = c.solve()
interp = c.interpolate()
gr=c.plot_integrated(thickness=2)
Gr=Gr+[gr]
# vertical geodesic facing up
v = M.tangent_space(p)((0,1), name='v')
c = M.integrated_geodesic(g,(t,0,2),v,name='c')      # comments as above
sol = c.solve()
interp = c.interpolate()
gr=c.plot_integrated(thickness=3)
Gr=Gr+[gr]
# vertical geodesic pointing down
v = M.tangent_space(p)((0,-1), name='v')
c = M.integrated_geodesic(g,(t,0,2),v,name='c')      # comments as above
sol = c.solve()
interp = c.interpolate()
gr=c.plot_integrated(thickness=2)
Gr=Gr+[gr]

sum(Gr).show(aspect_ratio=1)                 # combine all plots As we can see (one can suspect that) geodesics in Poincare half-plane are semicircles or vertical half-lines.

Example 22.8

Show (numerically) that one of the geodesics on the sphere $S^2$ is the "equator".

In :
N = Manifold(2, 'N')         # manifold N=S^2
c_sph.<theta,phi>=N.chart()  # spherical coordinates
g=N.metric('g')              # metric on S^2
g[0,0],g[1,1]=1,sin(theta)^2 # components of g
p = N((pi/2,pi), name='p')   # initial point on S^2
t = var('t')                 # symbolic variable for geodesic
v = N.tangent_space(p)((0,1), name='v') # tang.vector at p
# define geodesic
c = N.integrated_geodesic(g, (t, 0, 2*pi), v, name='c')
sol = c.solve()              # find the geodesic points
interp = c.interpolate()     # interpolate the result


The geodesic is computed, but to show a 3-dimensional picture we need the embedding $\psi: S^2\to R^3$.

In :
M = Manifold(3, 'R^3')       # manifold M=R^3
c_xyz.<x,y,z> = M.chart()    # Cartesian coordinates
psi = N.diff_map(M, (sin(theta)*cos(phi),
sin(theta)*sin(phi),cos(theta)),
name='psi',latex_name=r'\psi') # embedding s^2 -> R^3
# plot the geodesic:
p1=c.plot_integrated(c_xyz,mapping=psi,thickness=3,color='red',
plot_points=200, aspect_ratio=1,label_axes=False)
p2=sphere(color='lightgrey',opacity=0.6)  # plot sphere
(p1+p2).show(frame=False)    # combine plots


## What's next?¶

Take a look at the notebook Curvature.