All Study Guides Riemannian Geometry Unit 2
🌀 Riemannian Geometry Unit 2 – Riemannian Metrics & Induced DistancesRiemannian metrics and induced distances form the backbone of Riemannian geometry. These concepts provide a framework for measuring lengths, angles, and curvature on smooth manifolds, generalizing familiar notions from Euclidean geometry to curved spaces.
Understanding these ideas is crucial for studying geodesics, curvature, and geometric flows. They have wide-ranging applications in physics, computer vision, and machine learning, where they help model complex systems and analyze high-dimensional data.
Key Concepts and Definitions
Riemannian manifold consists of a smooth manifold M M M equipped with a Riemannian metric g g g
Riemannian metric g g g assigns an inner product g p g_p g p to each tangent space T p M T_pM T p M at every point p ∈ M p \in M p ∈ M
Inner product varies smoothly as the point p p p varies
Tangent space T p M T_pM T p M vector space attached to each point p p p of the manifold M M M
Tangent vectors represent velocities of curves passing through the point p p p
Smooth manifold topological space that locally resembles Euclidean space near each point
Transition maps between overlapping coordinate charts are smooth functions
Induced distance function d ( p , q ) d(p,q) d ( p , q ) measures the shortest path length between points p p p and q q q on the manifold
Geodesic shortest path between two points on the manifold, generalizing the concept of a straight line in Euclidean space
Curvature measures the deviation of the manifold from being flat (Euclidean)
Quantifies how parallel transport of vectors along closed loops fails to preserve their angle
Riemannian Manifolds Basics
Riemannian manifold ( M , g ) (M, g) ( M , g ) fundamental object of study in Riemannian geometry
Manifold M M M represents the underlying space
Riemannian metric g g g provides geometric structure
Dimension n n n of a Riemannian manifold number of coordinates needed to specify a point locally
Examples: surface (n = 2 n=2 n = 2 ), space (n = 3 n=3 n = 3 ), spacetime (n = 4 n=4 n = 4 )
Coordinate chart ( U , φ ) (U, \varphi) ( U , φ ) consists of an open subset U ⊂ M U \subset M U ⊂ M and a homeomorphism φ : U → R n \varphi: U \to \mathbb{R}^n φ : U → R n
Allows expressing points in U U U using n n n real coordinates
Atlas collection of coordinate charts that cover the entire manifold M M M
Transition maps between overlapping charts must be smooth
Tangent bundle T M TM TM disjoint union of all tangent spaces T p M T_pM T p M of the manifold M M M
Riemannian manifolds equipped with a natural volume form d V g dV_g d V g induced by the Riemannian metric g g g
Enables integration of functions over the manifold
Riemannian Metrics: Properties and Examples
Riemannian metric g g g assigns an inner product ⟨ ⋅ , ⋅ ⟩ p \langle \cdot, \cdot \rangle_p ⟨ ⋅ , ⋅ ⟩ p to each tangent space T p M T_pM T p M
Inner product symmetric, bilinear, and positive-definite
Components of the Riemannian metric g i j g_{ij} g ij in local coordinates express the inner product of basis vectors ∂ i \partial_i ∂ i and ∂ j \partial_j ∂ j
Matrix representation [ g i j ] [g_{ij}] [ g ij ] symmetric and positive-definite at each point
Euclidean metric g E g_E g E on R n \mathbb{R}^n R n assigns the standard dot product to each tangent space
Components g i j = δ i j g_{ij} = \delta_{ij} g ij = δ ij (Kronecker delta)
Induced metric on a submanifold S ⊂ M S \subset M S ⊂ M obtained by restricting the ambient Riemannian metric to tangent spaces of S S S
Example: sphere S 2 S^2 S 2 with induced metric from Euclidean R 3 \mathbb{R}^3 R 3
Conformal change of metric multiplying a Riemannian metric g g g by a positive smooth function f f f
Preserves angles between vectors but changes lengths
Product metric on a product manifold M × N M \times N M × N obtained by summing the metrics on M M M and N N N
Example: torus S 1 × S 1 S^1 \times S^1 S 1 × S 1 with product metric from circle S 1 S^1 S 1
Riemannian submersion π : ( M , g M ) → ( N , g N ) \pi: (M, g_M) \to (N, g_N) π : ( M , g M ) → ( N , g N ) maps Riemannian manifolds such that the differential d π d\pi d π is an isometry on horizontal subspaces
Useful for constructing new Riemannian manifolds with symmetries
Induced Distances and Length Spaces
Riemannian metric g g g induces a distance function d ( p , q ) d(p,q) d ( p , q ) on the manifold M M M
Measures the infimum of lengths of piecewise smooth curves connecting p p p and q q q
Length of a piecewise smooth curve γ : [ a , b ] → M \gamma: [a,b] \to M γ : [ a , b ] → M given by the integral of the norm of its velocity vector γ ′ ( t ) \gamma'(t) γ ′ ( t )
L ( γ ) = ∫ a b g γ ( t ) ( γ ′ ( t ) , γ ′ ( t ) ) d t L(\gamma) = \int_a^b \sqrt{g_{\gamma(t)}(\gamma'(t), \gamma'(t))} dt L ( γ ) = ∫ a b g γ ( t ) ( γ ′ ( t ) , γ ′ ( t )) d t
Riemannian manifold ( M , g ) (M, g) ( M , g ) becomes a metric space ( M , d ) (M, d) ( M , d ) with the induced distance function d d d
Satisfies axioms of a metric: non-negativity, symmetry, and triangle inequality
Hopf-Rinow theorem states that a connected Riemannian manifold is complete as a metric space if and only if it is geodesically complete
Completeness ensures the existence of minimal geodesics between any two points
Length space ( X , d ) (X, d) ( X , d ) generalizes the concept of a Riemannian manifold
Distance function d d d defined as the infimum of lengths of admissible paths
Intrinsic metric on a length space induced by the length structure
Makes the space a metric space and generalizes the Riemannian distance
Alexandrov space length space with curvature bounded below in the sense of comparison triangles
Generalizes Riemannian manifolds with lower curvature bounds
Geodesics and Shortest Paths
Geodesic γ : I → M \gamma: I \to M γ : I → M locally length-minimizing curve on a Riemannian manifold ( M , g ) (M, g) ( M , g )
Satisfies the geodesic equation ∇ γ ′ γ ′ = 0 \nabla_{\gamma'}\gamma' = 0 ∇ γ ′ γ ′ = 0 , where ∇ \nabla ∇ is the Levi-Civita connection
Exponential map exp p : T p M → M \exp_p: T_pM \to M exp p : T p M → M maps tangent vectors at p p p to points on the manifold along geodesics
Geodesics through p p p correspond to straight lines in T p M T_pM T p M under the exponential map
Minimizing geodesic shortest path between two points on the manifold
May not be unique, as in the case of antipodal points on a sphere
Cut locus C p C_p C p of a point p p p set of points where geodesics from p p p cease to be minimizing
Exponential map is a diffeomorphism from a star-shaped domain in T p M T_pM T p M to M ∖ C p M \setminus C_p M ∖ C p
Geodesic triangle formed by three minimizing geodesics connecting three points on the manifold
Angles and side lengths satisfy triangle comparison theorems based on curvature bounds
Jacobi fields measure the deviation of nearby geodesics
Satisfy the Jacobi equation, a second-order linear ODE involving the Riemann curvature tensor
Rauch comparison theorem relates the growth of Jacobi fields to the curvature of the manifold
Used to prove the Toponogov triangle comparison theorem and the sphere theorem
Curvature in Riemannian Geometry
Riemann curvature tensor R ( X , Y ) Z R(X, Y)Z R ( X , Y ) Z measures the non-commutativity of the covariant derivative ∇ \nabla ∇
Quantifies the failure of parallel transport along infinitesimal parallelograms
Sectional curvature K ( X , Y ) K(X, Y) K ( X , Y ) measures the Gaussian curvature of the 2-plane spanned by orthonormal vectors X X X and Y Y Y
Computed as K ( X , Y ) = ⟨ R ( X , Y ) Y , X ⟩ ⟨ X , X ⟩ ⟨ Y , Y ⟩ − ⟨ X , Y ⟩ 2 K(X, Y) = \frac{\langle R(X, Y)Y, X \rangle}{\langle X, X \rangle \langle Y, Y \rangle - \langle X, Y \rangle^2} K ( X , Y ) = ⟨ X , X ⟩ ⟨ Y , Y ⟩ − ⟨ X , Y ⟩ 2 ⟨ R ( X , Y ) Y , X ⟩
Ricci curvature Ric ( X , X ) \text{Ric}(X, X) Ric ( X , X ) trace of the Riemann curvature tensor in the direction of the vector X X X
Measures the average sectional curvature of planes containing X X X
Scalar curvature S S S contraction of the Ricci curvature with the metric tensor
Provides a single real number measuring the overall curvature at each point
Constant curvature manifolds have the same sectional curvature K K K at every point and for every 2-plane
Examples: Euclidean space (K = 0 K=0 K = 0 ), sphere (K > 0 K>0 K > 0 ), hyperbolic space (K < 0 K<0 K < 0 )
Comparison theorems relate the geometry of a Riemannian manifold to that of constant curvature spaces
Bonnet-Myers theorem, Synge's theorem, and the Cartan-Hadamard theorem
Curvature affects the behavior of geodesics, Jacobi fields, and the topology of the manifold
Positive curvature implies compact manifolds and converging geodesics
Negative curvature implies non-compact manifolds and diverging geodesics
Applications and Real-World Examples
General relativity models spacetime as a 4-dimensional Lorentzian manifold with a metric tensor
Curvature of spacetime related to the presence of matter and energy via Einstein's field equations
Riemannian geometry used in computer vision and image processing
Treats images as 2D manifolds and uses geodesic distances for segmentation and registration
Machine learning and data analysis utilize Riemannian geometry
Manifold learning algorithms (Isomap, LLE) seek to uncover the underlying low-dimensional structure of high-dimensional data
Robotics and motion planning benefit from Riemannian geometry
Configuration spaces of robots modeled as Riemannian manifolds
Geodesics used to plan optimal trajectories and avoid obstacles
Geometric mechanics formulates classical mechanics on Riemannian manifolds
Configuration space of a mechanical system treated as a Riemannian manifold with a kinetic energy metric
Lie groups equipped with bi-invariant Riemannian metrics
Geodesics correspond to one-parameter subgroups and the exponential map is the Lie exponential
Optimization on Riemannian manifolds generalizes classical optimization techniques
Gradient descent, conjugate gradient, and trust-region methods adapted to curved spaces
Shape analysis and computational anatomy study the geometry of shapes and anatomical structures
Shapes represented as points on infinite-dimensional Riemannian manifolds of diffeomorphisms or embeddings
Advanced Topics and Further Reading
Comparison geometry studies Riemannian manifolds with curvature bounds
Alexandrov spaces, CAT(k k k ) spaces, and Gromov-Hausdorff convergence
Geometric flows evolve Riemannian metrics according to PDEs
Ricci flow, mean curvature flow, and Yamabe flow
Used to prove the Poincaré conjecture and the geometrization conjecture
Harmonic maps between Riemannian manifolds generalize the concept of harmonic functions
Minimize the energy functional and satisfy the harmonic map equation
Eigenvalue problems on Riemannian manifolds study the spectrum of the Laplace-Beltrami operator
Relate the geometry of the manifold to the properties of eigenfunctions and eigenvalues
Spin geometry studies Riemannian manifolds with additional structure called a spin structure
Allows the construction of spinor bundles and Dirac operators
Kähler geometry studies complex manifolds with a compatible Riemannian metric
Unifies Riemannian, complex, and symplectic geometry
Finsler geometry generalizes Riemannian geometry by allowing the metric to depend on direction
Finsler metrics assign a norm to each tangent space instead of an inner product
Sub-Riemannian geometry studies manifolds with a distribution of subspaces of the tangent spaces
Geodesics are constrained to be tangent to the distribution
Further reading:
"Riemannian Geometry" by Manfredo P. do Carmo
"Riemannian Manifolds: An Introduction to Curvature" by John M. Lee
"Comparison Geometry" by Karsten Grove
"Geometric Flows and Geometric Operators" by Bennett Chow and Dan Knopf