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Riemannian optimization on stiefel and grassmann manifolds — Examples
1 · Prerequisites
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Compactness
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Conjugate Gradients, MINRES and Preconditioning
- Connectedness
- Constant Rank, Submersions, Immersions and Regular Level Sets
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Cw Complexes and Cellular Homology
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalue Iterations and the QR Algorithm
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Fibrations Fiber Bundles and Homotopy Exact Sequences
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Fundamental Trigonometric Identities
- Gaussian Elimination, Elementary Matrices and Reduced Row Echelon Form
- Gradient Like Vector Fields and Morse Trajectories
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hereditary and Productive Behaviour of the Separation Axioms
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Krylov methods and Lanczos convergence
- Krylov Subspaces, Arnoldi and GMRES
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Mixed Partials, Taylor Formulae, and Extrema
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Morse Critical Points Hessians and Indices
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Partitions of Unity and Paracompactness
- Polynomial Rings, the Division Algorithm and Roots
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Products Segre and Veronese Embeddings and Grassmannians
- Properties of the Integral and the Working FTC
- Rank Theorems and Embedded Submanifolds
- Relations, Functions, and Quotients
- Riemannian Metrics Length Distance and Volume
- Riemannian optimization on stiefel and grassmann manifolds
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Separation Axioms: the Hierarchy
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Simple Field Extensions and the Construction of the Complex Numbers
- Smooth Manifolds and Smooth Maps
- Smooth Vector Bundles and Sections
- Splitting Fields
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- Tangent Cotangent and the Differential
- Tensor Fields Exterior Algebra and Differential Forms
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Fundamental Theorems of Calculus
- The Galois Correspondence
- The Inverse and Implicit Function Theorems
- The Inverse Function Theorem Completed
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topological Vector Bundles and Grassmannian Classification
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Uniform Spaces: the Three Definitions
- Vector Fields Flows and Lie Derivatives
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
These examples work through a trace eigenspace objective and a Krylov start-angle bound, a strict saddle on the sphere-shaped Stiefel manifold, and the continuum of minimizers produced by a repeated eigenvalue.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
A repeated eigenvalue gives a continuum of Grassmann minimizers
Example
Let and minimize the rank-one trace objective on . Every line in is a global minimizer. The repeated bottom eigenvalue therefore produces a continuum of minimizers represented by one Grassmann critical set.
Facts & Assumptions
Given: The matrix and the rank-one Grassmann objective in the Example.
A Grassmann objective must be invariant under changing an orthonormal frame of the same subspace, and its gradient is the horizontal projection (Projected Euclidean gradients are the Stiefel and Grassmann gradients).
The horizontal tangent at a unit vector consists of vectors with (Tangent spaces and Frobenius projections on Stiefel and Grassmann).
Proof
For , , so the objective is well-defined on lines by [F1]. Since is positive semidefinite, for every unit . If , then and , so every such line is a global minimizer.
The vectors for are unit and give distinct lines: two such vectors span the same line only when their angles differ by a multiple of , which cannot occur for distinct parameters in this half-open interval. Thus they parametrize continuum many distinct lines. At each such , , so the ambient gradient and its horizontal projection vanish. The family is a continuum of Grassmann critical minimizers.
A strict saddle of a quadratic Stiefel objective
Example
For , minimize on . The point is critical, but one unit-speed tangent curve decreases and another increases it. Thus it is a strict saddle, not a local minimum.
Facts & Assumptions
Given: The unit sphere, the symmetric matrix and the objective in the Example.
The Riemannian gradient of a smooth ambient objective is its Euclidean gradient projected onto the Stiefel tangent (Projected Euclidean gradients are the Stiefel and Grassmann gradients).
At a unit frame , the tangent space is (Tangent spaces and Frobenius projections on Stiefel and Grassmann).
Proof
The ambient gradient is , so . Its Stiefel projection is by [F1]; hence is critical.
Both and are tangent at by [F2]. The curves and stay on the unit sphere and have initial velocities . Their objective values are and for all sufficiently small nonzero . Thus arbitrarily nearby feasible points have smaller and larger values, proving strict saddle behavior.
A trace eigenspace objective and a Lanczos start-angle bound
Example
Let and minimize on . The top eigenspace is , so the optimum is . Start Lanczos at and use the two-dimensional Krylov space. The normalized vector generated by , with , has Rayleigh quotient for , objective value , and objective gap . The Chebyshev start-angle theorem bounds the largest Ritz-value gap by . Direct arithmetic also gives for this explicit candidate; the candidate need not maximize the Rayleigh quotient on the Krylov space.
Facts & Assumptions
Given: The diagonal matrix , unit starting vector , and the Stiefel trace objective above.
The Stiefel gradient is the tangent projection of the ambient Euclidean gradient (Projected Euclidean gradients are the Stiefel and Grassmann gradients).
For a simple top eigenvalue, a nonzero top-eigenspace component of the start vector gives the stated Chebyshev Ritz-value error bound (Lanczos largest Ritz values obey a Chebyshev start-angle bound, including degenerate branches).
Proof
The ambient gradient of is . At , the Stiefel projection in [F1] is zero, so the eigendirection is stationary; at the global minimizer , . For the displayed start, is nonzero and belongs to .
The squared norm of this vector is , and its -quadratic form is . Dividing gives Rayleigh quotient , hence objective and gap .
Here , , , the top multiplicity is one, and . Since step 1.1 places the candidate in , its quotient shows . For Krylov dimension , the theorem [F2] gives Since , its Ritz gap obeys , so the candidate comparison and source bound agree. Also and are independent because the third coordinate of is zero while that of is nonzero, so the two-dimensional Krylov space is attained without breakdown.
Sources
- P.-A. Absil, R. Mahony, and R. Sepulchre, Optimization Algorithms on Matrix Manifolds, Chapter 3 (2008)
- Nicolas Boumal, An Introduction to Optimization on Smooth Manifolds (2023 prepublication)
- Yousef Saad, Iterative Methods for Sparse Linear Systems, second edition
- David Bindel, Matrix Computations (CS 6210), Cornell University, Lectures 38–39 (2012)