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Riemannian optimization on stiefel and grassmann manifolds — Examples

1 · Prerequisites

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

ExampleConstruction: Literature-sourcedVerification: AI-generatedOpen item page →

A repeated eigenvalue gives a continuum of Grassmann minimizers

Example

Let A=diag⁡(0,0,2) and minimize the rank-one trace objective f([x])=xTAx on Gr⁡(1,3). Every line in span⁡(e1,e2) 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 A and the rank-one Grassmann objective in the Example.

[F1]

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).

[F2]

The horizontal tangent at a unit vector x consists of vectors z with xTz=0 (Tangent spaces and Frobenius projections on Stiefel and Grassmann).

Proof

technique · evaluate the Rayleigh quotient and its horizontal gradient
1.1F1givenalgebra

For q=±1∈O⁡(1), (xq)TA(xq)=xTAx, so the objective is well-defined on lines by [F1]. Since A is positive semidefinite, f([x])≥0 for every unit x. If x∈span⁡(e1,e2), then Ax=0 and f([x])=0, so every such line is a global minimizer.

2.1F1F2givenstep 1.1algebra∎

The vectors x=(cos⁡θ)e1+(sin⁡θ)e2 for 0≤θ<π are unit and give distinct lines: two such vectors span the same line only when their angles differ by a multiple of π, which cannot occur for distinct parameters in this half-open interval. Thus they parametrize continuum many distinct lines. At each such x, Ax=0, so the ambient gradient 2Ax and its horizontal projection vanish. The family is a continuum of Grassmann critical minimizers.

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A strict saddle of a quadratic Stiefel objective

Example

For A=diag⁡(0,1,2), minimize f(x)=xTAx on St⁡(3,1)=S2. The point e2 is critical, but one unit-speed tangent curve decreases f and another increases it. Thus it is a strict saddle, not a local minimum.

Facts & Assumptions

Given: The unit sphere, the symmetric matrix A and the objective in the Example.

[F1]

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).

[F2]

At a unit frame x, the tangent space is {z:xTz=0} (Tangent spaces and Frobenius projections on Stiefel and Grassmann).

Proof

technique · compute the projected gradient and the objective along two feasible curves
1.1F1givenalgebra

The ambient gradient is ∇f(x)=2Ax, so ∇f(e2)=2e2. Its Stiefel projection is 2e2−e2(e2T2e2)=0 by [F1]; hence e2 is critical.

2.1F2givenalgebra∎

Both e1 and e3 are tangent at e2 by [F2]. The curves x−(t)=cos⁡(t)e2+sin⁡(t)e1 and x+(t)=cos⁡(t)e2+sin⁡(t)e3 stay on the unit sphere and have initial velocities e1,e3. Their objective values are f(x−(t))=cos⁡2t<1 and f(x+(t))=cos⁡2t+2sin⁡2t=1+sin⁡2t>1 for all sufficiently small nonzero t. Thus arbitrarily nearby feasible points have smaller and larger values, proving strict saddle behavior.

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A trace eigenspace objective and a Lanczos start-angle bound

Example

Let A=diag⁡(5,2,0) and minimize f(x)=−xTAx on St⁡(3,1)=S2. The top eigenspace is span⁡(e1), so the optimum is −5. Start Lanczos at b=(1,1,1)T/3 and use the two-dimensional Krylov space. The normalized vector generated by p(A)b, with p(λ)=(λ−1)/4, has Rayleigh quotient 41/9 for A, objective value −41/9, and objective gap 4/9. The Chebyshev start-angle theorem bounds the largest Ritz-value gap by 5/8. Direct arithmetic also gives 4/9≤5/8 for this explicit candidate; the candidate need not maximize the Rayleigh quotient on the Krylov space.

Facts & Assumptions

Given: The diagonal matrix A, unit starting vector b, and the Stiefel trace objective above.

[F1]

The Stiefel gradient is the tangent projection of the ambient Euclidean gradient (Projected Euclidean gradients are the Stiefel and Grassmann gradients).

[F2]

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

technique · explicit polynomial filtering and exact Rayleigh-quotient arithmetic
1.1F1F2givenalgebra

The ambient gradient of f is −2Ax. At x=e2, the Stiefel projection in [F1] is zero, so the eigendirection is stationary; at the global minimizer e1, f(e1)=−5. For the displayed start, p(A)b=(1,1/4,−1/4)T/3 is nonzero and belongs to K2(A,b)=span⁡{b,Ab}.

2.1givenstep 1.1algebra

The squared norm of this vector is (1+1/16+1/16)/3=3/8, and its A-quadratic form is (5+2/16)/3=41/24. Dividing gives Rayleigh quotient 41/9, hence objective −41/9 and gap 5−41/9=4/9.

3.1F2givenstep 1.1step 2.1algebra∎

Here λ1=5, λ2=2, λ3=0, the top multiplicity is one, and τ=∥(I−P)b∥/∥Pb∥=2. Since step 1.1 places the candidate in K2, its quotient shows θ2≥41/9. For Krylov dimension m=2, the theorem [F2] gives λ1−θ2≤(5−0)(2)2T1(1+2(5−2)/(2−0))2=1042=58. Since θ2≥41/9, its Ritz gap obeys 0≤λ1−θ2≤4/9≤5/8, so the candidate comparison and source bound agree. Also b and Ab are independent because the third coordinate of Ab is zero while that of b is nonzero, so the two-dimensional Krylov space is attained without breakdown.

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