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Power-rangefinder error transfer under finite expected powered error

Statement

Let F∈{R,C}, let (Ξ,A,P) be a probability space, let A∈Fm×n be fixed, let ℓ≥1 be an integer, and let q≥0 be an integer. Let Ω:Ξ→Fn×ℓ be measurable and let P:Ξ→Fm×m be a measurable orthogonal projector-valued map whose value P(ξ) projects onto Ran⁡((AA∗)qAΩ(ξ)). For Gaussian sketches this is the output range of Reorthogonalized randomized power subspace iteration. Assume E∥(I−P)(AA∗)qA∥2<∞, where expectation is with respect to P. Then E∥(I−P)A∥2≤(E∥(I−P)(AA∗)qA∥2)1/(2q+1). This is a transfer inequality; it assumes the powered error expectation has already been bounded.

Facts & Assumptions

Given: A fixed finite matrix, a random test matrix, an integer q≥0, and a finite expected powered error.

[F1]

The reorthogonalized procedure applies the finite scan to AΩ, then to A∗Qj−1 and AQ~j at each iteration (Reorthogonalized randomized power subspace iteration).

[F2]

For each orthogonal projector P, ∥(I−P)A∥2≤∥(I−P)(AA∗)qA∥21/(2q+1) (Orthogonal-projector power error inequality).

[F3]

Jensen's inequality applies to an integrable real random variable when its convex image is integrable or nonnegative (Jensen's inequality for expectation).

Proof

1.1givenF1algebra

For a Gaussian draw, write S0=Ran⁡(AΩ). At iteration j of [F1], the intermediate range is A∗Sj−1 and the next range is AA∗Sj−1, since replacing a matrix by a basis preserves its range. Induction gives Sq=Ran⁡((AA∗)qAΩ), including empty ranges and q=0. Thus the range specified directly in the statement agrees with the algorithm's output range for Gaussian draws. For a general measurable sketch, the statement specifies P directly and requires no Gaussian sampling step.

2.1step 1.1F2F3algebra∎

Set Z=∥(I−P)(AA∗)qA∥2. By [F2], ∥(I−P)A∥2≤Z1/(2q+1) pointwise. Since Z is integrable and Z1/(2q+1)≤1+Z, Jensen [F3] applied to the convex function −t1/(2q+1) gives EZ1/(2q+1)≤(EZ)1/(2q+1). Taking expectations of the pointwise inequality proves the result.

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