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Exact coordinate updates are monotone and finite sweeps have a call bound

Statement

Assume AC and the hypotheses of Coordinate-ascent variational inference. At a coordinate j, suppose the conditional expectation hj has the stated finite-valued version, 0<cj=∫ehjdμj<∞, and qj∗q−j is admissible. Then qj∗=ehj/cj attains the maximum of ELBO⁡(rjq−j) over all admissible coordinate replacements rj. In particular, replacing the current factor by qj∗ does not decrease the ELBO. This is a single-update claim; it gives no convergence or global-optimum conclusion.

For the finite-sweep procedure with finite inputs m≥1 and N≥0, count one call whenever a coordinate-update block is entered, treating evaluation of its integrals and admissibility decision as part of that call. If each entered call returns either an update or the unavailable status, the procedure halts after at most Nm calls, hence has O(Nm) update-call complexity in this model. This is not a bound on the work required to evaluate an integral or decide admissibility.

Facts & Assumptions

Given: A finite product of sigma-finite reference spaces, a measurable γ≥0 with finite positive normalizer, an admissible mean-field product, a coordinate j, and the normalizer and admissibility conditions in the Statement. Assume AC for the library KL clause.

[A2]

For the call-count claim, each entered coordinate-update block returns either an accepted update or the unavailable status, and one such block is the unit of cost; its internal integral calculations are not separately costed.

[F1]

Variational family and evidence lower bound: admissibility requires q=0 μ-a.e. on {π=0} and ∫q(∣log⁡γ∣+∣log⁡q∣)dμ<∞.

[F2]

Variational family and evidence lower bound: ELBO⁡(q)=∫q(log⁡γ−log⁡q)dμ.

[F3]

Coordinate-ascent variational inference: hj=∫q−jlog⁡γ dμ−j, qj∗=ehj/cj, and the update is made only when the normalizer and resulting-admissibility checks pass.

[F4]

For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique: the finite product reference measure exists, has the rectangle formula, and is unique.

[F5]

Tonelli's theorem for nonnegative measurable functions on a sigma-finite product: nonnegative product-measurable functions have measurable section integrals and equal iterated integrals.

[F6]

Integrating against a density agrees with integrating the product: integrating a nonnegative function against a density measure f dμ equals integrating its product with f against μ.

[F7]

Fubini's theorem for L^1 functions on a sigma-finite product: for an absolutely integrable function on the sigma-finite product, almost-everywhere section integrals exist and the iterated integrals agree.

[F8]

Kullback–Leibler divergence: for probability measures P≪R, under AC, D(P∥R)=∫log⁡(dP/dR)dP in this direction.

[F9]

Kullback–Leibler divergence is nonnegative: for probability measures P,R under AC, D(P∥R)≥0.

[A1]

The Axiom of Choice: every family of nonempty sets has a choice function. AC is used only through [F8] and [F9].

Proof

Given: The hypotheses in the Statement.

1.1F1F4F5F6chooseconstructalgebra

Fix any admissible replacement rj and form u=rjq−j. Tonelli [F5] gives ∫u dμ=1, and the product rectangle formula [F4] gives Pu:=uμ=⨂k=1m(ukμk) is the product probability. Each coordinate density uk is positive and finite almost surely under its factor law; put Xk=log⁡uk(Zk) and S=log⁡u(Z)=∑k=1mXk. By [F1], Eu∣S∣=∫u∣log⁡u∣dμ<∞. Fix j and put R=∑k≠jXk, which is finite almost surely under the product of the other factor laws. Since {∣R∣≤n} increases to a full-measure set, choose an integer K for which p:=Pu(∣R∣≤K)>0. Product integration from [F5] and [F6] then gives p∫uj∣log⁡uj∣dμj=∫{∣R∣≤K}∣Xj∣ dPu≤∫∣S∣dPu+Kp<∞, using ∣Xj∣≤∣S∣+∣R∣ on that event. Thus every factor log is integrable; the same argument applies to the current factor and to qj∗ because their products are admissible.

2.1F1F2F3F7step 1.1algebra

For each admissible replacement uj, [F1] makes ulog⁡γ absolutely integrable. Fubini [F7] applies also to u∣log⁡γ∣, so ∫uj∣hj∣dμj<∞ and exchanging the j and −j integrals gives ∫ulog⁡γdμ=∫ujhjdμj. By step 1.1, all factor log terms are integrable, so log⁡u=log⁡uj+log⁡q−j almost surely and ELBO⁡(ujq−j)=∫ujhjdμj−∫ujlog⁡ujdμj−∫q−jlog⁡q−jdμ−j. The final term is constant over coordinate replacements.

3.1F1F3step 2.1algebra

Write qj∗=ehj/cj. If qj∗=0, then hj=−∞ almost everywhere on that set. For any admissible rj, step 2.1 makes ∫rj∣hj∣dμj<∞, hence rj=0 almost everywhere there. Thus rjμj≪qj∗μj. On {qj∗>0} the ratio rj/qj∗ is measurable, and for every measurable A, ∫A(rj/qj∗)d(qj∗μj)=∫A∩{qj∗>0}rjdμj=∫Arjdμj, so it is the Radon–Nikodym density.

3.2A1F8F9step 1.1step 2.1algebra

By [A1] and [F8], D(rjμj∥qj∗μj)=∫rjlog⁡(rj/qj∗)dμj=∫rjlog⁡rjdμj−∫rjhjdμj+log⁡cj. All terms are finite by step 1.1 and step 2.1. Also ∫qj∗hj−∫qj∗log⁡qj∗=log⁡cj. Subtracting the ELBO expressions in step 2.1 therefore gives ELBO⁡(qj∗q−j)−ELBO⁡(rjq−j)=D(rjμj∥qj∗μj)≥0 by [F9]. Hence qj∗ is an attained exact coordinate maximizer.

4.1step 3.2given

The current factor qj is itself an admissible replacement. Taking rj=qj in step 3.2 proves that the update does not decrease ELBO.

5.1F3A2algebra∎

By [F3], the procedure has at most N sweeps and at most m coordinate-update blocks in each sweep. An unavailable result returns immediately, so every run is either a prefix ending at that branch or a run through all N sweeps. Since N and m are finite, under [A2] it returns after at most Nm calls. The call-count complexity is therefore O(Nm); no cost for evaluating an individual call is asserted.

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