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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 , suppose the conditional expectation has the stated finite-valued version, , and is admissible. Then attains the maximum of over all admissible coordinate replacements . In particular, replacing the current factor by 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 and , 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 calls, hence has 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 with finite positive normalizer, an admissible mean-field product, a coordinate , and the normalizer and admissibility conditions in the Statement. Assume AC for the library KL clause.
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.
Variational family and evidence lower bound: admissibility requires -a.e. on and .
Coordinate-ascent variational inference: , , and the update is made only when the normalizer and resulting-admissibility checks pass.
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.
Tonelli's theorem for nonnegative measurable functions on a sigma-finite product: nonnegative product-measurable functions have measurable section integrals and equal iterated integrals.
Integrating against a density agrees with integrating the product: integrating a nonnegative function against a density measure equals integrating its product with against .
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.
Kullback–Leibler divergence: for probability measures , under AC, in this direction.
Kullback–Leibler divergence is nonnegative: for probability measures under AC, .
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.
Fix any admissible replacement and form . Tonelli [F5] gives , and the product rectangle formula [F4] gives is the product probability. Each coordinate density is positive and finite almost surely under its factor law; put and . By [F1], . Fix and put , which is finite almost surely under the product of the other factor laws. Since increases to a full-measure set, choose an integer for which . Product integration from [F5] and [F6] then gives , using on that event. Thus every factor log is integrable; the same argument applies to the current factor and to because their products are admissible.
For each admissible replacement , [F1] makes absolutely integrable. Fubini [F7] applies also to , so and exchanging the and integrals gives . By step 1.1, all factor log terms are integrable, so almost surely and . The final term is constant over coordinate replacements.
Write . If , then almost everywhere on that set. For any admissible , step 2.1 makes , hence almost everywhere there. Thus . On the ratio is measurable, and for every measurable , , so it is the Radon–Nikodym density.
By [A1] and [F8], . All terms are finite by step 1.1 and step 2.1. Also . Subtracting the ELBO expressions in step 2.1 therefore gives by [F9]. Hence is an attained exact coordinate maximizer.
The current factor is itself an admissible replacement. Taking in step 3.2 proves that the update does not decrease ELBO.
By [F3], the procedure has at most sweeps and at most 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 sweeps. Since and are finite, under [A2] it returns after at most calls. The call-count complexity is therefore ; no cost for evaluating an individual call is asserted.
Depends on
- Coordinate-ascent variational inference
- The Axiom of Choice
- Kullback–Leibler divergence
- Variational family and evidence lower bound
- Fubini's theorem for L^1 functions on a sigma-finite product
- Integrating against a density agrees with integrating the product
- Kullback–Leibler divergence is nonnegative
- For sigma-finite factors, the product measure exists, has the rectangle formula, is sigma-finite, and is unique
- Tonelli's theorem for nonnegative measurable functions on a sigma-finite product
Used by
Nothing in the library uses this result yet.
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Sources
- David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe, Variational Inference: A Review for Statisticians (2017) (standard reference, not scraped)
- Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul, An Introduction to Variational Methods for Graphical Models (1999) (standard reference, not scraped)