Alphabeta Math
ExampleConstruction: Literature-sourcedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Constant first-moment weight is outside the proved square-root regret schedule

Example

Let F=[−1,1], x1=0, ℓt(x)=x, gt=1, αt=1/t, β1,t=1/4 for every round, and β2=1/2. The hypotheses of the AMSGrad regret inequality hold, but the second, first-moment correction term in its displayed bound is at least 4T3/2/9 for T≥2. This shows that the bound does not give an O(T) guarantee for this constant schedule; it gives no lower bound on the algorithm's actual regret.

Example references

AMSGrad has an online convex regret bound under decaying first-moment weights: gives the regret inequality and states the geometric and reciprocal first-moment schedules for its O(T) consequence.

Intervals of R: the nine order-convex forms, nondegeneracy, and length: [−1,1] is the closed interval used as the feasible domain.

Uncorrected AMSGrad with a coordinatewise historical maximum: defines the raw moment recurrence used to calculate the correction term.

Reddi, Kale, and Kumar, Theorem 4 and Corollary 1

Facts & Assumptions

Given: The domain, losses, gradients, and parameters in the Example.

[F1]

Uncorrected AMSGrad with a coordinatewise historical maximum: v0=0 and vt=β2vt−1+(1−β2)gt2, with v^t=max⁡{v^t−1,vt}.

[F2]

AMSGrad has an online convex regret bound under decaying first-moment weights: under its stated domain, loss, gradient, and parameter hypotheses, the second correction term is D∞2(1−β1)2∑t=1Tβ1,tv^tαt.

[F3]

Convex and strictly convex functions on Euclidean convex sets and A convex subset of Rm contains every line segment between two of its points: convexity means f((1−θ)x+θy)≤(1−θ)f(x)+θf(y) on a convex set, for θ∈[0,1].

[F6]

Subgradients and the subdifferential of a convex function: g∈∂f(x) when f(y)≥f(x)+g(y−x) for all y in the domain.

[F7]

Every complete ordered field is Archimedean: the positive integers are unbounded, so the growth estimates below are unbounded as T increases.

[F8]

Ordered field and Sign rules for products and monotonicity of multiplication: nonnegative coefficients preserve interval bounds under weighted sums.

[F9]

Intervals of R: the nine order-convex forms, nondegeneracy, and length: [−1,1] is the closed interval with endpoints −1 and 1.

Verification

Given: The one-dimensional online run in the Example.

1.1F1F2F3F4F5F6F8F9giveninductionalgebra

The interval [−1,1] is nonempty, closed, and bounded by [F9], hence compact by [F5]. For x,y∈[−1,1] and θ∈[0,1], [F8] gives −1≤(1−θ)x+θy≤1, so the domain is convex. Also ℓt((1−θ)x+θy)=(1−θ)ℓt(x)+θℓt(y), so each loss is convex by [F3]. Since ∣ℓt(x)−ℓt(y)∣=∣x−y∣, choosing δ=η in [F4] proves continuity for every tolerance η>0. The gradient gt=1 is a subgradient because ℓt(y)=ℓt(x)+(y−x) by [F6], and γ=β1/β2=2/4<1. Thus the hypotheses for [F2] hold. Iterating [F1] gives vt=1−2−t, which is nondecreasing, so v^t=vt≥1/2 for every t≥1.

2.1F2F4step 1.1givenalgebra

Here αt=1/t and β1,t=1/4. The second term in [F2] is therefore at least 4(3/4)2∑t=1T142t=1692∑t=1Tt. For T≥2, at least T/2 indices satisfy t≥T/2, so ∑t=1Tt≥T3/2/(22). The correction term is at least 4T3/2/9.

3.1F2F7step 2.1givenalgebra∎

The constant schedule is neither β1,t=β1λt−1 with λ<1 nor β1,t=β1/t. Since the theorem's upper-bound expression contains this positive term, that expression cannot be O(T) for this schedule: dividing its lower bound by T gives 4T/9, which is unbounded by [F7]. The computation concerns the stated bound only and gives no lower bound on actual regret.

Depends on

Used by

Nothing in the library uses this result yet.

Dependency tree · two levels

63 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources