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 , , , , , for every round, and . The hypotheses of the AMSGrad regret inequality hold, but the second, first-moment correction term in its displayed bound is at least for . This shows that the bound does not give an 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 consequence.
Intervals of : the nine order-convex forms, nondegeneracy, and length: 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.
Facts & Assumptions
Given: The domain, losses, gradients, and parameters in the Example.
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
Convex and strictly convex functions on Euclidean convex sets and A convex subset of contains every line segment between two of its points: convexity means on a convex set, for .
Absolute value in an ordered field, Basic properties of the absolute value, Continuity of a map between metric spaces, at a point and globally, in the - form, and The absolute value makes a metric space: is a metric, its open balls are the intervals , and it is unbounded: continuity on is checked with the usual distance and the - criterion.
For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent: a nonempty closed bounded subset of is compact.
Subgradients and the subdifferential of a convex function: when for all in the domain.
Every complete ordered field is Archimedean: the positive integers are unbounded, so the growth estimates below are unbounded as increases.
Ordered field and Sign rules for products and monotonicity of multiplication: nonnegative coefficients preserve interval bounds under weighted sums.
Intervals of : the nine order-convex forms, nondegeneracy, and length: is the closed interval with endpoints and .
Verification
Given: The one-dimensional online run in the Example.
The interval is nonempty, closed, and bounded by [F9], hence compact by [F5]. For and , [F8] gives , so the domain is convex. Also , so each loss is convex by [F3]. Since , choosing in [F4] proves continuity for every tolerance . The gradient is a subgradient because by [F6], and . Thus the hypotheses for [F2] hold. Iterating [F1] gives , which is nondecreasing, so for every .
Here and . The second term in [F2] is therefore at least For , at least indices satisfy , so . The correction term is at least .
The constant schedule is neither with nor . Since the theorem's upper-bound expression contains this positive term, that expression cannot be for this schedule: dividing its lower bound by gives , which is unbounded by [F7]. The computation concerns the stated bound only and gives no lower bound on actual regret.
Depends on
- Uncorrected AMSGrad with a coordinatewise historical maximum
- RMSProp, Adam, and AMSGrad moment updates are well-defined
- AMSGrad has an online convex regret bound under decaying first-moment weights
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- Square roots exist: a unique $\sqrt{a} \ge 0$ with $(\sqrt{a})^2 = a$; the positives are $\{x^2 : x \neq 0\}$
- Ordered field
- Sign rules for products and monotonicity of multiplication
- Convex and strictly convex functions on Euclidean convex sets
- A convex subset of $\mathbb{R}^m$ contains every line segment between two of its points
- Every complete ordered field is Archimedean
- Absolute value in an ordered field
- Basic properties of the absolute value
- Continuity of a map between metric spaces, at a point and globally, in the $\varepsilon$-$\delta$ form
- The absolute value makes $\mathbb{R}$ a metric space: $d(x,y) = |x-y|$ is a metric, its open balls are the intervals $(x-r, x+r)$, and it is unbounded
- For a nonempty subset of $\mathbb{R}^n$ with $n\ge1$, compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent
- Subgradients and the subdifferential of a convex function
- Intervals of $\mathbb{R}$: the nine order-convex forms, nondegeneracy, and length
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
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Sources
- Sashank J. Reddi, Satyen Kale, and Sanjiv Kumar, On the Convergence of Adam and Beyond (standard reference, not scraped)
- Elad Hazan, Introduction to Online Convex Optimization, second edition (standard reference, not scraped)