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.
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- 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.
No-failure MMW rounds yield an approximate dual-feasible vector
Statement
Let and consider a primal-dual pair with , , , and a dual optimum attained by some vector of norm at most . Set . Suppose for the MMW update uses losses where , , , and supplied estimates satisfy , , and . If and , then is dual feasible and .
Facts & Assumptions
Given: The normalized finite SDP, each round's multiplier vector and trace estimates, and the MMW regret bound.
Finite semidefinite primal-dual pair: dual feasibility requires nonnegative coordinates and positive semidefinite slack .
Matrix multiplicative-weights game and update: the round density matrix is and the round loss is .
Proof
By and , the triangle inequality gives , so the regret bound applies. The trace-estimate assumptions and give Together with the shifted-oracle inequality, this implies .
Sum the lower bounds from step 1.1 and combine them with [F3]. Writing , we get , hence .
The dual slack of is , because the identity coefficient contributes . Step 2.1 and the lower bound on make this matrix positive semidefinite. Also and . These are exactly the dual feasibility and objective claims in [F1].
Depends on
Used by
Dependency tree · two levels
30 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
- Joran van Apeldoorn et al., Quantum SDP-solvers: Better upper and lower bounds (standard reference, not scraped)