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.
Midpoint convexity gives the convexity inequality at every dyadic weight
Statement
Let be midpoint convex. For every , every , and all ,
Facts & Assumptions
Given: A midpoint-convex and .
Midpoint convexity is the convexity inequality at weight (Convex, strictly convex, concave, strictly concave, and midpoint-convex real functions on an interval).
Proof
For , the only weights are and , and the asserted inequalities are equalities.
Assume the assertion at . At , an even numerator reduces to the induction hypothesis; an odd numerator is the midpoint of the adjacent weights and , so [L1] followed by the induction hypothesis proves the assertion.
The base and successor steps establish the assertion for every natural and every .
Depends on
Used by
Dependency tree · next 3 levels
Direct dependencies and their dependencies through the next three levels: 43 results over 15 levels. An arrow runs from a result to what uses it, and this result sits at the bottom with a heavier outline. Click the chart to enlarge it.
Sources
- R. Gardner, Convex Functions, Notes 6.6 (standard reference, not scraped)