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.
Convex, strictly convex, concave, strictly concave, and midpoint-convex real functions on an interval
Definition
Let be an interval (Intervals of : the nine order-convex forms, nondegeneracy, and length) and let . For and , the point belongs to . The function is convex when the convex-combination inequality holds for every weight in :
It is strictly convex when this inequality is strict whenever and . It is concave (respectively strictly concave) when is convex (respectively strictly convex). It is midpoint convex when the displayed inequality is required only at .
Remarks
The endpoint weights and impose equalities, so strict convexity does not ask for strictness there. A singleton interval satisfies the convexity and strict-convexity conditions vacuously.
Depends on
Used by
- A strictly convex function has at most one global minimizer Corollary
- Every local minimum of a convex function on an interval is a global minimum Corollary
- Assuming Choice, a Hamel coefficient map is midpoint convex but discontinuous and therefore not convex Counterexample
- An inflection point as a point of continuity where convexity changes to concavity or conversely Definition
- Log-convex positive functions Definition
- A positive convex function need not be log-convex Example
- Finite Jensen for x↦ x² gives that the square of a weighted mean is at most the weighted mean of the squares Example
- The absolute-value function is convex Example
- Absolute real powers are Borel measurable and convex Lemma
- For a convex function and x<y<z, the three secant slopes satisfy s(x,y)≤ s(x,z)≤ s(y,z) Lemma
- Midpoint convexity gives the convexity inequality at every dyadic weight k/2ⁿ Lemma
- Finite Jensen inequality for a convex function and nonnegative weights summing to one Theorem
Dependency tree · two levels
5 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
- R. Gardner, Convex Functions, Notes 6.6 (standard reference, not scraped)