Convex function
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A function on a convex domain is convex when its value at every two-point convex combination is at most the same weighted combination of endpoint values.
mko-convex-function · definition · v0.10.0
Mathematical statement
On a convex domain C, f is convex if f(lambda*x+(1−lambda)*y)≤lambda*f(x)+(1−lambda)*f(y) for every x,y in C and every lambda in [0,1]. This slice uses complete algebraic certificates for intervals and univariate affine or quadratic functions.
Representative formula (exact TeX)
J(f)
Assumptions
- The domain is an explicitly declared nonempty real interval; quadratic coefficients and Jensen instances are exact.
- One Jensen violation suffices to refute convexity, but one passing instance cannot prove it. Positive certificates must cover every x, y and lambda.
Symbol scope
J— Jensen-convexity recordf— real-valued function
Intuition, definition, and misconceptions
An affine function gives equality for every Jensen combination.
The Jensen gap for a quadratic simplifies to a*lambda*(1−lambda)*(x−y)².
Finite instances supply counterexamples or illustrations, not replacements for universal certificates.
Do not use the usual convex-function qualification without a convex domain or infer global convexity from a few passing Jensen samples.
Adopted derivation guide
Convex function: a complete certificate via the Jensen gap of a quadratic, and a single Jensen counterexampleThe original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.
Prerequisites
- Convex set — A convex-function claim first requires a convex domain.
- Function mapping — The function must be total on its domain.
- Usual order on the real numbers — Jensen conditions compare values by inequalities.
Verification boundary
Only affine/quadratic algebraic certificates on one-variable intervals are checked.
not_formalized
This does not mean the content is false; it means no replayable computational or formal evidence is registered yet.
Next obligation
Add strict convexity, subgradients and higher-dimensional convex functions.
Same-source structured data
This page and the interactive view use the same object and existing language edition; this adds no proof or human review.
Canonical MKO JSON · Plain Markdown