Convex set
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A set is convex when the entire segment formed by every pair of its points and every lambda in [0,1] remains in the set.
mko-convex-set · definition · v0.10.0
Mathematical statement
A subset C of a real vector space is convex if lambda*x+(1−lambda)*y belongs to C for every x,y in C and every lambda in [0,1]. Positive cases here use complete algebraic certificates for intervals, boxes and halfspaces; a single exact counterexample can refute convexity.
Representative formula (exact TeX)
K(C)
Assumptions
- Only nonempty closed intervals, nonempty axis-aligned boxes, closed halfspaces and explicit finite-point-set counterexamples are handled.
- Sampling finitely many lambda values cannot prove convexity. Failure of a proposed finite-point counterexample does not conversely establish convexity.
Symbol scope
K— convexity certificate or counterexampleC— real-vector-space subset
Intuition, definition, and misconceptions
Coordinatewise upper- and lower-bound certificates cover every lambda for intervals and boxes.
A linear inequality defining a halfspace is preserved by convex combinations.
If a set contains both endpoints but not their midpoint, that single midpoint witness suffices to refute convexity.
Do not assert convexity after a few lambda checks or merely because one counterexample search failed.
Adopted derivation guide
Convex set: complete convexity certificates for a halfspace and an axis-aligned box, and a midpoint counterexample for a finite point setThe original MKO is retained. The guide has its own append-only revision and verification boundaries, not a new Lean proof.
Prerequisites
- Finite convex combination — Two-point convex combinations describe segments.
- Subset — A convex set is a subset of a real vector space.
- Real vector space — Segment operations use vector addition and scalar multiplication.
Verification boundary
Positive cases require universal algebraic certificates; a failed counterexample is not a positive proof.
not_formalized
This does not mean the content is false; it means no replayable computational or formal evidence is registered yet.
Next obligation
Add convex functions, convex hulls and convex optimization.
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