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 and strictly convex functionals on a convex subset of a real vector space
Definition
Let be a real vector space and . The set is convex if for all and . Fix a nonempty convex and an extended-real functional (Proper, coercive and weakly lower semicontinuous extended-real functionals), with the sums and positive-weight products of The extended real line , its order, and the arithmetic that is left undefined. For convex combinations only, additionally define ; this is a local convention, since that product is left undefined in the general extended-real arithmetic. Then is convex if and strictly convex if the inequality is strict whenever , and . A convex functional has convex sublevel sets: for every the set is convex. Only real coefficients are used: on a complex vector space these notions are read on the underlying real structure.
Remarks
-
Sublevel sets. Let be convex and let . If satisfy and , then , and for convexity and the extended-real conventions give ; hence is convex. This is the property used when a sublevel set is intersected with a weakly closed admissible set.
-
Endpoint coefficients. At and the defining inequality reads and , using for the extended value ; the strict form is therefore imposed only for , as stated.
-
Finite competitors. For , if either or is , the right-hand side of the convexity inequality is , so it carries no information at such a pair; the strict form is correspondingly restricted to pairs in the effective domain (Proper, coercive and weakly lower semicontinuous extended-real functionals).
Depends on
Used by
- Strict convexity gives uniqueness of a minimiser Corollary
- A nonconvex gradient energy can lose weak lower semicontinuity Counterexample
- Non-strict convexity allows many minimisers Counterexample
- Stationarity of the Euler-Lagrange equation does not imply a minimum Counterexample
- A convex norm-lower-semicontinuous functional is weakly lower semicontinuous Lemma
- Higher eigenvalues by orthogonality-constrained minimisation Theorem
- Stationarity is sufficient for a global minimum of a convex differentiable functional Theorem
- The direct method for convex integral functionals Theorem
- The Dirichlet principle for the Poisson equation Theorem
- The first Dirichlet eigenfunction by constrained minimisation Theorem
Dependency tree · two levels
14 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
- Gerald Teschl, Partial Differential Equations: From Classical to Modern (2026 author manuscript; complete 392-page archived text) (standard reference, not scraped)
- Francesco Paolo Maiale (course by Giovanni Alberti), Lecture Notes Calculus of Variations A, University of Pisa (last update 21 August 2019; complete 149-page notes) (standard reference, not scraped)