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.
The heat flow need not converge in supremum norm
Statement refuted
Assuming Countable Choice, the claim that the endpoint can be added to the convergence theorem for the heat flow, that is: for every one has as . This fails even for the simplest jump data, and locally uniform convergence on compact sets containing the jump fails as well. The same witnesses also obstruct convergence in the essential supremum norm: for one has and ; for , which belongs to every finite and to , one has and for every . Thus the continuity hypothesis cannot be discarded; actual supremum convergence for bounded real data requires uniform continuity, and essential supremum convergence requires a uniformly continuous representative.
Facts & Assumptions
Given: Countable Choice, , , , the half-line datum and the compactly supported datum .
Countable Choice is the hypothesis carried by the evolution suppliers below (The Axiom of Countable Choice ()).
For the heat kernel is , even in , and satisfies (The heat kernel on and its causal extension, Normalisation, parabolic scaling, heat equation and derivative bounds for the heat kernel).
The indicator of a measurable set is measurable (An indicator function is measurable exactly when its set is measurable), so and .
For bounded measurable data the heat evolution is the everywhere-defined bounded representative (The heat evolution of initial data).
For and , as (The heat Cauchy problem for data), and for bounded uniformly continuous data the convergence is locally uniform (The heat Cauchy problem for bounded uniformly continuous data, approximate identities converge uniformly on compacta for bounded continuous functions).
For bounded real data , is smooth and its first spatial derivative satisfies (Spatial derivative estimates for the heat flow, with , ). A bounded continuous derivative obeys this essential bound pointwise: a violation would persist on an interval of positive measure. The mean value theorem then bounds increments by the derivative bound (The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with ). Uniform limits of uniformly continuous real functions are uniformly continuous (The uniform limit of uniformly continuous real-valued functions is uniformly continuous); every real Cauchy sequence converges (The reals are complete).
Counterexample
The half-line datum is bounded and measurable, with , but for every finite . Evenness and unit mass give , so its pointwise supremum distance is at least . This also gives an essential supremum bound: for any , continuity of at supplies such that for . On that interval , so . As the interval has positive measure and is arbitrary, for every .
Uniform continuity is necessary for an actual supremum-norm convergence claim on bounded real data: for each fixed , [F5] and the mean value theorem make globally Lipschitz, hence uniformly continuous. If , the sequence converges uniformly to , which is uniformly continuous by [F5]. For convergence in the essential supremum norm the corresponding necessity concerns the class: the continuous differences have equal supremum and essential supremum, so the sequence is uniformly Cauchy; real completeness gives a pointwise limit . For any , a uniform Cauchy bound for all and sufficiently large , followed by , gives . Thus the limit is uniform and is uniformly continuous by [F5]. Finally . Thus that class has a uniformly continuous representative.
The interval datum has for every finite and . By positivity and step 1.1, , since the omitted integral on is positive. Put and , so . Continuity at supplies with for . There and . Hence on a set of positive measure, and the pointwise supremum is also greater than .
For this same , [F4] gives for every . Nevertheless each compact set containing has pointwise supremum error at least , so locally uniform convergence fails there. The continuity hypothesis in the bounded-data theorem cannot be discarded.
Steps 1.1–3.1 preserve the exact half-line value and supply compactly supported data in with essential and pointwise supremum errors bounded away from zero, while every finite- norm converges. Step 1.2 justifies the uniform-continuity qualification with the distinction between representatives and classes explicit. These witnesses refute the claimed endpoint extension and locally uniform convergence at the jump.
Depends on
- $L^1$ approximate identities converge uniformly on compacta for bounded continuous functions
- The uniform limit of uniformly continuous real-valued functions is uniformly continuous
- The mean value theorem, as the case $g(x) = x$ of Cauchy's: for $f$ continuous on $[a,b]$ with $a < b$ and differentiable on $(a,b)$ there is $c \in (a,b)$ with $f(b) - f(a) = f'(c)(b-a)$
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- The heat evolution $H_t$ of initial data
- The heat kernel on $\mathbb{R}^n$ and its causal extension
- Normalisation, parabolic scaling, heat equation and derivative bounds for the heat kernel
- An indicator function is measurable exactly when its set is measurable
- The heat Cauchy problem for bounded uniformly continuous data
- The heat Cauchy problem for $L^p$ data
- The reals are complete
- Spatial derivative estimates for the heat flow
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
72 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
- John K. Hunter, Notes on Partial Differential Equations (revised 18 June 2014, UC Davis) (standard reference, not scraped)
- Gerald Teschl, Partial Differential Equations: From Classical to Modern (2025 archived author manuscript, AMS Graduate Studies in Mathematics) (standard reference, not scraped)
- Jared Speck, MIT 18.152 Introduction to Partial Differential Equations, Class Meeting #5: The Fundamental Solution for the Heat Equation (Fall 2011) (standard reference, not scraped)