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.
Weighted cutoff maximum gives a uniform normal angle
Statement
Let be a smooth manifold with nonempty compact boundary , and let be a supplied smooth boundary collar: is a smooth embedding, , and its image is an open neighborhood of in . Choose , put , and supply a smooth real function on , smooth up to its boundary. The compact smooth graph collar in this lemma is , identified with by graph projection. Identify its inner boundary, the graph over , with ; its artificial outer edge is the graph over . Let be a smooth Riemannian metric on , smooth up to the boundary. Use its gradient , squared norm and positive-divergence Laplace–Beltrami operator . Suppose is smooth up to the boundary, , and on . Let be continuous on and smooth on , with the following assumed cutoff properties: , on , on the outer edge, and on the bounds and . Here , and are fixed constants; one common may be obtained by enlarging the constants in the two differential estimates. Then some integer , determined only by these constants, satisfies . No uniform candidate second-derivative estimate is assumed.
Statement references
Facts & Assumptions
The cutoff value, regularity, gradient and Laplacian properties in the Statement are assumptions of this lemma. For geometric applications, Projection estimates for a Gaussian collar cutoff supplies these properties under its own hypotheses: a smooth Gaussian tube with compact closure, smooth reference-boundary distance, a smooth spacelike maximal graph in the prescribed cone, the stated tube shrinking and cutoff-width restrictions.
The logarithmic angle inequality in the Statement is an assumption of this lemma. For geometric applications, Logarithmic angle inequality through the full range v at least one supplies it for a smooth spacelike maximal hypersurface and a smooth future unit timelike field with the stated uniform observer-metric bounds on its first two covariant derivatives and the ambient Ricci tensor. Compactness here is only that of the chosen closed graph collar with its fixed outer edge.
The metric is positive definite, including at a smooth boundary, as in Riemannian metric and riemannian manifold. The gradient is characterized by as in The Riemannian gradient is the metric dual of the differential, and is the metric trace of the Levi–Civita Hessian with the positive-divergence sign convention in Laplace–Beltrami operator as the trace of the Hessian.
The Hessian formulas and are Gradient hessian and divergence connection formulas.
A continuous real-valued function on a nonempty compact space attains its maximum, by claim 2 of A continuous image of a compact space is compact; a continuous real-valued map on a nonempty compact space attains a maximum and a minimum; and a continuous bijection from a compact space to a Hausdorff space is a homeomorphism.
At an interior local maximum of a function on a Euclidean open set, its first derivatives vanish and its Euclidean Hessian is negative semidefinite. The Hessian is negative semidefinite at an interior local maximum The conversion to the metric trace on the collar is given below.
Proof
Given: The compact collar, Riemannian metric, regularity and inequalities in the Statement. Goal: Bound the angle on the positive inner part without a uniform candidate second-derivative estimate.
For any integer , is continuous on the nonempty compact collar, so [F5] gives a point where it attains its maximum. On the outer edge , and on it is at most . If its maximum is zero the desired bound is immediate. Otherwise, if is interior, and is smooth in a neighborhood of and has a local maximum there. Thus no differentiation across is needed.
At that interior positive maximum choose a smooth interior coordinate chart. Apply [F6] to the coordinate representative of to get and a negative semidefinite matrix . By [F4], the Christoffel term vanishes at , so the intrinsic Hessian has that same quadratic form. Its value on each -unit basis vector is nonpositive; tracing as in [F3] gives . Also [F3] gives , hence at .
For a positive smooth scalar , [F3] and [F4] give and by differentiating twice. Taking the metric trace gives . Apply this identity to near , and insert the assumed logarithmic angle inequality and . With step 2.1 this yields at .
Put , which is real by and . If an interior positive maximum has , then . Since , the coefficient is positive. Multiplying step 3.1 by and using and gives . Choose an integer . Then and , a contradiction. This choice depends only on the stated constants.
For that fixed , a positive maximum therefore either lies on , where , or is interior with , where since . The zero-maximum case also obeys . On , division by now gives the stated bound. Only individual smoothness at a positive maximum was used, so no uniform candidate second-derivative estimate was assumed.
Depends on
- Smooth collars of a manifold boundary
- Projection estimates for a Gaussian collar cutoff
- Logarithmic angle inequality through the full range v at least one
- Riemannian metric and riemannian manifold
- The Riemannian gradient is the metric dual of the differential
- Laplace–Beltrami operator as the trace of the Hessian
- Gradient hessian and divergence connection formulas
- A continuous image of a compact space is compact; a continuous real-valued map on a nonempty compact space attains a maximum and a minimum; and a continuous bijection from a compact space to a Hausdorff space is a homeomorphism
- The Hessian is negative semidefinite at an interior local maximum
Dependency tree · 2 levels
Each result is drawn at its shortest distance from this item. Arrows run from prerequisites to the items that use them; this item has a heavier outline. External source nodes link directly to the cited text. The view shows up to two dependency steps; open a prerequisite to continue.