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Finiteness, superlinearity of the Lagrangian and localisation of Hopf--Lax near-minimisers
Statement
Let be convex and superlinear with Legendre transform , and let be bounded and continuous. Then: (1) is real-valued on , convex, continuous, and superlinear; (2) for every and the infimum defining is attained, and for every there is a finite radius such that every near-minimiser with satisfies ; (3) consequently is real-valued for every and every . No choice principle is used.
Facts & Assumptions
Given: A convex superlinear with , its Legendre transform , a bounded continuous , and the operators of The Hopf--Lax operator and the Hopf--Lax formula.
For , , and (The Hopf--Lax operator and the Hopf--Lax formula).
for every , the supremum being the least upper bound in of the set of real numbers (The Legendre transform of a finite-valued convex Hamiltonian).
Every convex function on an open convex set is continuous on it; in particular any finite convex function on is continuous (A convex function on an open convex set is continuous).
A nonempty subset of is compact exactly when it is closed and bounded, and every continuous real-valued function on such a set attains a maximum and a minimum (For a nonempty subset of with , compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent).
Proof
Properties of . Fix . Superlinearity of gives with for , so there; on the closed ball , which is closed and bounded and hence compact by [F4], the continuous function (continuity of is [F3]) attains a maximum by [F4]. Hence for every , while because is admissible; by the least-upper-bound property of [F2], is a real number. Convexity of : for fixed the map is affine, and a pointwise supremum of affine functions is convex; is real-valued, so it is finite and convex on the open convex set and therefore continuous by [F3]. Superlinearity: fix ; for the admissible test point gives , where the maximum is finite by [F3] and [F4]; dividing by and letting gives , and since was arbitrary, .
Attainment and localisation. Fix , and , and put , so that by [F1]. The competitor gives , where . Also every term is at least by [F2], so . Let satisfy ; then Superlinearity of from step 1.1 gives such that whenever . If , then , contradicting the preceding bound. Hence , so every -near-minimiser lies in the closed ball with ; the radius depends only on and the fixed (and may harmlessly be viewed as a function of as in the statement). For attainment, let . By the definition of the finite infimum, is nonempty. It is closed by continuity of and bounded by the localisation just proved with , so [F4] makes it compact. The continuous function attains a minimum on at some . This minimum equals the global infimum: it is at least , and for every the infimum property gives with , which lies in , so the minimum is at most . Thus and the infimum is attained, without selecting a sequence.
Real-valuedness of . For each term satisfies by the estimate of step 1.1, and the value at is finite; hence by [F1]. For this is , real-valued by hypothesis.
Conclusion. Part (1) is step 1.1, part (2) is step 2.1, and part (3) is step 3.1.
Remarks
- Dependence of the radius. The radius produced depends on , , and only through the quantifier-free bounds of step 2.1; it is uniform in on compact -sets because the estimates are translation invariant. No compactness of the ambient space and no subsequence selection is used, hence no choice principle.
Depends on
- The Hopf--Lax operator and the Hopf--Lax formula
- The Legendre transform of a finite-valued convex Hamiltonian
- A convex function on an open convex set is continuous
- For a nonempty subset of $\mathbb{R}^n$ with $n\ge1$, compactness, closedness and boundedness, pseudocompactness, and attainment of extrema by every continuous real-valued function are equivalent
- For $n\ge1$, every Euclidean closed ball and every Euclidean sphere of positive radius is compact
- Uniform continuity of a map of metric spaces: one $\delta$ serving every point
- Lower bound, bounded below, bounded set
Used by
- The Hopf--Lax operator preserves a modulus of continuity Corollary
- Nonconvexity can break the equation; nonsuperlinearity can limit the Lagrangian domain Counterexample
- A Hopf--Lax solution with a forming corner from smooth data Example
- The quadratic Hopf--Lax formula as an infimal convolution Example
- Vanishing viscosity selects the Hopf--Lax solution for bounded data Example
- A Hopf--Lax minimiser satisfies the characteristic Euler relation at differentiability points Lemma
- The Hamilton--Jacobi correspondence in one dimension Theorem
- The Hopf--Lax formula solves the Hamilton--Jacobi Cauchy problem Theorem
- The Hopf--Lax operators form a semigroup (dynamic programming) Theorem
Dependency tree · two levels
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