Math 2.0

Calculus of variations and optimal control; optimization Definition

Coercive functional

Builds on Banach spaceFunctional

Definition

Let XX be a real normed space and let J ⁣:X→(−∞,+∞]J \colon X \to (-\infty, +\infty] be a functional. The functional JJ is coercive if J(u)→+∞J(u) \to +\infty as ∥u∥→∞\norm{u} \to \infty. That is, for every M∈ℝM \in \R there is R>0R > 0 such that J(u)>MJ(u) > M for all u∈Xu \in X with ∥u∥>R\norm{u} > R.

Remarks

Equivalently, every sublevel set {u∈X:J(u)≤t}\set{u \in X : J(u) \le t}, t∈ℝt \in \R, is bounded. So a minimising sequence of a coercive functional with inf⁡J<+∞\inf J < +\infty is bounded. In a reflexive Banach space it then has a weakly convergent subsequence.

Example: on a Hilbert space HH with f∈Hf \in H, let J(u)=12∥u∥2−⟨f,u⟩J(u) = \frac{1}{2}\norm{u}^2 - \ip{f}{u}. Then J(u)≥12∥u∥2−∥f∥∥u∥J(u) \ge \frac{1}{2}\norm{u}^2 - \norm{f} \norm{u}, which tends to +∞+\infty, so JJ is coercive.

Non-example: on X=ℝX = \R, let J(u)=euJ(u) = e^{u}. The values stay below 11 for all u<0u < 0, so JJ is not coercive, and it has no minimiser.

Some authors use a stronger, quantitative form: J(u)≥c∥u∥p−CJ(u) \ge c \norm{u}^p - C for constants c>0c > 0, p≥1p \ge 1 and CC. For integral functionals, this follows from a growth bound L(x,u,ξ)≥c∣ξ∣p−CL(x, u, \xi) \ge c \abs{\xi}^p - C on the integrand together with boundary conditions and the Poincaré inequality.

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Further reading

  1. Web https://en.wikipedia.org/wiki/Coercive_function
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MSC49J45, 58E30
DefinesCoercive functional
UsesBanach space, Functional

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