Math 2.0

Calculus of variations and optimal control; optimization Definition

Euler–Lagrange equation

Builds on Calculus of variationsPartial derivativeDivergence

Definition

Let Ω⊆ℝn\Omega \subseteq \R^n be an open set and let L ⁣:Ω×ℝ×ℝn→ℝL \colon \Omega \times \R \times \R^n \to \R, written L(x,z,p)L(x, z, p), be a function of class C2C^2. Write Lz=∂L/∂zL_z = \partial L / \partial z and Lpi=∂L/∂piL_{p_i} = \partial L / \partial p_i for its partial derivatives. The Euler–Lagrange equation of the functional J(u)=∫ΩL(x,u,∇u) dxJ(u) = \int_\Omega L(x, u, \nabla u) \, dx of the calculus of variations is the second-order equation −∑i=1n∂∂xi(Lpi(x,u(x),∇u(x)))+Lz(x,u(x),∇u(x))=0in Ω-\sum_{i=1}^n \frac{\partial}{\partial x_i} \Big( L_{p_i}\big(x, u(x), \nabla u(x)\big) \Big) + L_z\big(x, u(x), \nabla u(x)\big) = 0 \quad \text{in } \Omega for an unknown u∈C2(Ω)u \in C^2(\Omega). In short form it reads −div⁡Lp(x,u,∇u)+Lz(x,u,∇u)=0-\operatorname{div} L_p(x, u, \nabla u) + L_z(x, u, \nabla u) = 0, with the divergence taken in xx.

Remarks

If Ω\Omega is bounded and u∈C2(Ω‾)u \in C^2(\overline{\Omega}) minimises JJ among C2(Ω‾)C^2(\overline{\Omega}) functions with the same boundary values, then uu solves the Euler–Lagrange equation. Indeed ddtJ(u+tφ)∣t=0=0\frac{d}{dt} J(u + t\varphi)\big|_{t=0} = 0 for every φ∈Cc∞(Ω)\varphi \in C^\infty_c(\Omega); integrating by parts gives ∫Ω(−div⁡Lp+Lz)φ dx=0\int_\Omega (-\operatorname{div} L_p + L_z) \varphi \, dx = 0 for all such φ\varphi, so the integrand vanishes. The weak form ∫Ω(Lp(x,u,∇u)⋅∇φ+Lz(x,u,∇u)φ) dx=0\int_\Omega \big( L_p(x, u, \nabla u) \cdot \nabla \varphi + L_z(x, u, \nabla u) \varphi \big) \, dx = 0 for all φ∈Cc∞(Ω)\varphi \in C^\infty_c(\Omega) makes sense for less regular uu.

Example: for L(x,z,p)=12∣p∣2−f(x)zL(x, z, p) = \frac{1}{2}\abs{p}^2 - f(x) z one has Lp=pL_p = p and Lz=−fL_z = -f, so the Euler–Lagrange equation is Poisson's equation −Δu=f-\Delta u = f.

Example: for n=1n = 1 and L(x,z,p)=1+p2L(x, z, p) = \sqrt{1 + p^2}, one gets ddx(u′/1+u′2)=0\frac{d}{dx}\big(u' / \sqrt{1 + u'^2}\big) = 0, so u′u' is constant and the graph is a straight line.

Non-example: a solution of the Euler–Lagrange equation need not be a minimiser. For n=1n = 1, Ω=(0,2π)\Omega = (0, 2\pi) and L=12(p2−z2)L = \frac{1}{2}(p^2 - z^2), the equation is u′′+u=0u'' + u = 0, solved by u=0u = 0; but J(tsin⁡(x/2))=t22∫02π(14cos⁡2(x/2)−sin⁡2(x/2)) dx=−3πt28<0=J(0)J(t \sin(x/2)) = \frac{t^2}{2} \int_0^{2\pi} \big(\frac{1}{4}\cos^2(x/2) - \sin^2(x/2)\big) \, dx = -\frac{3\pi t^2}{8} < 0 = J(0) for t≠0t \ne 0, and tsin⁡(x/2)t\sin(x/2) has the same boundary values 00.

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  1. Calculus of variations concept
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  3. Function concept
  4. Fréchet derivative definition
  5. Function of several variables concept
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  7. Limit of a function of several variables concept
  8. Partial derivative concept
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  10. Differentiable function of several variables definition
  11. Partial derivative definition
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  13. Jacobian matrix definition
  14. Higher partial derivatives definition
  15. Iterated derivative definition
  16. C^k function characterised by continuous derivatives statement
  17. Vector field definition
  18. Divergence definition
  19. Euler–Lagrange equation definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Euler%E2%80%93Lagrange_equation
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