Euler–Lagrange equation

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In calculus of variations, the Euler–Lagrange equation, Euler's equation,[1] or Lagrange's equation (although the latter name is ambiguous—see disambiguation page), is a second-order partial differential equation whose solutions are the functions for which a given functional is stationary. It was developed by Swiss-Russian mathematician Leonhard Euler and French-Italian mathematician Joseph-Louis Lagrange in the 1750s.

Because a differentiable functional is stationary at its local maxima and minima, the Euler–Lagrange equation is useful for solving optimization problems in which, given some functional, one seeks the function minimizing (or maximizing) it. This is analogous to Fermat's theorem in calculus, stating that at any point where a differentiable function attains a local extremum, its derivative is zero.

In Lagrangian mechanics, because of Hamilton's principle of stationary action, the evolution of a physical system is described by the solutions to the Euler–Lagrange equation for the action of the system. In classical mechanics, it is equivalent to Newton's laws of motion, but it has the advantage that it takes the same form in any system of generalized coordinates, and it is better suited to generalizations. In classical field theory there is an analogous equation to calculate the dynamics of a field.

History[edit]

The Euler–Lagrange equation was developed in the 1750s by Euler and Lagrange in connection with their studies of the tautochrone problem. This is the problem of determining a curve on which a weighted particle will fall to a fixed point in a fixed amount of time, independent of the starting point.

Lagrange solved this problem in 1755 and sent the solution to Euler. Both further developed Lagrange's method and applied it to mechanics, which led to the formulation of Lagrangian mechanics. Their correspondence ultimately led to the calculus of variations, a term coined by Euler himself in 1766.[2]

Statement[edit]

The Euler–Lagrange equation is an equation satisfied by a function, q, of a real argument, t, which is a stationary point of the functional

\displaystyle S(\boldsymbol q) = \int_a^b L(t,\boldsymbol q(t),\boldsymbol q'(t))\, \mathrm{d}t

where:

  • \boldsymbol q is the function to be found:
\begin{align}
\boldsymbol q \colon [a, b] \subset \mathbb{R} & \to     X \\
                                 t & \mapsto x = \boldsymbol q(t)
\end{align}
such that \boldsymbol q is differentiable, \boldsymbol q(a) = \boldsymbol x_a, and \boldsymbol q(b) = \boldsymbol x_{b} ;
  • \boldsymbol q'; is the derivative of \boldsymbol q:
    \begin{align}
q' \colon [a, b] & \to     T_{q(t)}X \\
               t & \mapsto v = q'(t)
\end{align}
TX being the tangent bundle of X defined by
 TX = \bigcup_{x \in X} \{ x \} \times T_{x}X  ;

The Euler–Lagrange equation, then, is given by

L_x(t,q(t),q'(t))-\frac{\mathrm{d}}{\mathrm{d}t}L_v(t,q(t),q'(t)) = 0.

where Lx and Lv denote the partial derivatives of L with respect to the second and third arguments, respectively.

If the dimension of the space X is greater than 1, this is a system of differential equations, one for each component:

\frac{\partial L}{\partial q_i}(t,\boldsymbol q(t),\boldsymbol q'(t))-\frac{\mathrm{d}}{\mathrm{d}t}\frac{\partial L}{\partial \dot q_i}(t,\boldsymbol q(t),\boldsymbol q'(t)) = 0
\quad \text{for } i = 1, \dots, n.

Examples[edit]

A standard example is finding the real-valued function on the interval [a, b], such that f(a) = c and f(b) = d, the length of whose graph is as short as possible. The length of the graph of f is:

 \ell (f) = \int_{a}^{b} \sqrt{1+(f'(x))^2}\,\mathrm{d}x,

the integrand function being L(x, y, y′) = 1 + y′ ² evaluated at (x, y, y′) = (x, f(x), f′(x)).

The partial derivatives of L are:

\frac{\partial L(x, y, y')}{\partial y'} = \frac{y'}{\sqrt{1 + y'^2}} \quad \text{and} \quad
\frac{\partial L(x, y, y')}{\partial y} = 0.

By substituting these into the Euler–Lagrange equation, we obtain

 
\begin{align}
\frac{\mathrm{d}}{\mathrm{d}x} \frac{f'(x)}{\sqrt{1 + (f'(x))^2}} &= 0 \\ 
\frac{f'(x)}{\sqrt{1 + (f'(x))^2}} &= C = \text{constant} \\
\Rightarrow f'(x)&= \frac{C}{\sqrt{1-C^2}} := A \\
\Rightarrow f(x) &= Ax + B
\end{align}

that is, the function must have constant first derivative, and thus its graph is a straight line.

Variations for several functions, several variables, and higher derivatives[edit]

Single function of single variable with higher derivatives[edit]

The stationary values of the functional


   I[f] = \int_{x_0}^{x_1} \mathcal{L}(x, f, f', f'', \dots, f^{(n)})~\mathrm{d}x ~;~~ 
     f' := \cfrac{\mathrm{d}f}{\mathrm{d}x}, ~f'' := \cfrac{\mathrm{d}^2f}{\mathrm{d}x^2}, ~
     f^{(n)} := \cfrac{\mathrm{d}^nf}{\mathrm{d}x^n}

can be obtained from the Euler–Lagrange equation[4]


   \cfrac{\partial \mathcal{L}}{\partial f} - \cfrac{\mathrm{d}}{\mathrm{d} x}\left(\cfrac{\partial \mathcal{L}}{\partial f'}\right) + \cfrac{\mathrm{d}^2}{\mathrm{d} x^2}\left(\cfrac{\partial \mathcal{L}}{\partial f''}\right) - \dots +
  (-1)^n \cfrac{\mathrm{d}^n}{\mathrm{d} x^n}\left(\cfrac{\partial \mathcal{L}}{\partial f^{(n)}}\right)  = 0

under fixed boundary conditions for the function itself as well as for the first n-1 derivatives (i.e. for all f^{(i)}, i \in \{0, ..., n-1\}). The endpoint values of the highest derivative f^{(n)} remain flexible.

Several functions of one variable[edit]

If the problem involves finding several functions (f_1, f_2, \dots, f_n) of a single independent variable (x) that define an extremum of the functional


    I[f_1,f_2, \dots, f_n] = \int_{x_0}^{x_1} \mathcal{L}(x, f_1, f_2, \dots, f_n, f_1', f_2', \dots, f_n')~\mathrm{d}x
    ~;~~ f_i' := \cfrac{\mathrm{d}f_i}{\mathrm{d}x}

then the corresponding Euler–Lagrange equations are[5]


   \begin{align}
     \cfrac{\partial \mathcal{L}}{\partial f_i} - \cfrac{\mathrm{d}}{\mathrm{d}x}\left(\cfrac{\partial \mathcal{L}}{\partial f_i'}\right) = 0 
   \end{align}

Single function of several variables[edit]

A multi-dimensional generalization comes from considering a function on n variables. If Ω is some surface, then

 
   I[f] = \int_{\Omega} \mathcal{L}(x_1, \dots , x_n, f, f_{x_1}, \dots , f_{x_n})\, \mathrm{d}\mathbf{x}\,\! ~;~~
      f_{x_i} := \cfrac{\partial f}{\partial x_i}

is extremized only if f satisfies the partial differential equation

 \frac{\partial \mathcal{L}}{\partial f} - \sum_{i=1}^{n} \frac{\partial}{\partial x_i} \frac{\partial \mathcal{L}}{\partial f_{x_i}} = 0. \,\!

When n = 2 and \mathcal{L} is the energy functional, this leads to the soap-film minimal surface problem.

Several functions of several variables[edit]

If there are several unknown functions to be determined and several variables such that

 
   I[f_1,f_2,\dots,f_m] = \int_{\Omega} \mathcal{L}(x_1, \dots , x_n, f_1, \dots, f_m, f_{1,1}, \dots , f_{1,n},  \dots, f_{m,1}, \dots, f_{m,n}) \, \mathrm{d}\mathbf{x}\,\! ~;~~
      f_{j,i} := \cfrac{\partial f_j}{\partial x_i}

the system of Euler–Lagrange equations is[4]

 
  \begin{align}
    \frac{\partial \mathcal{L}}{\partial f_1} - \sum_{i=1}^{n} \frac{\partial}{\partial x_i} \frac{\partial \mathcal{L}}{\partial f_{1,i}} &= 0 \\
    \frac{\partial \mathcal{L}}{\partial f_2} - \sum_{i=1}^{n} \frac{\partial}{\partial x_i} \frac{\partial \mathcal{L}}{\partial f_{2,i}} &= 0 \\
    \vdots \qquad \vdots \qquad &\quad \vdots  \\
    \frac{\partial \mathcal{L}}{\partial f_j} - \sum_{i=1}^{n} \frac{\partial}{\partial x_i} \frac{\partial \mathcal{L}}{\partial f_{j,i}} &= 0.
  \end{align}

Single function of two variables with higher derivatives[edit]

If there is a single unknown function f to be determined that is dependent on two variables x1 and x2 and if the functional depends on higher derivatives of f up to n-th order such that


   \begin{align}
     I[f] & = \int_{\Omega} \mathcal{L}(x_1, x_2, f, f_{,1}, f_{,2}, f_{,11}, f_{,12}, f_{,22},
                                        \dots, f_{,22\dots 2})\, \mathrm{d}\mathbf{x} \\
     & \qquad \quad
        f_{,i} := \cfrac{\partial f}{\partial x_i} \; , \quad
        f_{,ij} := \cfrac{\partial^2 f}{\partial x_i\partial x_j} \; , \;\; \dots
   \end{align}

then the Euler–Lagrange equation is[4]


  \begin{align}
    \frac{\partial \mathcal{L}}{\partial f}
    & - \frac{\partial}{\partial x_1}\left(\frac{\partial \mathcal{L}}{\partial f_{,1}}\right)
      - \frac{\partial}{\partial x_2}\left(\frac{\partial \mathcal{L}}{\partial f_{,2}}\right) 
      + \frac{\partial^2}{\partial x_1^2}\left(\frac{\partial \mathcal{L}}{\partial f_{,11}}\right)
      + \frac{\partial^2}{\partial x_1\partial x_2}\left(\frac{\partial \mathcal{L}}{\partial f_{,12}}\right)
      + \frac{\partial^2}{\partial x_2^2}\left(\frac{\partial \mathcal{L}}{\partial f_{,22}}\right) \\
    & - \dots
      + (-1)^n \frac{\partial^n}{\partial x_2^n}\left(\frac{\partial \mathcal{L}}{\partial f_{,22\dots 2}}\right) = 0
  \end{align}

which can be represented shortly as:


    \frac{\partial \mathcal{L}}{\partial f} +\sum_{i=1}^n (-1)^i \frac{\partial^i}{\partial x_{\mu_{1}}\dots \partial x_{\mu_{i}}} \left( \frac{\partial \mathcal{L} }{\partial f_{,\mu_1\dots\mu_i}}\right)=0

where \mu_1 \dots \mu_i are indices that span the number of variables, that is they go from 1 to 2. Here summation over the \mu_1 \dots \mu_i indices is implied according to Einstein notation.


Several functions of several variables with higher derivatives[edit]

If there is are p unknown functions fi to be determined that are dependent on m variables x1 ... xm and if the functional depends on higher derivatives of the fi up to n-th order such that


   \begin{align}
     I[f_1,\ldots,f_p] & = \int_{\Omega} \mathcal{L}(x_1, \ldots, x_m; f_1,\ldots,f_p; f_{1,1},\ldots,
     f_{p,m}; f_{1,11},\ldots, f_{p,mm};\ldots; f_{p,m\ldots m})\, \mathrm{d}\mathbf{x} \\
     & \qquad \quad
        f_{i,\mu} := \cfrac{\partial f_i}{\partial x_\mu} \; , \quad
        f_{i,\mu_1\mu_2} := \cfrac{\partial^2 f_i}{\partial x_{\mu_1}\partial x_{\mu_2}} \; , \;\; \dots
   \end{align}

where \mu_1 \dots \mu_j are indices that span the number of variables, that is they go from 1 to m. Then the Euler–Lagrange equation is


    \frac{\partial \mathcal{L}}{\partial f_i} +\sum_{j=1}^n (-1)^j \frac{\partial^j}{\partial x_{\mu_{1}}\dots \partial x_{\mu_{j}}} \left( \frac{\partial \mathcal{L} }{\partial f_{i,\mu_1\dots\mu_j}}\right)=0

where summation over the \mu_1 \dots \mu_j is implied according to Einstein notation. This can be expressed more compactly as


\sum_{j=0}^n (-1)^j \partial_{ \mu_{1}\ldots \mu_{j} }^j \left( \frac{\partial \mathcal{L} }{\partial f_{i,\mu_1\dots\mu_j}}\right)=0

Generalization to Manifolds[edit]

Let M be a smooth manifold, and let C^\infty([a,b]) denote the space of smooth functions f:[a,b]\to M. Then, for functionals S:C^\infty ([a,b])\to \mathbb{R} of the form


S[f]=\int_a^b (L\circ\dot{f})(t)\,\mathrm{d} t

where L:TM\to\mathbb{R} is the Lagrangian, the statement \mathrm{d} S_f=0 is equivalent to the statement that, for all t\in [a,b], each coordinate frame trivialization (x^i,X^i) of a neighborhood of \dot{f}(t) yields the following \dim M equations:


\forall i:\frac{\mathrm{d}}{\mathrm{d}t}\frac{\partial F}{\partial X^i}\bigg|_{\dot{f}(t)}=\frac{\partial F}{\partial x^i}\bigg|_{\dot{f}(t)}

See also[edit]

Notes[edit]

  1. ^ Fox, Charles (1987). An introduction to the calculus of variations. Courier Dover Publications. ISBN 978-0-486-65499-7. 
  2. ^ A short biography of Lagrange
  3. ^ Courant & Hilbert 1953, p. 184
  4. ^ a b c Courant, R; Hilbert, D (1953). Methods of Mathematical Physics. Vol. I (First English ed.). New York: Interscience Publishers, Inc. ISBN 978-0471504474. 
  5. ^ Weinstock, R., 1952, Calculus of Variations With Applications to Physics and Engineering, McGraw-Hill Book Company, New York.

References[edit]