Page 142 - INTRODUCTION TO THE CALCULUS OF VARIATIONS
P. 142

Introduction                                                      129

                More generally if n ≥ 2,wedefine (cf. Theorem 4.4.10 in Morrey [75])

                                    ⎡                               2  ⎤ 1/2
                                     n+1  Ã ¡  1   i−1  i+1    n+1  ¢ !
                                     X    ∂ v , ..., v  ,v  , ..., v
                           g (∇v)=  ⎣                                ⎦
                                                 ∂ (x 1 , ..., x n )
                                     i=1
                       ¡        ¢
                         1
                where ∂ u , ..., u n  /∂ (x 1 , ..., x n ) stands for the determinant of the n × n ma-
                    ¡       ¢
                       i
                trix ∂u /∂x j      . In the terminology of Section 3.5 such a function g is
                             1≤i,j≤n
                polyconvex but not convex. The area for such a surface is therefore given by
                                                   Z
                                  Area (Σ)= J (v)=    g (∇v (x)) dx .
                                                    Ω
                   The problem is then, given Γ,to find a parametric surface that minimizes
                                     (Q)  inf {Area (Σ): ∂Σ = Γ} .
                   It is clear that problem (Q) is more general than (P). It is however a more
                complicated problem than (P) for several reasons besides the geometrical ones.
                Contrary to (P) it is a vectorial problem of the calculus of variations and the
                Euler-Lagrange equations associated to (Q) form now a system of (n +1) partial
                differential equations. Moreover, although, as for (P), any minimizer is a solution
                of these equations, it is not true in general, contrary to what happens with (P),
                that every solution of the Euler-Lagrange equations is necessarily a minimizer
                of (Q). Finally uniqueness is also lost for (Q) in contrast with what happens for
                (P).
                   We now come to the definition of minimal surfaces. A minimal surface will
                be a solution of the Euler-Lagrange equations associated to (Q), it will turn
                out that it has (see Section 5.2) zero mean curvature. We should draw the
                attention to the misleading terminology (this confusion is not present in the
                case of nonparametric surfaces): a minimal surface is not necessarily a surface
                of minimal area, while the converse is true, namely, a surface of minimal area is
                a minimal surface.
                   The problem of finding a minimal surface with prescribed boundary is known
                as Plateau problem.
                   We now describe the content of the present chapter. In most part we will
                only consider the case n =2. In Section 5.2 we will recall some basic facts
                about surfaces, mean curvature and isothermal coordinates. We will then give
                several examples of minimal surfaces. In Section 5.3 we will outline some of the
                main ideas of the method of Douglas, as revised by Courant and Tonelli, for
                solving Plateau problem. This method is valid only when n =2, since it uses
                strongly the notion and properties of conformal mappings. In Section 5.4 we
                briefly, and without proofs, mention some results of regularity, uniqueness and
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