Lagrange multipliers on Banach spaces


In the field of calculus of variations in mathematics, the method of Lagrange multipliers on Banach spaces can be used to solve certain infinite-dimensional constrained optimization problems. The method is a generalization of the classical method of Lagrange multipliers as used to find extrema of a function of finitely many variables.

The Lagrange multiplier theorem for Banach spaces

Let X and Y be real Banach spaces. Let U be an open subset of X and let f : U → R be a continuously differentiable function. Let g : U → Y be another continuously differentiable function, the constraint: the objective is to find the extremal points of f subject to the constraint that g is zero.
Suppose that u0 is a constrained extremum of f, i.e. an extremum of f on
Suppose also that the Fréchet derivative Dg : X → Y of g at u0 is a surjective linear map. Then there exists a Lagrange multiplier λ : Y → R in Y∗, the dual space to Y, such that
Since Df is an element of the dual space X∗, equation can also be written as
where ∗ is the pullback of λ by Dg, i.e. the action of the adjoint map ∗ on λ, as defined by

Connection to the finite-dimensional case

In the case that X and Y are both finite-dimensional then writing out equation in matrix form shows that λ is the usual Lagrange multiplier vector; in the case n = 1, λ is the usual Lagrange multiplier, a real number.

Application

In many optimization problems, one seeks to minimize a functional defined on an infinite-dimensional space such as a Banach space.
Consider, for example, the Sobolev space and the functional given by
Without any constraint, the minimum value of f would be 0, attained by u0 = 0 for all x between −1 and +1. One could also consider the constrained optimization problem, to minimize f among all those u ∈ X such that the mean value of u is +1. In terms of the above theorem, the constraint g would be given by
However this problem can be solved as in the finite dimensional case since the Lagrange multiplier is only a scalar.