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 (maxima or minima) 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
Since Df(u0) is an element of the dual space X∗, equation (L) can also be written as
Connection to the finite-dimensional case
In the case that X and Y are both finite-dimensional (i.e. linearly isomorphic to Rm and Rn for some natural numbers m and n) then writing out equation (L) 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.
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 X = H01([−1, +1]; R) and the functional f : X → R given by
Without any constraint, the minimum value of f would be 0, attained by u0(x) = 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.
- Pontryagin's minimum principle, Hamiltonian method in calculus of variations
- Zeidler, Eberhard (1995). Applied functional analysis: Variational Methods and Optimization. Applied Mathematical Sciences 109. New York, NY: Springer-Verlag. ISBN 978-1-4612-9529-7.