Lagrange Multiplier

Fig. 1. Drawn in green is the locus of points satisfying the constraint g(x,y) = c. Drawn in blue are contours of f. Arrows represent the gradient, which points in a direction normal to the contour.

In mathematical optimization problems, Lagrange multipliers, named after Joseph Louis Lagrange, are a method for dealing with constraints. We seek the local extrema of a function of several variables subject to one or more constraints. This method reduces the constrained problem in n variables to an unconstrained problem in n + 1 variables wich can be solved. The method introduces a new unknown scalar variable, the Lagrange multiplier, for each constraint and forms a linear combination involving the multipliers as coefficients.

The defense for this can be carried out in the standard way as concerns partial differentiation, using either total differentials, or their close relatives, the chain rules. The object is, for some implicit function, to find the conditions so that the derivative in terms of the independent variables of a function equal zero at some set of inputs.

Introduction

Consider a two-dimensional case. Suppose we have a function, f(x,y), to maximize subject to

g(x,y) = c,

where c is a constant. We can visualize contours of f given by

f(x,y) = d_n

for various values of d_n, and the contour of g given by g(x,y) = c. Suppose we walk along the g = c contour. Since, in general, the contours of f and g will be distinct, traversing the g = c contour will generally cross many contours of f. In general, by moving along the line g=c we can increase or decrease the value of f. Only when the contour we are crossing actually touches tangentially the contour g = c we are following will this not be possible. This occurs at the constrained local extrema and the constrained inflection points of f.

A familiar example can be obtained from weather maps, with their contours for temperature and pressure: the constrained extrema will occur where the superposed maps show touching lines (isopleths).

Geometrically we translate the tangency condition to saying that the gradients of f and g are parallel vectors at the maximum. Introducing an unknown scalar, λ, we solve

for . This

Once values for λ are determined, we are back to the original number of variables and so can go on to find extrema of the new unconstrained equation

F(x,y) = f(x,y) + λ(g(x,y) − c)

in traditional ways. That is, F(x,y) = f(x,y) for all (x,y) satisfying the constraint because g(x,y) − c equals zero on the constraint, but the zeros of are all on g(x,y) = c.

The method of Lagrange multipliers

Let f be a function defined on Rⁿ, and let the constraints be given by g_k(x) = 0 (perhaps by moving the constant to the left, as in g_k(x) - c = 0). Now, define the Lagrangian, Λ, as

Observe that both the optimization criteria and constraints g_k are compactly encoded as extrema of the Lagrangian:

and

Often the Lagrange multipliers have an interpretation as some salient quantity of interest. To see why this might be the case, observe that:

Thus, λ_k is the rate of change of the quantity being optimized as a function of the constraint variable. As examples, in Lagrangian mechanics the equations of motion are derived by finding stationary points of the action, the time integral of the difference between kinetic and potential energy. Thus, the force on a particle due to a scalar potential can be interpreted as a Lagrange multiplier determining the change in action (transfer of potential to kinetic energy) following a variation in the particle's constrained trajectory. In economics, the optimal profit to a player is calculated subject to a constrained space of actions, where a Lagrange multiplier is the value of relaxing a given constraint (e.g. through bribery or other means).

The method of Lagrange multipliers is generalized by the Karush-Kuhn-Tucker conditions.

Example

Suppose we wish to find the discrete probability distribution with maximal information entropy. Then

Of course, the sum of these probabilities equals 1, so our constraint is g(p) = 1 with

We can use Lagrange multipliers to find the point of maximum entropy (depending on the probabilities). For all k from 1 to n, we require that

which gives

Carrying out the differentiation of these n equations, we get

This shows that all p_i are equal (because they depend on λ only). By using the constraint ∑_k p_k = 1, we find

Hence, the uniform distribution is the distribution with the greatest entropy.

For another example, see also derivation of the partition function.

External links

For references to Lagrange's original work and for an account of the terminology see the Lagrange Multiplier entry in

• Earliest known uses of some of the words of mathematics: L

For additional text and interactive applets

• Applet
• Tutorial and applet
• Conceptual introduction (plus a brief discussion of Lagrange multipliers in the calculus of variations as used in physics)
• Lagrange Multipliers without Permanent Scarring (tutorial by Dan Klein)