We give a new method of finding extrema.

**There is more than one way to solve it.**

The method of Lagrange multipliers tells us that to maximize a function constrained to a curve, we need to find where the gradient of the function is perpendicular to the curve.

Previously, when we were finding extrema of functions when constrained to some curve, we had to find an explicit formula for the curve. Consider this example from the previous section:

The first step for solving this problem was to find an explicit formula that
drew the curve . In the case above, we choose: However, finding a function
that draws the constraining set could be very difficult or even impossible!
If our constraining set had been our previous method will not work, as
we (at least this author!) cannot find an explicit formula describing the set
above. Nevertheless, there is another way. It is called the method of *Lagrange
multipliers*. This method is named after the mathematician Joseph-Louis
Lagrange. This method relies on the geometric properties of the *gradient vector*.
Recall: There are three things you must know about the gradient vector:

- .
- points in the direction that one must leave in order to see the initial greatest increase in .
- points in the direction that is perpendicular to any level surface of .

It is the last two facts that we will think about now. Below we see level curves for some function along with a constraining curve that we will call :

Let’s add vectors to our graph that point in the direction of . Since we know that the
gradient vector is perpendicular to level curves, we can do this *without* computation.

**cannot**give an extremal value of , as moving along will either increase or decrease the value of . Here’s the upshot:

**The only candidates for local extrema occur when the
gradient of is perpendicular to .**

How do we find these points? To do this, we will imagine that is a level curve for some other function , and define as: now, the candidates for extrema of when constrained to a curve are found by finding such that since that satisfy this equation are those where the gradient vectors of are perpendicular to the level curve . This is the essence of the method of Lagrange multipliers.

The first step for solving a constrained optimization problem using the method of Lagrange multipliers is to write down the equations needed to solve the problem.

### Working with geometry

Lagrange multipliers tell us that to maximize a function along a curve defined by , we need to find where is perpendicular to . In essence we are detecting geometric behavior using the tools of calculus.

### Working with algebra

We’ll start with an example we did in our last section.

Lagrange multipliers help out when when constraint set is given by an implicit function. Let’s see this in an example.

The method of Lagrange multipliers gives a unified method for solving a large class of constrained optimization problems, and hence is used in many areas of applied mathematics.

For some interesting extra reading check out:

*Unifying a Family of Extrema Problems*, W. Barnier and D. Martin, College Math Journal, November 1997.*An “Extremely” Cautionary Tale*, M. Krusemeyer, College Math Journal, March 2000.*Lagrange Multipliers Can Fail to Determine Extrema*, J. Nunemacher, College Math Journal, January 2003.*On the Genesis of the Lagrange Multipliers*, P. Bussotti, Journal of Optimization Theory and Applications, June 2003.