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# Equality Constraints (Lagrangians)

Suppose we have a problem:

Maximize subject to If we ignore the constraint, we get the solution , which is too large for the constraint. Let us penalize ourselves for making the constraint too big. We end up with a function This function is called the Lagrangian of the problem. The main idea is to adjust so that we use exactly the right amount of the resource. leads to (2,1). leads to (3/2,0) which uses too little of the resource. gives (5/3, 1/3) and the constraint is satisfied exactly.

We now explore this idea more formally. Given a nonlinear program (P) with equality constraints:

Minimize (or maximize) f(x)

subject to    a solution can be found using the Lagrangian: (Note: this can also be written ).

Each gives the price associated with constraint i.

The reason L is of interest is the following: Of course, Case (i) above cannot occur when there is only one constraint. The following example shows how it might occur. It is easy to check directly that the minimum is acheived at . The associated Lagrangian is Observe that and consequently does not vanish at the optimal solution. The reason for this is the following. Let and denote the left hand sides of the constraints. Then and are linearly dependent vectors. So Case (i) occurs here!

Nevertheless, Case (i) will not concern us in this course. When solving optimization problems with equality constraints, we will only look for solutions that satisfy Case (ii).

Note that the equation is nothing more than In other words, taking the partials with respect to does nothing more than return the original constraints.

Once we have found candidate solutions , it is not always easy to figure out whether they correspond to a minimum, a maximum or neither. The following situation is one when we can conclude. If f(x) is concave and all of the are linear, then any feasible with a corresponding making maximizes f(x) subject to the constraints. Similarly, if f(x) is convex and each is linear, then any with a making minimizes f(x) subject to the constraints.     Now, the first two equations imply . Substituting into the final equation gives the solution , and , with function value 2/3.

Since is convex (its Hessian matrix is positive definite) and is a linear function, the above solution minimizes subject to the constraint.    Next: Geometric Interpretation Up: Constrained Optimization Previous: Constrained Optimization

Michael A. Trick
Mon Aug 24 14:26:21 EDT 1998