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Unformatted text preview: The KuhnTucker and Envelope Theorems Peter Ireland * EC720.01  Math for Economists Boston College, Department of Economics Fall 2009 The KuhnTucker and Envelope theorems can be used to characterize the solution to a wide range of constrained optimization problems: static and dynamic, under perfect fore sight or featuring randomness and uncertainty. In addition, these same two theorems provide foundations for the work we will do later on the maximum principle and dynamic program ming. For both of these reasons, they provide a good starting point for our analysis. Lets consider each in turn, first in fairly general or abstract settings and then applied to some economic examples. 1 The KuhnTucker Theorem References: Dixit, Chapters 2 and 3. SimonBlume, Chapter 18. Acemoglu, Appendix A. Consider a simple constrained optimization problem: x R choice variable F : R R objective function, continuously differentiable c G ( x ) constraint, with c R and G : R R , also continuously differentiable. The problem can be stated as: max x F ( x ) subject to c G ( x ) * Copyright c 2009 by Peter Ireland. Redistribution is permitted for educational and research purposes, so long as no changes are made. All copies must be provided free of charge and must include this copyright notice. 1 This problem is simple because it is static and contains no random or stochastic elements that would force decisions to be made under uncertainty. This problem is also simple because it has a single choice variable and a single constraint. All these simplifications will make our statement and proof of the KuhnTucker theorem as clean and intuitive as possible. But the results can be generalized along all of these dimensions and, later, we will work through examples that do so. Probably the easiest way to solve this problem is via the method of Lagrange multipliers. The mathematical foundations that allow for the application of this method are given to us by Lagranges Theorem or, in its most general form, the KuhnTucker Theorem. To prove this theorem, begin by defining the Lagrangian: L ( x, ) = F ( x ) + [ c G ( x )] for any x R and R . Theorem (KuhnTucker) Suppose that x * maximizes F ( x ) subject to c G ( x ), where F and G are both continuously differentiable, and suppose that G ( x * ) 6 = 0. Then there exists a value * of such that x * and * satisfy the following four conditions: L 1 ( x * , * ) = F ( x * ) * G ( x * ) = 0 , (1) L 2 ( x * , * ) = c G ( x * ) , (2) * , (3) and * [ c G ( x * )] = 0 . (4) Proof Consider two possible cases, depending on whether or not the constraint is binding at x * ....
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This note was uploaded on 02/19/2010 for the course ECON 720 taught by Professor Ireland during the Fall '09 term at BC.
 Fall '09
 IRELAND
 Economics

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