Optimization in Economic TheoryBuilding on a base of simple economic theory and elementary linear algebra and calculus, this broad treatment of static and dynamic optimization methods discusses the importance of shadow prices, and reviews functions defined by solutions of optimization problems. Recently revised and expanded, the second edition will be a valuable resource for upper level undergraduate and graduate students. |
Contents
Introduction | 1 |
Lagranges Method | 10 |
Extensions and Generalizations | 24 |
Shadow Prices | 40 |
Maximum Value Functions | 55 |
Convex Sets and Their Separation | 69 |
Concave Programming | 86 |
Other editions - View all
Common terms and phrases
argument Chapter choice variables comparative statics complementary slackness component concave function concave programming consider constraint qualification consumer consumer's consumption convex function convex set defined differential Dynamic Programming economic Envelope Theorem equal equation Example Exercise F and G F(xª feasible first-order conditions first-order necessary conditions fixed Hamiltonian income increase indifference curve inequality constraints interpretation intuition Kuhn-Tucker Theorem labor Lagrange multipliers Lagrangian linear m-dimensional marginal utility mathematical matrix maximization problem maximizes F(x Maximum Principle maximum value function Microeconomic negative non-negative notation objective function optimization problem optimum choice output p₁ parameters partial derivatives positive production profit quantity quasi-concave quasi-convex resource result right-hand side risk-aversion row vector satisfy scalar second-order conditions separation shadow prices slope solution solve stocks straints subject to G(x substitution sufficient conditions Suppose tangent theory tion utility function utility level write Y₁ yr+1 zero