Constrained optimization: Difference between revisions

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Substitution method: g(x) → p(x); \frac{\partial g}{\partial x} → \frac{\partial p}{\partial x} {objective: to prevent confusion of the interpretation here as an adapted cost function with the interpretation in an equality constraint for g intended elsewhere in the article }
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===Equality constraints===
====Substitution method====
For very simple problems, say a function of two variables subject to a single equality constraint, it is most practical to apply the method of substitution.<ref>{{cite book |first=Mike |last=Prosser |title=Basic Mathematics for Economists |___location=New York |publisher=Routledge |year=1993 |isbn=0-415-08424-5 |chapter=Constrained Optimization by Substitution |pages=338–346 }}</ref> The idea is to substitute the constraint into the objective function to create a [[Function composition|composite function]] that incorporates the effect of the constraint. For example, assume the objective is to maximize <math>f(x,y) = x \cdot y</math> subject to <math>x + y = 10</math>. The constraint implies <math>y = 10 - x</math>, which can be substituted into the objective function to create <math>gp(x) = x (10 - x) = 10x - x^{2}</math>. The first-order necessary condition gives <math>\frac{\partial gp}{\partial x} = 10 - 2x = 0</math>, which can be solved for <math>x=5</math> and, consequently, <math>y = 10 - 5 = 5</math>.
 
====Lagrange multiplier====