Duality: Difference between revisions
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== Numerical Example == | == Numerical Example == | ||
=== Construct the Dual for the following maximization problem: === | |||
maximize <math>z=6x_1+14x_2+13x_3</math> | maximize <math>z=6x_1+14x_2+13x_3</math> | ||
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<math>x_1+2x_2+4x_3\leq 60</math> | <math>x_1+2x_2+4x_3\leq 60</math> | ||
For the problem above, form augmented matrix A. The first two rows represent constraints one and two respectively. The last row represents the objective function. | |||
<math>A =\begin{bmatrix} \tfrac{1}{2} & 2 & 1 & 24 \\ 1 & 2 & 4 & 60 \\ 6 & 14 & 13 & 1 \end{bmatrix}</math> | |||
Note that the original maximization problem had three variables and two constraints. | |||
== Applications == | == Applications == |
Revision as of 21:31, 7 November 2020
Author: Claire Gauthier, Trent Melsheimer, Alexa Piper, Nicholas Chung, Michael Kulbacki (SysEn 6800 Fall 2020)
Steward: TA's name, Fengqi You
Introduction
Every linear programming optimization problem may be viewed either from the primal or the dual, this is the principal of duality. Duality develops the relationships between one linear programming problem and another related linear programming problem. For example in economics, if the primal optimization problem deals with production and consumption levels, then the dual of that problem relates to the prices of goods and services. The dual variables in this example can be referred to as shadow prices.
The shadow price of a constraint ...
Theory, methodology, and/or algorithmic discussions
Definition:
Primal
Maximize
subject to:
Dual
Minimize
subject to:
Constructing a Dual:
Numerical Example
Construct the Dual for the following maximization problem:
maximize
subject to:
For the problem above, form augmented matrix A. The first two rows represent constraints one and two respectively. The last row represents the objective function.
Note that the original maximization problem had three variables and two constraints.