# Difference between revisions of "Duality"

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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. | 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>\qquad \begin{matrix} x_1 & x_2 & x_3 \end{matrix}</math> | ||

<math>A =\begin{bmatrix} \tfrac{1}{2} & 2 & 1 & 24 \\ 1 & 2 & 4 & 60 \\ 6 & 14 & 13 & 1 \end{bmatrix}</math> | <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. | + | Note that the original maximization problem had three variables and two constraints. The dual problem has two variables and three constraints. |

== Applications == | == Applications == |

## Revision as of 22:35, 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. The dual problem has two variables and three constraints.