Difference between revisions of "Duality"

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=== Construct the Dual for the following maximization problem: ===
 
=== 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>
  
subject to:
+
'''subject to:'''
  
 
<math>\tfrac{1}{2}x_1+2x_2+x_3\leq 24</math>
 
<math>\tfrac{1}{2}x_1+2x_2+x_3\leq 24</math>
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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>
 +
 
 +
Find the transpose of matrix A
 +
 
 +
<math>A^T=\begin{bmatrix} \tfrac{1}{2} & 1 & 6 \\ 2 & 2 & 14 \\ 1 & 4 & 13 \\ 24 & 60 & 1\end{bmatrix}</math>
 +
 
 +
From the last row of the transpose of matrix A, we can derive the objective function of the dual. Each of the preceding rows represents a constraint. Note that the original maximization problem had three variables and two constraints. The dual problem has two variables and three constraints.
 +
 
 +
'''minimize''' <math>z=24y-1+60y_2
 +
</math>
 +
 
 +
'''subject to:'''
 +
 
 +
<math>\tfrac{1}{2}y_1+y_2 \geq 6</math>
  
<math>A =\begin{bmatrix} \tfrac{1}{2} & 2 & 1 & 24 \\ 1 & 2 & 4 & 60  \\ 6 & 14 & 13 & 1 \end{bmatrix}</math>
+
<math>2y_1+2y_2\geq 14</math>
  
Note that the original maximization problem had three variables and two constraints. The dual problem has two variables and three constraints.
+
<math>y_1+4y_2\geq 13</math>
  
 
== Applications ==
 
== Applications ==

Revision as of 21:46, 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.

Find the transpose of matrix A

From the last row of the transpose of matrix A, we can derive the objective function of the dual. Each of the preceding rows represents a constraint. Note that the original maximization problem had three variables and two constraints. The dual problem has two variables and three constraints.

minimize

subject to:

Applications

Conclusion

References

  1. https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/lecture-notes/lec18_duality_thy.pdf
  2. http://web.mit.edu/15.053/www/AMP-Chapter-04.pdf