1 / 20

# Operations Research - PowerPoint PPT Presentation

Operations Research. Chapter 4 Linear Programming Duality and Sensitivity analysis. Linear Programming Duality and Sensitivity analysis. Dual Problem of an LPP Definition of the dual problem Examples (1,2 and 3) Sensitivity Analysis Examples (1 and 2). Dual Problem of an LPP.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' Operations Research' - monifa

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Chapter 4

Linear Programming

Duality and Sensitivity analysis

Linear Programming Duality and Sensitivity analysis

• Dual Problem of an LPP

• Definition of the dual problem

• Examples (1,2 and 3)

• Sensitivity Analysis

• Examples (1 and 2)

Given a LPP (called the primal problem), we shall associate another LPP called the dual problem of the original (primal) problem. We shall see that the Optimal values of the primal and dual are the same provided both have finite feasible solutions. This topic is further used to develop another method of solving LPPs and is also used in the sensitivity (or post-optimal) analysis.

Given the primal problem (in standard form)

Maximize

subject to

Minimize

subject to

If the primal problem (in standard form) is

Minimize

subject to

Maximize

subject to

1. In the dual, there are as many (decision) variables as there are constraints in the primal.

We usually say yi is the dual variable associated with the ith constraint of the primal.

2. There are as many constraints in the dual as there are variables in the primal.

3. If the primal is maximization then the dual is minimization and all constraints are 

If the primal is minimization then the dual is maximization and all constraints are 

• In the primal, all variables are  0 while in the dualall the variables are unrestricted in sign.

5. The objective function coefficients minimization and all constraints are cjof the primal are the RHS constants of the dual constraints.

6. The RHS constants bi of the primal constraints are the objective function coefficients of the dual.

7. The coefficient matrix of the constraints of the dual is the transpose of the coefficient matrix of the constraints of the primal.

Example1 minimization and all constraints are

Write the dual of the LPP

Maximize

subject to

Thus the primal in the standard form is: minimization and all constraints are

Maximize

subject to

Hence the dual is: minimization and all constraints are

Minimize

subject to

Example2 minimization and all constraints are

Write the dual of the LPP

Minimize

subject to

Thus the primal in the standard form is: minimization and all constraints are

Minimize

subject to

Hence the dual is: minimization and all constraints are

Maximize

subject to

Example3 minimization and all constraints are

Write the dual of the LPP

Maximize

subject to

Thus the primal in the standard form is: minimization and all constraints are

Maximize

subject to

Hence the dual is: minimization and all constraints are

Minimize

subject to

Theorem: minimization and all constraints are The dual of the dual is the primal (original problem).

Proof. Consider the primal problem (in standard form)

Maximize

subject to