## What is a standard minimization problem?

In Section 4.3, we learned that some types of linear programming problems, where the objective function is maximized, are called standard maximization problems. A similar form exists for another for linear programming problems where the objective function is minimized.

A standard minimization problem is a type of linear programming problem in which the objective function is to be minimized and has the form

*w* = *d*_{1} *y*_{1} + *d*_{2} *y*_{2} + … + *d _{n}*

*y*

_{n}where *d*_{1}, … , *d _{n}* are real numbers and

*y*

_{1}, … ,

*y*are decision variables. The decision variables must represent non-negative values. The other constraints for the standard minimization problem have the form

_{n}*e*_{1} *y*_{1} + *e*_{2} *y*_{2} + … + *e _{n}*

*y*

_{n }≥ fwhere *e*_{1}, … , *e _{n}* and

*f*are real numbers and

*f*≥ 0.

The standard minimization problem is written with the decision variables *y*_{1}, … , *y _{n}*, but any letters could be used as long as the standard minimization problem and the corresponding dual maximization problem do not share the same variable names.

Often a problem can be rewritten to put it into standard minimization form. In particular, constraints are often manipulated algebraically so the each constraint has the form *e*_{1} *y*_{1} + *e*_{2} *y*_{2} + … + *e _{n}*

*y*. Example 1 demonstrates how a constraint can be changed to put it in the proper form.

_{n }≥ fFor the problems in this section, we will require the coefficients of the objective function be positive. Although this is not a requirement of the Simplex Method, it simplifies the presentation in this section.

### Example 1 Write As A Standard Minimization Problem

In Section 4.2, we solved the linear programming problem

using a graph. Rewrite this linear programming problem as a standard minimization problem.

**Solution** In a standard minimization problem, the objective function must have the form *w* = *d*_{1} *y*_{1} + *d*_{2} *y*_{2} + … + *d _{n}*

*y*where

_{n }*d*

_{1}, … ,

*d*are real number constants and

_{n}*y*

_{1}, … ,

*y*are the decision variables. The objective function matches this form with .

_{n }Each constraint must have the form *e*_{1} *y*_{1} + *e*_{2} *y*_{2} + … + *e _{n}*

*y*where

_{n }≥ f*e*

_{1}, … ,

*e*and

_{n}*f*are real number constants. Additionally, the constant

*f*must be non-negative. The second constraint, 7

*y*

_{1}+ 4

*y*

_{2}≥ 32, fits this form perfectly.

The first constraint appears to have the correct type of terms, but variable terms are on both sides of the inequality. To put in the proper format, add ^{1}/_{4 }*y*_{1} to both sides of the inequality:

^{1}/_{4} *y*_{1} + *y*_{2} ≥ 2

With this change, we can write the problem as a standard minimization problem,

In addition to adding and subtracting terms to a constraint, we can also multiply or divide the terms in a constraint by nonzero real numbers. However, remember that the direction of the inequality changes when you multiply or divide by a negative number. This can complicate or even prevent a linear programming problem from being changed to standard minimization form.