Version:

Last Modified: March 15, 2017

Solves a linear programming problem. This node uses formulas to represent the linear function to optimize and the constraints.

To solve the optimization problem, an optimal vector must exist. This node returns an error if an optimal vector does not exist.

Linear function to maximize or minimize. The formula can contain any number of valid variables.

Constraints under which you want to optimize the objective function. The formula can contain any number of valid variables.

Optimization problem this node solves.

Name | Description |
---|---|

Maximize | Solves a maximization problem. |

Minimize | Solves a minimization problem. |

**Default: **Maximize

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an **error in** input and an **error out** output so that the node can respond to and communicate errors that occur while code is running. The value of **error in** specifies whether an error occurred before the node runs. Most nodes respond to values of **error in** in a standard, predictable way.

**Default: **No error

Maximum or minimum value, if it exists, of the solution vector under the constraints.

Solution vector. The *n*^{th} element in **solution** returns the optimal solution of the *n*^{th} element in **objective function**.

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

**error in** input and an **error out** output so that the node can respond to and communicate errors that occur while code is running. The value of **error in** specifies whether an error occurred before the node runs. Most nodes respond to values of **error in** in a standard, predictable way.

The solution to a linear programming problem is a two-step process. This node completes the following steps to solve a linear programming problem.

To find the minimum value of *f*(*x*, *y*) = *x* + *y* under the constraint *x* ≥ 0 and *y* ≥ 2, enter the following values on the panel:

objective function |
x + y |

subject to constraints |
(x >= 0, y >= 2) |

This node returns 2 as **optimization cost** and (0, 2) as **solution**, where the *n*^{th} element in **solution** is the optimal solution of the *n*^{th} variable in **objective function**.

**Where This Node Can Run: **

Desktop OS: Windows

FPGA: This product does not support FPGA devices