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This example uses the objective function, `ps_example`,
which is included with Global Optimization Toolbox software.
View the code for the function by entering

type ps_example

The following figure shows a plot of the function.

To find the minimum of `ps_example`, perform
the following steps:

optimtool

and then choose the

`patternsearch`solver.In the

**Objective function**field of the Optimization app, enter`@ps_example`.In the

**Start point**field, type`[2.1 1.7]`.Leave the fields in the

**Constraints**pane blank because the problem is unconstrained.

The **Run solver and view results** pane
displays the results of the pattern search.

The reason the optimization terminated is that the mesh size
became smaller than the acceptable tolerance value for the mesh size,
defined by the **Mesh tolerance** parameter in the **Stopping
criteria** pane. The minimum function value is approximately
–2. The **Final point** pane displays
the point at which the minimum occurs.

To see the performance of the pattern search, display plots
of the best function value and mesh size at each iteration. First,
select the following check boxes in the **Plot functions** pane:

**Best function value****Mesh size**

Then click **Start** to run the pattern search.
This displays the following plots.

The upper plot shows the objective function value of the best point at each iteration. Typically, the objective function values improve rapidly at the early iterations and then level off as they approach the optimal value.

The lower plot shows the mesh size at each iteration. The mesh size increases after each successful iteration and decreases after each unsuccessful one, explained in How Pattern Search Polling Works.

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