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As an example, consider the following function.

$$f\left({x}_{1},{x}_{2}\right)=\{\begin{array}{ll}{x}_{1}^{2}+{x}_{2}^{2}-25\hfill & \text{for}{x}_{1}^{2}+{x}_{2}^{2}\le 25\hfill \\ {x}_{1}^{2}+{\left({x}_{2}-9\right)}^{2}-16\hfill & \text{for}{x}_{1}^{2}+{\left({x}_{2}-9\right)}^{2}\le 16\hfill \\ 0\hfill & \text{otherwise}\text{.}\hfill \end{array}$$

The following figure shows a plot of the function.

Code for generating the figure

The global minimum of the function occurs at (0, 0), where its value is -25. However, the function also has a local minimum at (0, 9), where its value is -16.

To create a file that computes the function, copy and paste
the following code into a new file in the MATLAB^{®} Editor.

function z = poll_example(x) if x(1)^2 + x(2)^2 <= 25 z = x(1)^2 + x(2)^2 - 25; elseif x(1)^2 + (x(2) - 9)^2 <= 16 z = x(1)^2 + (x(2) - 9)^2 - 16; else z = 0; end

Then save the file as `poll_example.m`

in a
folder on the MATLAB path.

To run a pattern search on the function, enter the following in the Optimization app:

Set

**Solver**to`patternsearch`

.Set

**Objective function**to`@poll_example`

.Set

**Start point**to`[0 5]`

.Set

**Level of display**to`Iterative`

in the**Display to command window**options.

Click **Start** to run the pattern search
with **Complete poll** set to `Off`

,
its default value. The Optimization app displays the results in the **Run
solver and view results** pane, as shown in the following
figure.

The pattern search returns the local minimum at (0, 9). At the initial point, (0, 5), the objective function value is 0. At the first iteration, the search polls the following mesh points.

*f*((0, 5) + (1, 0)) = *f*(1, 5) = 0

*f*((0, 5) + (0, 1)) = *f*(0, 6) = -7

As soon as the search polls the mesh point (0, 6), at which the objective function value is less than at the initial point, it stops polling the current mesh and sets the current point at the next iteration to (0, 6). Consequently, the search moves toward the local minimum at (0, 9) at the first iteration. You see this by looking at the first two lines of the command line display.

Iter f-count f(x) MeshSize Method 0 1 0 1 1 3 -7 2 Successful Poll

Note that the pattern search performs only two evaluations of the objective function at the first iteration, increasing the total function count from 1 to 3.

Next, set **Complete poll** to `On`

and
click **Start**. The **Run solver and
view results** pane displays the following results.

This time, the pattern search finds
the global minimum at (0, 0). The difference between this run and
the previous one is that with **Complete poll** set
to `On`

, at the first iteration the pattern search
polls all four mesh points.

*f*((0, 5) + (1, 0)) = *f*(1, 5) = 0

*f*((0, 5) + (0, 1)) = *f*(0, 6) = -6

*f*((0, 5) + (-1, 0)) = *f*(-1, 5) = 0

*f*((0, 5) + (0, -1)) = *f*(0, 4) = -9

Because the last mesh point has the lowest objective function value, the pattern search selects it as the current point at the next iteration. The first two lines of the command-line display show this.

Iter f-count f(x) MeshSize Method 0 1 0 1 1 5 -9 2 Successful Poll

In this case, the objective function is evaluated four times at the first iteration. As a result, the pattern search moves toward the global minimum at (0, 0).

The following figure compares the sequence of points returned
when **Complete poll** is set to `Off`

with
the sequence when **Complete poll** is `On`

.

This example shows how several poll options interact in terms of iterations and total function evaluations. The main results are:

GSS is more efficient than GPS or MADS for linearly constrained problems.

Whether setting

`UseCompletePoll`

to`true`

increases efficiency or decreases efficiency is unclear, although it affects the number of iterations.Similarly, whether having a

`2N`

poll is more or less efficient than having an`Np1`

poll is also unclear. The most efficient poll is`GSS Positive Basis Np1`

with**Complete poll**set to`on`

. The least efficient is`MADS Positive Basis Np1`

with**Complete poll**set to`on`

.

The efficiency of an algorithm depends on the problem. GSS is efficient for linearly constrained problems. However, predicting the efficiency implications of the other poll options is difficult, as is knowing which poll type works best with other constraints.

The problem is the same as in Performing a Pattern Search on the Example. This linearly
constrained problem uses the `lincontest7`

file that
comes with the toolbox:

Enter the following into your MATLAB workspace:

x0 = [2 1 0 9 1 0]; Aineq = [-8 7 3 -4 9 0]; bineq = 7; Aeq = [7 1 8 3 3 3; 5 0 -5 1 -5 8; -2 -6 7 1 1 9; 1 -1 2 -2 3 -3]; beq = [84 62 65 1];

Open the Optimization app by entering

`optimtool`

at the command line.Choose the

`patternsearch`

solver.Enter the problem and constraints as pictured.

Ensure that the

**Poll method**is`GPS Positive basis 2N`

.

Run the optimization.

Choose

**File > Export to Workspace**.Export the results and name them

`gps2noff`

.Set

**Options > Poll > Complete poll**to`on`

.Run the optimization.

Export the result and name it

`gps2non`

.Set

**Options > Poll > Poll method**to`GPS Positive basis Np1`

and set**Complete poll**to`off`

.Run the optimization.

Export the result and name it

`gpsnp1off`

.Set

**Complete poll**to`on`

.Run the optimization.

Export the result and name it

`gpsnp1on`

.Continue in a like manner to create solution structures for the other poll methods with

**Complete poll**set`on`

and`off`

:`gss2noff`

,`gss2non`

,`gssnp1off`

,`gssnp1on`

,`mads2noff`

,`mads2non`

,`madsnp1off`

, and`madsnp1on`

.

You have the results of 12 optimization runs. The following table shows the efficiency of the runs, measured in total function counts and in iterations. Your MADS results could differ, since MADS is a stochastic algorithm.

Algorithm | Function Count | Iterations |
---|---|---|

GPS2N, complete poll off | 1462 | 136 |

GPS2N, complete poll on | 1396 | 96 |

GPSNp1, complete poll off | 864 | 118 |

GPSNp1, complete poll on | 1007 | 104 |

GSS2N, complete poll off | 758 | 84 |

GSS2N, complete poll on | 889 | 74 |

GSSNp1, complete poll off | 533 | 94 |

GSSNp1, complete poll on | 491 | 70 |

MADS2N, complete poll off | 922 | 162 |

MADS2N, complete poll on | 2285 | 273 |

MADSNp1, complete poll off | 1155 | 201 |

MADSNp1, complete poll on | 1651 | 201 |

To obtain, say, the first row in the table, enter `gps2noff.output.funccount`

and `gps2noff.output.iterations`

. You can also examine options
in the Variables editor by double-clicking the options in the Workspace Browser,
and then double-clicking the `output`

structure.

The main results gleaned from the table are:

Setting

**Complete poll**to`on`

generally lowers the number of iterations for GPS and GSS, but the change in number of function evaluations is unpredictable.Setting

**Complete poll**to`on`

does not necessarily change the number of iterations for MADS, but substantially increases the number of function evaluations.The most efficient algorithm/options settings, with efficiency meaning lowest function count:

`GSS Positive basis Np1`

with**Complete poll**set to`on`

(function count 491)`GSS Positive basis Np1`

with**Complete poll**set to`off`

(function count 533)`GSS Positive basis 2N`

with**Complete poll**set to`off`

(function count 758)`GSS Positive basis 2N`

with**Complete poll**set to`on`

(function count 889)

The other poll methods had function counts exceeding 900.

For this problem, the most efficient poll is

`GSS Positive Basis Np1`

with**Complete poll**set to`on`

, although the**Complete poll**setting makes only a small difference. The least efficient poll is`MADS Positive Basis 2N`

with**Complete poll**set to`on`

. In this case, the**Complete poll**setting makes a substantial difference.

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