How Pattern Search Polling Works

Context

patternsearch finds a sequence of points, x0, x1, x2, ... , that approach an optimal point. The value of the objective function either decreases or remains the same from each point in the sequence to the next. This section explains how pattern search works for the function described in Optimize Using the GPS Algorithm.

To simplify the explanation, this section describes how the generalized pattern search (GPS) works using a maximal positive basis of 2N, with Scale set to Off in Mesh options.

This section does not show how the patternsearch algorithm works with bounds or linear constraints. For bounds and linear constraints, patternsearch modifies poll points to be feasible, meaning to satisfy all bounds and linear constraints.

This section does not encompass nonlinear constraints. To understand how patternsearch works with nonlinear constraints, see Nonlinear Constraint Solver Algorithm.

The problem setup:

Successful Polls

The pattern search begins at the initial point x0 that you provide. In this example, x0 = [2.1 1.7].

Iteration 1

At the first iteration, the mesh size is 1 and the GPS algorithm adds the pattern vectors to the initial point x0 = [2.1 1.7] to compute the following mesh points:

[1 0] + x0 = [3.1 1.7]
[0 1] + x0 = [2.1 2.7]
[-1 0] + x0 = [1.1 1.7]
[0 -1] + x0 = [2.1 0.7]

The algorithm computes the objective function at the mesh points in the order shown above. The following figure shows the value of ps_example at the initial point and mesh points.

The algorithm polls the mesh points by computing their objective function values until it finds one whose value is smaller than 4.6347, the value at x0. In this case, the first such point it finds is [1.1 1.7], at which the value of the objective function is 4.5146, so the poll at iteration 1 is successful. The algorithm sets the next point in the sequence equal to

x1 = [1.1 1.7]

    Note   By default, the GPS pattern search algorithm stops the current iteration as soon as it finds a mesh point whose fitness value is smaller than that of the current point. Consequently, the algorithm might not poll all the mesh points. You can make the algorithm poll all the mesh points by setting Complete poll to On.

Iteration 2

After a successful poll, the algorithm multiplies the current mesh size by 2, the default value of Expansion factor in the Mesh options pane. Because the initial mesh size is 1, at the second iteration the mesh size is 2. The mesh at iteration 2 contains the following points:

2*[1 0] + x1 = [3.1 1.7]
2*[0 1] + x1 = [1.1 3.7]
2*[-1 0] + x1 = [-0.9 1.7]
2*[0 -1] + x1 = [1.1 -0.3]

The following figure shows the point x1 and the mesh points, together with the corresponding values of ps_example.

The algorithm polls the mesh points until it finds one whose value is smaller than 4.5146, the value at x1. The first such point it finds is [-0.9 1.7], at which the value of the objective function is 3.25, so the poll at iteration 2 is again successful. The algorithm sets the second point in the sequence equal to

x2 = [-0.9 1.7]

Because the poll is successful, the algorithm multiplies the current mesh size by 2 to get a mesh size of 4 at the third iteration.

An Unsuccessful Poll

By the fourth iteration, the current point is

x3 = [-4.9 1.7]

and the mesh size is 8, so the mesh consists of the points

8*[1 0] + x3 = [3.1 1.7]
8*[0 1] + x3 = [-4.9 9.7]
8*[-1 0] + x3 = [-12.9 1.7]
8*[0 -1] + x3 = [-4.9 -1.3]

The following figure shows the mesh points and their objective function values.

At this iteration, none of the mesh points has a smaller objective function value than the value at x3, so the poll is unsuccessful. In this case, the algorithm does not change the current point at the next iteration. That is,

x4 = x3;

At the next iteration, the algorithm multiplies the current mesh size by 0.5, the default value of Contraction factor in the Mesh options pane, so that the mesh size at the next iteration is 4. The algorithm then polls with a smaller mesh size.

Displaying the Results at Each Iteration

You can display the results of the pattern search at each iteration by setting Level of display to Iterative in the Display to command window options. This enables you to evaluate the progress of the pattern search and to make changes to options if necessary.

With this setting, the pattern search displays information about each iteration at the command line. The first four iterations are

Iter     f-count          f(x)      MeshSize     Method
    0        1        4.63474             1      
    1        4        4.51464             2     Successful Poll
    2        7           3.25             4     Successful Poll
    3       10      -0.264905             8     Successful Poll
    4       14      -0.264905             4     Refine Mesh

The entry Successful Poll below Method indicates that the current iteration was successful. For example, the poll at iteration 2 is successful. As a result, the objective function value of the point computed at iteration 2, displayed below f(x), is less than the value at iteration 1.

At iteration 4, the entry Refine Mesh tells you that the poll is unsuccessful. As a result, the function value at iteration 4 remains unchanged from iteration 3.

By default, the pattern search doubles the mesh size after each successful poll and halves it after each unsuccessful poll.

More Iterations

The pattern search performs 60 iterations before stopping. The following plot shows the points in the sequence computed in the first 13 iterations of the pattern search.

The numbers below the points indicate the first iteration at which the algorithm finds the point. The plot only shows iteration numbers corresponding to successful polls, because the best point doesn't change after an unsuccessful poll. For example, the best point at iterations 4 and 5 is the same as at iteration 3.

Poll Method

At each iteration, the pattern search polls the points in the current mesh—that is, it computes the objective function at the mesh points to see if there is one whose function value is less than the function value at the current point. How Pattern Search Polling Works provides an example of polling. You can specify the pattern that defines the mesh by the Poll method option. The default pattern, GPS Positive basis 2N, consists of the following 2N directions, where N is the number of independent variables for the objective function.

[1 0 0...0]
[0 1 0...0]
...
[0 0 0...1]
[–1 0 0...0]
[0 –1 0...0]
[0 0 0...–1].

For example, if the objective function has three independent variables, the GPS Positive basis 2N, consists of the following six vectors.

[1 0 0]
[0 1 0]
[0 0 1]
[–1 0 0]
[0 –1 0]
[0 0 –1].

Alternatively, you can set Poll method to GPS Positive basis NP1, the pattern consisting of the following N + 1 directions.

[1 0 0...0]
[0 1 0...0]
...
[0 0 0...1]
[–1 –1 –1...–1].

For example, if objective function has three independent variables, the GPS Positive basis Np1, consists of the following four vectors.

[1 0 0]
[0 1 0]
[0 0 1]
[–1 –1 –1].

A pattern search will sometimes run faster using GPS Positive basis Np1 rather than the GPS Positive basis 2N as the Poll method, because the algorithm searches fewer points at each iteration. Although not being addressed in this example, the same is true when using the MADS Positive basis Np1 over the MADS Positive basis 2N, and similarly for GSS. For example, if you run a pattern search on the example described in Linearly Constrained Problem, the algorithm performs 1588 function evaluations with GPS Positive basis 2N, the default Poll method, but only 877 function evaluations using GPS Positive basis Np1. For more detail, see Compare the Efficiency of Poll Options.

However, if the objective function has many local minima, using GPS Positive basis 2N as the Poll method might avoid finding a local minimum that is not the global minimum, because the search explores more points around the current point at each iteration.

Complete Poll

By default, if the pattern search finds a mesh point that improves the value of the objective function, it stops the poll and sets that point as the current point for the next iteration. When this occurs, some mesh points might not get polled. Some of these unpolled points might have an objective function value that is even lower than the first one the pattern search finds.

For problems in which there are several local minima, it is sometimes preferable to make the pattern search poll all the mesh points at each iteration and choose the one with the best objective function value. This enables the pattern search to explore more points at each iteration and thereby potentially avoid a local minimum that is not the global minimum. In the Optimization app you can make the pattern search poll the entire mesh setting Complete poll to On in Poll options. At the command line, use psoptimset to set the CompletePoll option to 'on'.

Stopping Conditions for the Pattern Search

The criteria for stopping the pattern search algorithm are listed in the Stopping criteria section of the Optimization app:

The algorithm stops when any of the following conditions occurs:

  • The mesh size is less than Mesh tolerance.

  • The number of iterations performed by the algorithm reaches the value of Max iteration.

  • The total number of objective function evaluations performed by the algorithm reaches the value of Max function evaluations.

  • The time, in seconds, the algorithm runs until it reaches the value of Time limit.

  • The distance between the point found in two consecutive iterations and the mesh size are both less than X tolerance.

  • The change in the objective function in two consecutive iterations and the mesh size are both less than Function tolerance.

Nonlinear constraint tolerance is not used as stopping criterion. It determines the feasibility with respect to nonlinear constraints.

The MADS algorithm uses an additional parameter called the poll parameter, Δp, in the mesh size stopping criterion:

Δp={NΔmfor positive basis N+1 pollΔmfor positive basis 2N poll,

where Δm is the mesh size. The MADS stopping criterion is:

Δp ≤ Mesh tolerance.

Robustness of Pattern Search

The pattern search algorithm is robust in relation to objective function failures. This means patternsearch tolerates function evaluations resulting in NaN, Inf, or complex values. When the objective function at the initial point x0 is a real, finite value, patternsearch treats poll point failures as if the objective function values are large, and ignores them.

For example, if all points in a poll evaluate to NaN, patternsearch considers the poll unsuccessful, shrinks the mesh, and reevaluates. If even one point in a poll evaluates to a smaller value than any seen yet, patternsearch considers the poll successful, and expands the mesh.

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