## Surrogate Optimization Options

### Algorithm Control

To control the surrogate optimization algorithm, use the following options.

• `ConstraintTolerance` — The constraint tolerance is not used as a stopping criterion. It is used to determine feasibility with respect to nonlinear constraints. The tolerance is satisfied when `max(fun(x).Ineq) <= ConstraintTolerance`, and otherwise is violated. The default value is `1e-3`.

• `InitialPoints` — Specify initial points in one of two ways.

• Matrix — Each row of the matrix represents an initial point. The length of each row is the same as the number of elements in the bounds `lb` or `ub`. The number of rows is arbitrary. `surrogateopt` uses all the rows to construct the initial surrogate. If there are fewer than `MinSurrogatePoints` rows, then `surrogateopt` generates the remaining initial points. `surrogateopt` evaluates the objective function at each initial point.

• Structure — The structure contains the field `X` and, optionally, the fields `Fval` and `Ineq`. The `X` field contains a matrix where each row represents an initial point. The `Fval` field contains a vector representing the objective function values at each point in `X`. Passing `Fval` saves time for the solver, because otherwise the solver evaluates the objective function value at each initial point. The `Ineq` field contains a matrix containing nonlinear inequality constraint values. Each row of `Ineq` represents one initial point, and each column represents a nonlinear constraint function value at that point. Passing `Ineq` saves time for the solver, because otherwise the solver evaluates the constraint function values at each initial point.

• `MinSurrogatePoints` — Number of initial points used for constructing the surrogate. Larger values lead to a more accurate finished surrogate, but take more time to finish the surrogate. `surrogateopt` creates this number of random points after each switch to the random generation phase. See Surrogate Optimization Algorithm.

• `MinSampleDistance` — This option controls two aspects of the algorithm.

• During the phase to estimate the minimum value of the surrogate, the algorithm generates random points at which to evaluate the surrogate. If any of these points are closer than `MinSampleDistance` to any previous point whose objective function value was evaluated, then `surrogateopt` discards the newly generated points and does not evaluate them.

• If `surrogateopt` discards all of the random points, then it does not try to minimize the surrogate and, instead, switches to the random generation phase. If the `surrogateoptplot` plot function is running, then it marks this switch with a blue vertical line.

For details, see Surrogate Optimization Algorithm.

### Stopping Criteria

Generally, the algorithm stops only when it reaches a limit that you set in the solver options. Additionally, a plot function or output function can halt the solver.

Stopping OptionStopping TestExit Flag
`MaxFunctionEvaluations`The solver stops after it completes `MaxFunctionEvaluations` function evaluations. When computing in parallel, the solver stops all workers after a worker returns with the final function evaluation, leaving some computations incomplete and unused.`0`
`MaxTime`The solver stops after it reaches `MaxTime` seconds from the start of the optimization, as measured by `tic` / `toc`. The solver does not interrupt a function evaluation in progress, so the actual compute time can exceed `MaxTime`.`0`
`ObjectiveLimit`The solver stops if it obtains an objective function value less than `ObjectiveLimit`.`1`
`OutputFcn` or `PlotFcn`An `OutputFcn` or `PlotFcn` can halt the iterations.`-1`
Bounds `lb` and `ub`If an entry in `lb` exceeds the corresponding entry in `ub`, the solver stops because the bounds are inconsistent.`-2`

### Command-Line Display

Set the `Display` option to control what `surrogateopt` returns to the command line.

• `'final'` — Return only the exit message. This is the default behavior.

• `'iter'` — Return iterative display.

• `'off'` or the equivalent `'none'` — No command-line display.

With an iterative display, the solver returns the following information in table format.

• `F-count` — Number of function evaluations

• `Time(s)` — Time in seconds since the solver started

• `Best Fval` — Lowest objective function value obtained

• `Current Fval` — Latest objective function value

• `Trial Type` — Algorithm giving the evaluated point, either `random` or `adaptive`. For details, see Surrogate Optimization Algorithm.

When the objective function returns a nonlinear constraint, the iterative display of `Best Fval` and `Current Fval` changes. Instead, the titles are `Best` and `Current`, and each displays two columns, `(Fval, Infeas)`.

• When a point is feasible, `surrogateopt` displays the function value, and shows `-` as the infeasibility.

• When a point is infeasible, `surrogateopt` displays the maximum infeasibility among all nonlinear constraint functions (a positive number), and shows `-` as the function value.

• Once `surrogateopt` finds a feasible point, subsequent entries in the `Best` column show only the smallest function value found, and show `-` as the best infeasibility.

With iterative display, the solver also returns problem information before the table:

• Number of variables

• Type of objective function (scalar or none)

• Number of inequalities

### Output Function

An output function can halt the solver or perform a computation at each iteration. To include an output function, set the `OutputFcn` option to `@myoutputfcn`, where `myoutputfcn` is a function with the syntax described in the next paragraph. This syntax is the same as for Optimization Toolbox™ output functions, but with different meanings of the `x` and `optimValues` arguments. For information about those output functions, see Output Function Syntax (Optimization Toolbox). For an example of an output function, see Integer Optimization with Custom Output Function.

The syntax of an output function is:

`stop = outfun(x,optimValues,state)`

`surrogateopt` passes the values of `x`, `optimValues`, and `state` to the output function (`outfun`, in this case) at each iteration. The output function returns `stop`, a Boolean value (`true` or `false`) indicating whether to stop `surrogateopt`.

• `x` — The input argument `x` is the best point found so far, meaning the point with the lowest objective function value.

• `optimValues` — This input argument is a structure containing the following fields. For more information about these fields, see Surrogate Optimization Algorithm.

`optimValues` Structure

Field NameContents
`constrviolation`Maximum constraint violation of best point, `max(optimValues.ineq)`
`currentConstrviolation`Maximum constraint violation of current point, `max(optimValues.currentIneq)`
`currentFlag`

How the current point was created.

• `'initial'` — Initial point passed in `options.InitialPoints`

• `'random'` — Random sample within the bounds

• `'adaptive'` — Result of the solver trying to minimize the surrogate

`currentFval`

Objective function value at the current point

`currentIneq`Constraint violation vector of current point, `fun(currentX).Ineq`
`currentX`

Current point

`elapsedtime`

Time in seconds since the solver started

`flag`

How the best point was created

• `'initial'` — Initial point passed in `options.InitialPoints`

• `'random'` — Random sample within the bounds

• `'adaptive'` — Result of the solver trying to minimize the surrogate

`funccount`

Total number of objective function evaluations

`fval`

Lowest objective function value encountered

`incumbentConstrviolation`Maximum constraint violation of current point, `max(optimValues.incumbentIneq)`
`incumbentIneq`Constraint violation vector of incumbent point, `fun(incumbentX).Ineq`
`incumbentFlag`

How the incumbent point was created

• `'initial'` — Initial point passed in `options.InitialPoints`

• `'random'` — Random sample within the bounds

• `'adaptive'` — Result of the solver trying to minimize the surrogate

`incumbentFval`

Objective function value at the incumbent point

`incumbentX`

Incumbent point, meaning the best point found since the last phase shift to random sampling

`ineq`Constraint violation vector of best point, `fun(x).Ineq`
`iteration`

Same as `funccount`; allows `surrogateopt` to use the same plot functions as some other solvers

`surrogateReset`

Boolean value indicating that the current iteration resets the model and switches to random sampling

`surrogateResetCount`

Total number of times that `surrogateReset` is `true`

• `state` — This input argument is the state of the algorithm, specified as one of these values.

• `'init'` — The algorithm is in the initial state before the first iteration. When the algorithm is in this state, you can set up plot axes or other data structures or open files.

### Note

When `state` is `'init'`, the input arguments `x` and `optimValues.fval` are empty (`[]`) because `surrogateopt` is designed for time-consuming objective functions, and so does not evaluate the objective function before calling the initialization step.

• `'iter'` — The algorithm just evaluated the objective function. You perform most calculations and view most displays when the algorithm is in this state.

• `'done'` — The algorithm performed its final objective function evaluation. When the algorithm is in this state, you can close files, finish plots, or prepare in other ways for `surrogateopt` to stop.

### Plot Function

A plot function displays information at each iteration. You can pause or halt the solver by clicking buttons on the plot. To include a plot function, set the `PlotFcn` option to a function name or function handle or cell array of function names or handles to plot functions. The four built-in plot functions are:

• `'optimplotfvalconstr'` (default) — Plot the best feasible objective function value found as a line plot. If there is no objective function, plot the maximum nonlinear constraint violation as a line plot.

• The plot shows infeasible points as red and feasible points as blue.

• If there is no objective function, the plot title shows the number of feasible solutions.

• `'optimplotfval'` — Shows the best function value. If you do not choose a plot function, `surrogateopt` uses `@optimplotfval`.

• `'optimplotx'` — Shows the best point found as a bar plot.

• `'surrogateoptplot'` — Shows the current objective function value, best function value, and information about the algorithm phase. See Interpret surrogateoptplot.

You can write a custom plot function using the syntax of an Output Function. For an example, examine the code for `surrogateoptplot` by entering ```type surrogateoptplot``` at the MATLAB® command line.

### Parallel Computing

If you set the `'UseParallel'` option to `true`, `surrogateopt` computes in parallel. Computing in parallel requires a Parallel Computing Toolbox™ license. For details, see Surrogate Optimization Algorithm.

### Checkpoint File

When you set the name of a checkpoint file using the `CheckpointFile` option, `surrogateopt` writes data to the file after each iteration, which enables the function to resume the optimization from the current state. When restarting, `surrogateopt` does not evaluate the objective function value at previously evaluated points.

A checkpoint file can be a file path such as `"C:\Documents\MATLAB\check1.mat"` or a file name such as `'checkpoint1June2019.mat'`. If you specify a file name without a path, `surrogateopt` saves the checkpoint file in the current folder.

You can change only the following options when resuming the optimization:

• `CheckpointFile`

• `Display`

• `MaxFunctionEvaluations`

• `MaxTime`

• `MinSurrogatePoints`

• `ObjectiveLimit`

• `OutputFcn`

• `PlotFcn`

• `UseParallel`

To resume the optimization from a checkpoint file, call `surrogateopt` with the file name as the first argument.

`[x,fval,exitflag,output] = surrogateopt('check1.mat')`

To resume the optimization using new options, include the new options as the second argument.

```opts = optimoptions(options,'MaxFunctionEvaluations',500); [x,fval,exitflag,output] = surrogateopt('check1.mat',opts)```

During the restart, `surrogateopt` runs any output functions and plot functions, based on the original function evaluations. So, for example, you can create a different plot based on an optimization that already ran. See Work with Checkpoint Files.

### Note

`surrogateopt` does not save all details of the state in the checkpoint file. Therefore, subsequent iterations can differ from the iterations that the solver takes without stopping at the checkpointed state.

### Note

Checkpointing takes time. This overhead is especially noticeable for functions that otherwise take little time to evaluate.

### Warning

Do not resume `surrogateopt` from a checkpoint file created with a different MATLAB version. `surrogateopt` can throw an error or give inconsistent results.

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