# run

Run multiple-start solver

## Syntax

## Description

## Examples

### Run `GlobalSearch`

on Multidimensional Problem

Create an optimization problem that has several local minima, and try to find the global minimum using `GlobalSearch`

. The objective is the six-hump camel back problem (see Run the Solver).

rng default % For reproducibility gs = GlobalSearch; sixmin = @(x)(4*x(1)^2 - 2.1*x(1)^4 + x(1)^6/3 ... + x(1)*x(2) - 4*x(2)^2 + 4*x(2)^4); problem = createOptimProblem('fmincon','x0',[-1,2],... 'objective',sixmin,'lb',[-3,-3],'ub',[3,3]); x = run(gs,problem)

GlobalSearch stopped because it analyzed all the trial points. All 8 local solver runs converged with a positive local solver exit flag.

`x = `*1×2*
-0.0898 0.7127

You can request the objective function value at `x`

when you call `run`

by using the following syntax:

`[x,fval] = run(gs,problem)`

However, if you neglected to request `fval`

, you can still compute the objective function value at `x`

.

fval = sixmin(x)

fval = -1.0316

### Run a Multiple Start Solver

Use a default `MultiStart`

object to solve the six-hump camel back problem (see Run the Solver).

rng default % For reproducibility ms = MultiStart; sixmin = @(x)(4*x(1)^2 - 2.1*x(1)^4 + x(1)^6/3 ... + x(1)*x(2) - 4*x(2)^2 + 4*x(2)^4); problem = createOptimProblem('fmincon','x0',[-1,2],... 'objective',sixmin,'lb',[-3,-3],'ub',[3,3]); [x,fval,exitflag,outpt,solutions] = run(ms,problem,30);

MultiStart completed the runs from all start points. All 30 local solver runs converged with a positive local solver exit flag.

Examine the best function value and the location where the best function value is attained.

`fprintf('The best function value is %f.\n',fval)`

The best function value is -1.031628.

`fprintf('The location where this value is attained is [%f,%f].',x)`

The location where this value is attained is [-0.089842,0.712656].

### Run `MultiStart`

from a Regular Array

Create a set of initial 2-D points for `MultiStart`

in the range `[-3,3]`

for each component.

v = -3:0.5:3; [X,Y] = meshgrid(v); ptmatrix = [X(:),Y(:)]; tpoints = CustomStartPointSet(ptmatrix);

Find the point that minimizes the six-hump camel back problem (see Run the Solver) by starting `MultiStart`

at the points in `tpoints`

.

rng default % For reproducibility ms = MultiStart; sixmin = @(x)(4*x(1)^2 - 2.1*x(1)^4 + x(1)^6/3 ... + x(1)*x(2) - 4*x(2)^2 + 4*x(2)^4); problem = createOptimProblem('fmincon','x0',[-1,2],... 'objective',sixmin,'lb',[-3,-3],'ub',[3,3]); x = run(ms,problem,tpoints)

MultiStart completed the runs from all start points. All 169 local solver runs converged with a positive local solver exit flag.

`x = `*1×2*
0.0898 -0.7127

### Examine `GlobalSearch`

Process

Create an optimization problem that has several local minima, and try to find the global minimum using `GlobalSearch`

. The objective is the six-hump camel back problem (see Run the Solver).

rng default % For reproducibility gs = GlobalSearch; sixmin = @(x)(4*x(1)^2 - 2.1*x(1)^4 + x(1)^6/3 ... + x(1)*x(2) - 4*x(2)^2 + 4*x(2)^4); problem = createOptimProblem('fmincon','x0',[-1,2],... 'objective',sixmin,'lb',[-3,-3],'ub',[3,3]); [x,fval,exitflag,output,solutions] = run(gs,problem);

GlobalSearch stopped because it analyzed all the trial points. All 8 local solver runs converged with a positive local solver exit flag.

To understand what `GlobalSearch`

did to solve this problem, examine the `output`

structure and `solutions`

object.

disp(output)

funcCount: 2245 localSolverTotal: 8 localSolverSuccess: 8 localSolverIncomplete: 0 localSolverNoSolution: 0 message: 'GlobalSearch stopped because it analyzed all the trial points....'

`GlobalSearch`

evaluated the objective function 2261 times.`GlobalSearch`

ran`fmincon`

starting from eight different points.All of the

`fmincon`

runs converged successfully to a local solution.

disp(solutions)

1x4 GlobalOptimSolution array with properties: X Fval Exitflag Output X0

arrayfun(@(x)x.Output.funcCount,solutions)

`ans = `*1×4*
31 34 40 3

The eight local solver runs found four solutions. The `funcCount`

output shows that `fmincon`

took no more than 40 function evaluations to reach each of the four solutions. The output does not show how many function evaluations four of the `fmincon`

runs took. Most of the 2261 function evaluations seem to be for `GlobalSearch`

to evaluate trial points, not for `fmincon`

to run starting from those points.

## Input Arguments

`gs`

— `GlobalSearch`

solver

`GlobalSearch`

object

`GlobalSearch`

solver, specified as a `GlobalSearch`

object. Create
`gs`

using the `GlobalSearch`

command.

`ms`

— `MultiStart`

solver

`MultiStart`

object

`MultiStart`

solver, specified as a `MultiStart`

object. Create
`ms`

using the `MultiStart`

command.

`problem`

— Optimization problem

problem structure

Optimization problem, specified as a problem structure. Create
`problem`

using `createOptimProblem`

. For further
details, see Create Problem Structure.

**Example: **```
problem =
createOptimProblem('fmincon','objective',fun,'x0',x0,'lb',lb)
```

**Data Types: **`struct`

`k`

— Number of start points

positive integer

Number of start points, specified as a positive integer. `MultiStart`

generates `k - 1`

start points using the same algorithm as for a `RandomStartPointSet`

object. `MultiStart`

also uses the `x0`

point from the
`problem`

structure.

**Example: **`50`

**Data Types: **`double`

`startpts`

— Start points for `MultiStart`

`CustomStartPointSet`

object | `RandomStartPointSet`

object | cell array of such objects

Start points for `MultiStart`

, specified as a `CustomStartPointSet`

object, as a
`RandomStartPointSet`

object, or as a
cell array of such objects.

**Example: **`{custompts,randompts}`

## Output Arguments

`x`

— Best point found

real array

Best point found, returned as a real array. The best point is the one with lowest objective function value.

`fval`

— Lowest objective function value encountered

real scalar

Lowest objective function value encountered, returned as a real scalar.
For `lsqcurvefit`

and `lsqnonlin`

, the
objective function is the sum of squares, also known as the squared norm of
the residual.

`exitflag`

— Exit condition summary

integer

Exit condition summary, returned as an integer.

**Global Solver Exit Flags**

`2` | At least one feasible local minimum found. Some runs of the local solver did not converge. |

`1` | At least one feasible local minimum found. All runs of the local solver converged (had positive exit flag). |

`0` | No local minimum found. Local solver called at least once, and at least one local solver exceeded the `MaxIterations` or `MaxFunctionEvaluations` tolerances. |

`-1` | One or more local solver runs stopped by the local solver output or plot function. |

`-2` | No feasible local minimum found. |

`-5` | `MaxTime` limit exceeded. |

`-8` | No solution found. All runs had local solver exit flag `-2` or lower, not all equal `-2` . |

`-10` | Failures encountered in user-provided functions. |

`output`

— Solution process details

structure

Solution process details, returned as a structure with the following fields.

Field | Meaning |
---|---|

`funcCount` | Number of function evaluations. |

`localSolverIncomplete` | Number of local solver runs with `0`
exit flag. |

`localSolverNoSolution` | Number of local solver runs with negative exit flag. |

`localSolverSuccess` | Number of local solver runs with positive exit flag. |

`localSolverTotal` | Total number of local solver runs. |

`message` | Exit message. |

`solutions`

— Distinct local solutions

vector of `GlobalOptimSolution`

objects

Distinct local solutions, returned as a vector of `GlobalOptimSolution`

objects.

## Version History

**Introduced in R2010a**

## MATLAB 명령

다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.

명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.

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