MATLAB: Fmincon and exitflag = -2 or 2 / non respect of constrained conditions

exitflagfminconinequality constraintsMATLABnon respect

Hello everyone,

I'm creating a model on Matlab for optimization with inequality constraints, especially the minimization of a function with several variables (at least 4). I'm trying to run the optimization for many times to get a list of solutions, gotten by a parameter varying at each simulation.

However, I cannot solve problems I met: – exitflag = -2 most of the time and 2 for the other simulations. I don't know where I can correct or change to get exitflag = 1. – among constrained conditions I set, some of them aren't respected after minimizations.

If someone could advice me what method I could follow?

Thank you for the answer.

General description of the model: *—— objective : minimize a section ( I shape) submitted to a tension force *—— variables : thicknesses, height, wide *——- constraints : – section > 0 – variables >0 + linear inequality relation between two of them – tension stress < certain value in MPa : non respected condition

Best Answer

getting exit message 2 is not bad but if you insist on having 1, you could try to use smaller values for TolX (ie.. 1e-10 or even 1e-14)

as for exit message -2: there could be many causes.

if you choose a feasible starting point, fmincon should be able to find a solution i guess...

but maybe you could also post the output of the last couple of iterations and the exit message? and the fmincon command + options? otherwise it is very hard to guess what's going on

Related Solutions

MATLAB: Error for ‘fmincon’ for global optimization

It's a case sensitivity issue. Instead of 'LB' and 'UB', pass in 'lb' and 'ub'.

MATLAB: Optimization toolbox: “lsqnonlin” stopped because it exceeded the function evaluation limit

Note that I don't know which release of MATLAB you are using. But what did you try? This seems clear:

opts = optimoptions('lsqnonlin')
opts = 
  lsqnonlin options:
   Options used by current Algorithm ('trust-region-reflective'):
   (Other available algorithms: 'levenberg-marquardt')
   Set properties:
     No options set.
   Default properties:
                   Algorithm: 'trust-region-reflective'
              CheckGradients: 0
                     Display: 'final'
    FiniteDifferenceStepSize: 'sqrt(eps)'
        FiniteDifferenceType: 'forward'
           FunctionTolerance: 1e-06
         JacobianMultiplyFcn: []
      MaxFunctionEvaluations: '100*numberOfVariables'
               MaxIterations: 400
         OptimalityTolerance: 1e-06
                   OutputFcn: []
                     PlotFcn: []
    SpecifyObjectiveGradient: 0
               StepTolerance: 1e-06
         SubproblemAlgorithm: 'factorization'
                    TypicalX: 'ones(numberOfVariables,1)'
                 UseParallel: 0

Now, nothing stops you from setting:

opts.MaxFunctionEvaluations = 2000
opts = 
  lsqnonlin options:
   Options used by current Algorithm ('trust-region-reflective'):
   (Other available algorithms: 'levenberg-marquardt')
   Set properties:
      MaxFunctionEvaluations: 2000
   Default properties:
                   Algorithm: 'trust-region-reflective'
              CheckGradients: 0
                     Display: 'final'
    FiniteDifferenceStepSize: 'sqrt(eps)'
        FiniteDifferenceType: 'forward'
           FunctionTolerance: 1e-06
         JacobianMultiplyFcn: []
               MaxIterations: 400
         OptimalityTolerance: 1e-06
                   OutputFcn: []
                     PlotFcn: []
    SpecifyObjectiveGradient: 0
               StepTolerance: 1e-06
         SubproblemAlgorithm: 'factorization'
                    TypicalX: 'ones(numberOfVariables,1)'
                 UseParallel: 0

As you can see, opts now contains the desired value. Pass opts into lsqnonlin to use these settings.

As Walter points out, in older releases, the property name was MaxFunEvals. But the same sort of thing will still apply. You may just need to use the proper property name.

Having said that, frequently when you run out of function evals or iterations, it means your problem is not converging very well. Tht in turn either means that your starting values are particularly poor, or that your problem is fairly ill-posed. In either case, merely increasing the function evals will not really help that much. Essentially, it says that you really need to get better data or a better model or better starting values, each a far better choice than merely increasing the function evals allowed.

Best Answer

Related Solutions

MATLAB: Error for ‘fmincon’ for global optimization

MATLAB: Optimization toolbox: “lsqnonlin” stopped because it exceeded the function evaluation limit

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