MATLAB OPTIMIZATION TOOLBOX - RELEASE NOTES Manuale Utente Scaricare il pdf (Pagina 43)

9-3

Enhanced Robustness in Nonlinear Solvers

More solvers now attempt to recover from errors in the evaluation of objective functions

and nonlinear constraint functions during iteration steps, or, for some algorithms, during

gradient estimation. The errors include results that are NaN or Inf for all solvers, or

complex for fmincon and fminunc. If there is such an error, the algorithms attempt to

take different steps. The following solvers are enhanced:

• fmincon trust-region-reflective algorithm (the interior-point and sqp

algorithms already had this robustness)

• fminunc LargeScale algorithm

• fsolve trust-region-reflective, trust-region-dogleg, and levenberg-

marquardt algorithms

• lsqcurvefit trust-region-reflective and levenberg-marquardt algorithms

• lsqnonlin trust-region-reflective and levenberg-marquardt algorithms

New Defaults in DiffMinChange and DiffMaxChange Options

The DiffMinChange and DiffMaxChange options set the minimum and maximum

possible step sizes for finite differences in gradient estimation. The defaults are now:

• DiffMinChange = 0 (formerly 1e-8)

• DiffMaxChange = Inf (formerly 0.1)

Solvers have mechanisms that ensure nonzero and non-infinite step sizes, so the new

defaults simply mean that the step size adjustment algorithms have fewer constraints.

The new defaults remove the previous arbitrary choices. The previous values can be

inappropriate when components are too large or small in magnitude. Tests show these

new defaults are good for most situations.

Compatibility Considerations

Some solver iterations can differ from previous ones. To obtain the previous behavior:

options = optimset('DiffMinChange',1e-8,'DiffMaxChange',0.1);

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MATLAB OPTIMIZATION TOOLBOX - RELEASE NOTES Manuale Utente Pagina 43