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8 Options common to all frontends

All frontends for optimization and for result statistics (nonlin_min, nonlin_residmin, nonlin_curvefit, residmin_stat, curvefit_stat)accept the following options, settable with (octave)optimset.

These options are handled within the frontend.

FinDiffRelStep

Column vector (or scalar, for all parameters) of fractional intervals supposed to be used by gradient or Jacobian functions performing finite differencing. Default: .002 * ones (size (parameters)) for central intervals and .001 * ones (size (parameters)) for one-sided intervals. The default function for finite differencing won’t let the absolute interval width get smaller than abs (FinDiffRelStep .* TypicalX (see below).

diffp

Can be used alternatively to FinDiffRelStep, but for central intervals twice the specified value will be used for backwards compatibility.

diff_onesided

Logical column vector (or scalar, for all parameters) indicating the parameters for which one-sided intervals (instead of central intervals) should be used by gradient or Jacobian functions performing finite differencing. Default: false (size (parameters)).

FinDiffType

Can be used alternatively to diff_onesided, but always applies to all parameters at once. Possible values: "central" (central intervals) or "forward" (one-sided intervals).

TypicalX

Column vector (or scalar, for all parameters) whose absolute value specifies minimal absolute parameter values for computation of intervals in finite differencing by gradient or Jacobian functions (see FinDiffRelStep). Default: 0.0001. Must not be zero.

cstep

Scalar step size for complex step derivative approximation of gradients or Jacobians. Default: 1e-20.

parallel_local

Logical or numeric scalar, default: false. If the parallel package, version >= 2.0.5, is loaded, estimate gradients of objective function and Jacobians of model function and of constraints in parallel processes. If parallel_local is set to an integer > 1, this is number of parallel processes; if it is <= 1, the number of processes will be the number of available processor cores. Works for default (real) finite differences and for complex step derivatives. Due to overhead, a speed advantage can only be expected if objective function, model function or constraint functions are time consuming enough. Additionally, this setting is also passed to the individual optimization backends, which may also consider this option (see documentation of backends). If this option is equivalent to true, a warning (ID: optim:parallel_local) will be issued if no parallel package of a correct version is loaded.

parallel_net

Empty (default) or a parallel connections object, see function pconnect of the parallel package. If not empty, estimate gradients of objective function and Jacobians of model function and of constraints using parallel processing in a network of machines. The considerations regarding a speed advantage are similar to those for option parallel_local.

fixed

Logical column vector indicating which parameters are not optimized, but kept to their inital value.

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