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All frontends for optimization (nonlin_min, nonlin_residmin, nonlin_curvefit) accept the following options, settable with (octave)optimset.
Algorithm
String specifying the backend.
complex_step_derivative_inequc,
complex_step_derivative_equc
Logical scalars, default: false
. Estimate Jacobian of general
inequality constraints and equality constraints, respectively, with
complex step derivative approximation. Use only if you know that your
function of general inequality constraints or function of general
equality constraints, respectively, is suitable for this. No user
function for the respective Jacobian must be specified.
Which of these options are actually honored is noted in the descriptions of the individual backends.
lbound,
ubound
Column vectors of lower and upper bounds for parameters. Default:
-Inf
and +Inf
, respectively. The bounds are non-strict,
i.e. parameters are allowed to be exactly equal to a bound. The default
function for gradients or Jacobians will respect bounds (but no further
inequality constraints) in finite differencing if the backend respects
bounds even during the course of optimization.
inequc
Further inequality constraints. Cell-array containing up to four
entries, two entries for linear inequality constraints and/or one or two
entries for general inequality constraints. Either linear or general
constraints may be the first entries, but the two entries for linear
constraints must be adjacent and, if two entries are given for general
constraints, they also must be adjacent. The two entries for linear
constraints are a matrix (say m
) and a vector (say v
),
specifying linear inequality constraints of the form m.' *
parameters + v >= 0
. The first entry for general constraints must be a
differentiable column-vector valued function (say h
), specifying
general inequality constraints of the form h (p[, idx]) >= 0
;
p
is the column vector of optimized paraters and the optional
argument idx
is a logical index. h
has to return the
values of all constraints if idx
is not given. It may choose to
return only the indexed constraints if idx
is given (so
computation of the other constraints can be spared); in this case, the
additional setting f_inequc_idx
has to be set to true
. In
gradient determination, this function may be called with an
informational third argument, whose content depends on the function for
gradient determination. If a second entry for general inequality
constraints is given, it must be a function computing the jacobian of
the constraints with respect to the parameters. For this function, the
description of the setting dfdp
,
see
dfdp,
applies, with 2 exceptions: 1) it is called with 3 arguments since it
has an additional argument idx
, a logical index, at second
position, indicating which rows of the jacobian must be returned (if the
function chooses to return only indexed rows, the additional setting
df_inequc_idx
has to be set to true
). 2) the default
jacobian function calls h
with 3 arguments, since the argument
idx
is also supplied. Note that specifying linear constraints as
general constraints will generally waste performance, even if further,
non-linear, general constraints are also specified.
f_inequc_idx,
df_inequc_idx
Indicate that functions for general inequality constraints or their
jacobian, respectively, return only the values or derivatives for the
indexed parameters. See description of setting inequc
above.
equc
Equality constraints. Specified the same way as inequality constraints
(see inequc
above).
f_equc_idx,
df_equc_idx
As f_inequc_idx
and df_inequc_idx
above, but for equality
constraints.
cpiv
Function for complementary pivoting, usable in algorithms for
constraints. Default: cpiv_bard
. Only the default function is
supplied with the package.
TolFun
Minimum fractional improvement in objective function (e.g. sum of squares) in an iteration (termination criterium). Default: .0001.
TolX
Minimum fractional change in a norm of the parameters in an iteration (termination criterium). Default: backend specific.
MaxIter
Maximum number of iterations (termination criterium). Default: backend-specific.
fract_prec
Column Vector, minimum fractional changes of corresponding parameters in an iteration (termination criterium if violated in two consecutive iterations). Default: backend-specific.
max_fract_change
Column Vector, enforced maximum fractional changes in corresponding parameters in an iteration. Default: backend-specific.
Display
String indicating the degree of verbosity. Default:
"off"
. Possible values are currently "off"
(no messages)
and "iter"
(some messages after each iteration). Support of
this setting and its exact interpretation are backend-specific.
debug
Logical scalar, default: false
. Will be passed to the backend,
which might print debugging information if true
.
FunValCheck
If "on"
, the output of user functions will be sanity-checked.
Default: "off"
.
user_interaction
Handle to a user function or cell-array with a number of these. Functions must have this interface:
[stop, info] = some_user_function (p, vals, state);
If stop is true
, the algorithm stops. In info
information about the reason for stopping can be returned in a free
format. info can be set to be empty, but it must be set. Note
that this is different from the otherwise similar Matlab setting
OutputFcn
. The functions will be called by the algorithms at the
start with state set to init
, after each iteration with
state set to iter
, and at the end with state set to
done
. p contains the current parameters, and vals
is a structure with other current values, the possible fields are
currently:
iteration
number of the current iteration,
fval
value of objective function (for scalar optimization),
residual
residuals (for residual-based optimization),
model_y
in nonlin_curvefit
, the output of the model function,
observations
in nonlin_curvefit
, the constant observations,
model_x
in nonlin_curvefit
, the constant argument x.
Information about the output of these functions when they were called
the last time (possibly causing a stop) will be contained in the output
outp of the frontend in field user_interaction
. Subfield
stop
is a vector containing the stop outputs of each
function, subfield info
is a cell-array containing the output
info of each function. In the case of a stop, the output
cvg of the frontent will be -1
.
Next: Parameter structures, Previous: Common frontend options, Up: Top [Index]