optimr | R Documentation |

General-purpose optimization wrapper function that calls other
R tools for optimization, including the existing optim() function.
`optimr`

also tries to unify the calling sequence to allow
a number of tools to use the same front-end, in fact using the
calling sequence of the R function `optim()`

.

optimr(par, fn, gr=NULL, hess=NULL, lower=-Inf, upper=Inf, method=NULL, hessian=FALSE, control=list(), ...)

`par` |
a vector of initial values for the parameters for which optimal values are to be found. Names on the elements of this vector are preserved and used in the results data frame. |

`fn` |
A function to be minimized (or maximized), with first argument the vector of parameters over which minimization is to take place. It should return a scalar result. |

`gr` |
A function to return (as a vector) the gradient for those methods that can use this information. If 'gr' is If 'gr' is a character string, then that string is taken as the name of
a gradient approximation function, for example, "grfwd", "grback" and
"grcentral" for standard forward, backward and central approximations.
Method "grnd"
uses the |

`hess` |
A function to return (as a matrix) the hessian for those methods that can use this information. |

`lower, upper` |
Bounds on the variables for methods such as |

`method` |
A character string giving the name of the optimization method to be
applied. See the list |

`hessian` |
A logical control that if TRUE forces the computation of an approximation
to the Hessian at the final set of parameters. Note that this will NOT necessarily
use the same approximation as may be provided by the method called. Instead,
the function |

`control` |
A list of control parameters. See ‘Details’. |

`...` |
Further arguments to be passed to |

Note that arguments after `...`

should be matched exactly.

By default this function performs minimization, but it will maximize
if `control$maximize`

is TRUE. The original optim() function allows
`control$fnscale`

to be set negative to accomplish this. DO NOT
use both mechanisms simultaneously.

Possible method choices are specified by the list `allmeth`

in the file
`ctrldefault.R`

which is part of this package. Fewer methods are available in
packge `optimr`

on CRAN than package `optimrx`

which is NOT on CRAN to
avoid issues if packages on which function `optimr()`

is dependent become
unavailable.

If no method is specified, the method specified by `defmethod`

in file
`ctrldefault.R`

(which is part of this package) will be attempted.

Function `fn`

must return a finite scalar value at the initial set
of parameters. Some methods can handle `NA`

or `Inf`

if the function cannot be evaluated at the supplied value. However, some methods, of
which `"L-BFGS-B"`

is known to be a case, require that the values
returned should always be finite.

While methods from the base R function `optim()`

can be used recursively,
and for a single parameter
as well as many, this may not be true for other methods in `optimrx`

.
`optim`

also accepts a zero-length `par`

, and just evaluates the function
with that argument.

Generally, you are on your own if you choose to apply constructs mentioned in the above two paragraphs.

For details of methods, please consult the documentation of the individual methods.
(The NAMESPACE file lists the packages from which functions are imported.)
However, method `"hjn"`

is a conservative implementation of a Hooke and
Jeeves (1961) and is part of this package. It is provided as a simple example of
a very crude optimization method; it is NOT intended as a production method, but
may be useful for didactic purposes.

The `control`

argument is a list that can supply any of the
components in the file `ctrldefault.R`

which is part of this
package. It may supply others that are
useful or required for particular methods, but users are warned to be careful to
ensure that extraneous or incorrect components and values are not passed.

Note that some `control`

elements apply only to some of methods.
See individual packages for details.

Any names given to `par`

will be copied to the vectors passed to
`fn`

and `gr`

. Apparently no other attributes of `par`

are copied over, but this may need to be verified, especially if parameters
are passed to non-R routines.

CAUTION: because there is a seldom-used parameter `hess`

, you should NOT
make a call like

ans <- optimr(start, myf, myg, lower, upper)

or you will likely get wrong results. Instead use

ans <- optimr(start, myf, myg, lower=lower, upper=upper)

A list with components:

- par
The best set of parameters found.

- value
The value of ‘fn’ corresponding to ‘par’.

- counts
A two-element integer vector giving the number of calls to ‘fn’ and ‘gr’ respectively. This excludes those calls needed to compute the Hessian, if requested, and any calls to ‘fn’ to compute a finite-difference approximation to the gradient.

- convergence
An integer code. ‘0’ indicates successful completion. The documentation for function

`opm()`

gives some other possible values and their meaning.- message
A character string giving any additional information returned by the optimizer, or ‘NULL’.

- hessian
If requested, an approximation to the hessian of ‘fn’ at the final parameters.

See the manual pages for `optim()`

.

Hooke R. and Jeeves, TA (1961). Direct search solution of numerical and statistical problems. Journal of the Association for Computing Machinery (ACM). 8 (2): 212–229.

Nash JC, and Varadhan R (2011). Unifying Optimization Algorithms to Aid Software System Users:
**optimx** for R., *Journal of Statistical Software*, 43(9), 1-14.,
URL http://www.jstatsoft.org/v43/i09/.

Nocedal J, and Wright SJ (1999). Numerical optimization. New York: Springer. 2nd Edition 2006.

# Simple Test Function 1: tryfun.f = function(x) { fun <- sum(x^2 ) ## if (trace) ... to be fixed print(c(x = x, fun = fun)) fun } tryfun.g = function(x) { grad<-2.0*x grad } tryfun.h = function(x) { n<-length(x) t<-rep(2.0,n) hess<-diag(t) } strt <- c(1,2,3) ansfgh <- optimr(strt, tryfun.f, tryfun.g, tryfun.h, method="nlm", hessian=TRUE, control=list(trace=2)) proptimr(ansfgh) # compact output of result

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