# General-purpose optimization

### Description

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

Because SANN does not return a meaningful convergence code
(conv), `opm()`

does not call the SANN method, but it can be invoked
in `optimr()`

.

There is a pseudo-method "ALL" that runs all available methods. Note that this is upper-case.

### Usage

1 2 3 4 |

### Arguments

`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 a 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, this character string will be taken to be the name
of an available gradient approximation function. Examples are "grfwd", "grback",
"grcentral" and "grnd", with the last name referring to the default method of
package |

`hess` |
A function to return (as a symmetric matrix) the Hessian of the objective
function for those methods that can use this information. At the time of writing,
we know of none in the methods which can be called by |

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

`method` |
A vector of the methods to be used, each as a character string.
Note that this is an important change from optim() that allows
just one method to be specified. See ‘Details’. If |

`hessian` |
A logical control that if TRUE forces the computation of an approximation
to the Hessian at the final set of parameters. If FALSE (default), the hessian is
calculated if needed to provide the KKT optimality tests (see |

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

`...` |
For |

### Details

Note that arguments after `...`

must be matched exactly.

For details of how `opm()`

calls the methods, see the documentation
and code for `optimr()`

. The documentation and code for individual
methods may also be useful. Note that some simplification of the calls
may have been necessary, for example, to provide reasonable default values
for method controls.

The `control`

argument is a list that can supply any of the
following components:

`trace`

Non-negative integer. If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method

`"L-BFGS-B"`

there are six levels of tracing. trace = 0 gives no output (To understand exactly what these do see the source code: higher levels give more detail.)`fnscale`

An overall scaling to be applied to the value of

`fn`

and`gr`

during optimization. If negative, turns the problem into a maximization problem. Optimization is performed on`fn(par)/fnscale`

. For methods from the set in`optim()`

. Note potential conflicts with the control`maximize`

.`parscale`

A vector of scaling values for the parameters. Optimization is performed on

`par/parscale`

and these should be comparable in the sense that a unit change in any element produces about a unit change in the scaled value.For`optim`

.`save.failures`

= TRUE (default) if we wish to keep "answers" from runs where the method does not return convcode==0. FALSE otherwise.

`maximize`

= TRUE if we want to maximize rather than minimize a function. (Default FALSE). Methods nlm, nlminb, ucminf cannot maximize a function, so the user must explicitly minimize and carry out the adjustment externally. However, there is a check to avoid usage of these codes when maximize is TRUE. See

`fnscale`

below for the method used in`optim`

that we deprecate.`all.methods`

= TRUE if we want to use all available (and suitable) methods. This is equivalent to setting

`method="ALL"`

`kkt`

=FALSE if we do NOT want to test the Kuhn, Karush, Tucker optimality conditions. The default is generally TRUE. However, because the Hessian computation may be very slow, we set

`kkt`

to be FALSE if there are more than than 50 parameters when the gradient function`gr`

is not provided, and more than 500 parameters when such a function is specified. We return logical values`KKT1`

and`KKT2`

TRUE if first and second order conditions are satisfied approximately. Note, however, that the tests are sensitive to scaling, and users may need to perform additional verification. If`hessian`

is TRUE, this overrides control`kkt`

.`all.methods`

= TRUE if we want to use all available (and suitable) methods.

`kkttol`

= value to use to check for small gradient and negative Hessian eigenvalues. Default = .Machine$double.eps^(1/3)

`kkt2tol`

= Tolerance for eigenvalue ratio in KKT test of positive definite Hessian. Default same as for kkttol

`dowarn`

= FALSE if we want to suppress warnings generated by

`opm()`

or`optimr()`

. Default is TRUE.`badval`

= The value to set for the function value when try(fn()) fails. Default is (0.5)*.Machine$double.xmax

There may be `control`

elements that apply only to some of the methods. Using these
may or may not "work" with `opm()`

, and errors may occur with methods for which
the controls have no meaning.
However, it should be possible to call the underlying `optimr()`

function with
these method-specific controls.

Any names given to `par`

will be copied to the vectors passed to
`fn`

and `gr`

. Note that no other attributes of `par`

are copied over. (We have not verified this as at 2009-07-29.)

### Value

If there are `npar`

parameters, then the result is a dataframe having one row
for each method for which results are reported, using the method as the row name,
with columns

`par_1, .., par_npar, value, fevals, gevals, niter, convcode, kkt1, kkt2, xtimes`

where

- par_1
..

- par_npar
The best set of parameters found.

- value
The value of

`fn`

corresponding to`par`

.- fevals
The number of calls to

`fn`

.- gevals
The number of calls to

`gr`

. 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.- niter
For those methods where it is reported, the number of “iterations”. See the documentation or code for particular methods for the meaning of such counts.

- convcode
An integer code.

`0`

indicates successful convergence. Various methods may or may not return sufficient information to allow all the codes to be specified. An incomplete list of codes includes`1`

indicates that the iteration limit

`maxit`

had been reached.`20`

indicates that the initial set of parameters is inadmissible, that is, that the function cannot be computed or returns an infinite, NULL, or NA value.

`21`

indicates that an intermediate set of parameters is inadmissible.

`10`

indicates degeneracy of the Nelder–Mead simplex.

`51`

indicates a warning from the

`"L-BFGS-B"`

method; see component`message`

for further details.`52`

indicates an error from the

`"L-BFGS-B"`

method; see component`message`

for further details.`9998`

indicates that the method has been called with a NULL 'gr' function, and the method requires that such a function be supplied.

`9999`

indicates the method has failed.

- kkt1
A logical value returned TRUE if the solution reported has a “small” gradient.

- kkt2
A logical value returned TRUE if the solution reported appears to have a positive-definite Hessian.

- xtimes
The reported execution time of the calculations for the particular method.

The attribute "details" to the returned answer object contains information,
if computed, on the gradient (`ngatend`

) and Hessian matrix (`nhatend`

)
at the supposed optimum, along with the eigenvalues of the Hessian (`hev`

),
as well as the `message`

, if any, returned by the computation for each `method`

,
which is included for each row of the `details`

.
If the returned object from optimx() is `ans`

, this is accessed
via the construct
`attr(ans, "details")`

This object is a matrix based on a list so that if ans is the output of optimx then attr(ans, "details")[1, ] gives the first row and attr(ans,"details")["Nelder-Mead", ] gives the Nelder-Mead row. There is one row for each method that has been successful or that has been forcibly saved by save.failures=TRUE.

There are also attributes

- maximize
to indicate we have been maximizing the objective

- npar
to provide the number of parameters, thereby facilitating easy extraction of the parameters from the results data frame

- follow.on
to indicate that the results have been computed sequentially, using the order provided by the user, with the best parameters from one method used to start the next. There is an example (

`ans9`

) in the script`ox.R`

in the demo directory of the package.

### Note

Most methods in `optimx`

will work with one-dimensional `par`

s, but such
use is NOT recommended. Use `optimize`

or other one-dimensional methods instead.

There are a series of demos available. Once the package is loaded (via `require(optimx)`

or
`library(optimx)`

, you may see available demos via

demo(package="optimx")

The demo 'brown_test' may be run with the command demo(brown_test, package="optimx")

The package source contains several functions that are not exported in the NAMESPACE. These are

`optimx.setup()`

which establishes the controls for a given run;

`optimx.check()`

which performs bounds and gradient checks on the supplied parameters and functions;

`optimx.run()`

which actually performs the optimization and post-solution computations;

`scalechk()`

which actually carries out a check on the relative scaling of the input parameters.

Knowledgeable users may take advantage of these functions if they are carrying out production calculations where the setup and checks could be run once.

### Source

See the manual pages for `optim()`

and the packages the DESCRIPTION `suggests`

.

### References

See the manual pages for `optim()`

and the packages the DESCRIPTION `suggests`

.

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/.

Nash JC (2014). On Best Practice Optimization Methods in R.,
*Journal of Statistical Software*, 60(2), 1-14.,
URL http://www.jstatsoft.org/v60/i02/.

### See Also

`spg`

, `nlm`

, `nlminb`

,
`bobyqa`

, `Rcgmin`

,
`Rvmmin`

, `ucminf`

,
`nmkb`

,
`hjkb`

.
`optimize`

for one-dimensional minimization;
`constrOptim`

or `spg`

for linearly constrained optimization.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | ```
require(graphics)
cat("Note possible demo(ox) for extended examples\n")
## Show multiple outputs of optimx using all.methods
# genrose function code
genrose.f<- function(x, gs=NULL){ # objective function
## One generalization of the Rosenbrock banana valley function (n parameters)
n <- length(x)
if(is.null(gs)) { gs=100.0 }
fval<-1.0 + sum (gs*(x[1:(n-1)]^2 - x[2:n])^2 + (x[2:n] - 1)^2)
return(fval)
}
genrose.g <- function(x, gs=NULL){
# vectorized gradient for genrose.f
# Ravi Varadhan 2009-04-03
n <- length(x)
if(is.null(gs)) { gs=100.0 }
gg <- as.vector(rep(0, n))
tn <- 2:n
tn1 <- tn - 1
z1 <- x[tn] - x[tn1]^2
z2 <- 1 - x[tn]
gg[tn] <- 2 * (gs * z1 - z2)
gg[tn1] <- gg[tn1] - 4 * gs * x[tn1] * z1
return(gg)
}
genrose.h <- function(x, gs=NULL) { ## compute Hessian
if(is.null(gs)) { gs=100.0 }
n <- length(x)
hh<-matrix(rep(0, n*n),n,n)
for (i in 2:n) {
z1<-x[i]-x[i-1]*x[i-1]
z2<-1.0-x[i]
hh[i,i]<-hh[i,i]+2.0*(gs+1.0)
hh[i-1,i-1]<-hh[i-1,i-1]-4.0*gs*z1-4.0*gs*x[i-1]*(-2.0*x[i-1])
hh[i,i-1]<-hh[i,i-1]-4.0*gs*x[i-1]
hh[i-1,i]<-hh[i-1,i]-4.0*gs*x[i-1]
}
return(hh)
}
startx<-4*seq(1:10)/3.
ans8<-opm(startx,fn=genrose.f,gr=genrose.g, hess=genrose.h,
control=list(all.methods=TRUE, save.failures=TRUE, trace=1), gs=10)
ans8
ans8[, "gevals"]
ans8["spg", ]
summary(ans8, par.select = 1:3)
summary(ans8, order = value)[1, ] # show best value
head(summary(ans8, order = value)) # best few
## head(summary(ans8, order = "value")) # best few -- alternative syntax
## order by value. Within those values the same to 3 decimals order by fevals.
## summary(ans8, order = list(round(value, 3), fevals), par.select = FALSE)
summary(ans8, order = "list(round(value, 3), fevals)", par.select = FALSE)
## summary(ans8, order = rownames, par.select = FALSE) # order by method name
summary(ans8, order = "rownames", par.select = FALSE) # same
summary(ans8, order = NULL, par.select = FALSE) # use input order
## summary(ans8, par.select = FALSE) # same
``` |