optimr: General-purpose optimization

View source: R/optimr.R

optimrR Documentation

General-purpose optimization


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,



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.


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.


A function to return (as a vector) the gradient for those methods that can use this information.

If 'gr' is NULL, whatever default action is specified for the chosen method for the case of a null gradient code is used. For many methods, this is a finite-difference approximation, but some methods require user input for the gradient and will fail otherwise. In such cases, we try to return convergence of 9998.

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 grad() function from package numDeriv.


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 "L-BFGS-B" that can handle box (or bounds) constraints. A small set of methods can handle masks, that is, fixed parameters, and these can be specified by making the lower and upper bounds equal to the starting value. (It is possible that the starting value could be different from the lower/upper bounds set, but this behaviour has NOT yet been defined and users are cautioned.)


A character string giving the name of the optimization method to be applied. See the list allmeth in file ctrldefault.R which is part of this package.


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 hessian() from package numDeriv is used if no gradient gr is supplied, else the function jacobian() from numDeriv is applied to the gradient function gr.


A list of control parameters. See ‘Details’.


Further arguments to be passed to fn and gr if needed for computation of these quantities; otherwise, further arguments are not used.


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:


The best set of parameters found.


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


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.


An integer code. ‘0’ indicates successful completion. The documentation for function opm() gives some other possible values and their meaning.


A character string giving any additional information returned by the optimizer, or ‘NULL’.


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))
tryfun.g = function(x) {
tryfun.h = function(x) {

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

optimx documentation built on May 11, 2022, 1:08 a.m.