optimInterface: Optimization Interface (continuous, bounded)
In CEGO: Combinatorial Efficient Global Optimization

optimInterface

R Documentation

Optimization Interface (continuous, bounded)

Description

This function is an interface fashioned like the optim function. Unlike optim, it collects a set of bound-constrained optimization algorithms with local as well as global approaches. It is, e.g., used in the CEGO package to solve the optimization problem that occurs during parameter estimation in the Kriging model (based on Maximum Likelihood Estimation). Note that this function is NOT applicable to combinatorial optimization problems.

Usage

optimInterface(x, fun, lower = -Inf, upper = Inf, control = list(), ...)

Arguments

`x`	is a point (vector) in the decision space of `fun`
`fun`	is the target function of type `y = f(x, ...)`
`lower`	is a vector that defines the lower boundary of search space
`upper`	is a vector that defines the upper boundary of search space
`control`	is a list of additional settings. See details.
`...`	additional parameters to be passed on to `fun`

Details

The control list contains:

funEvals: stopping criterion, number of evaluations allowed for fun (defaults to 100)
reltol: stopping criterion, relative tolerance (default: 1e-6)
factr: stopping criterion, specifying relative tolerance parameter factr for the L-BFGS-B method in the optim function (default: 1e10)
popsize: population size or number of particles (default: 10*dimension, where dimension is derived from the length of the vector lower).
restarts: whether to perform restarts (Default: TRUE). Restarts will only be performed if some of the evaluation budget is left once the algorithm stopped due to some stopping criterion (e.g., reltol).
method: will be used to choose the optimization method from the following list: "L-BFGS-B" - BFGS quasi-Newton: stats Package optim function
"nlminb" - box-constrained optimization using PORT routines: stats Package nlminb function
"DEoptim" - Differential Evolution implementation: DEoptim Package
Additionally to the above methods, several methods from the package nloptr can be chosen. The complete list of suitable nlopt methods (non-gradient, bound constraints) is:
"NLOPT_GN_DIRECT","NLOPT_GN_DIRECT_L","NLOPT_GN_DIRECT_L_RAND", "NLOPT_GN_DIRECT_NOSCAL","NLOPT_GN_DIRECT_L_NOSCAL","NLOPT_GN_DIRECT_L_RAND_NOSCAL", "NLOPT_GN_ORIG_DIRECT","NLOPT_GN_ORIG_DIRECT_L","NLOPT_LN_PRAXIS", "NLOPT_GN_CRS2_LM","NLOPT_LN_COBYLA", "NLOPT_LN_NELDERMEAD","NLOPT_LN_SBPLX","NLOPT_LN_BOBYQA","NLOPT_GN_ISRES"

All of the above methods use bound constraints. For references and details on the specific methods, please check the documentation of the packages that provide them.