multistart | R Documentation |

Multiple initial parameter wrapper function that calls other R tools for optimization, including the existing optimr() function.

multistart(parmat, fn, gr=NULL, lower=-Inf, upper=Inf, method=NULL, hessian=FALSE, control=list(), ...)

`parmat` |
a matrix of which each row is a set 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 |

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

`method` |
A list of the methods to be used. Note that this is an important change from optim() that allows just one method to be specified. See ‘Details’. The default of NULL causes an appropriate set of methods to be supplied depending on the presence or absence of bounds on the parameters. The default unconstrained set is Rvmminu, Rcgminu, lbfgsb3, newuoa and nmkb. The default bounds constrained set is Rvmminb, Rcgminb, lbfgsb3, bobyqa and nmkb. |

`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 |

Note that arguments after `...`

must be matched exactly.

See `optimr()`

for other details.

An array with one row per set of starting parameters. Each row contains:

`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 |

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

`hessian` |
Always NULL for this routine. |

See the manual pages for `optim()`

and the packages the DESCRIPTION `suggests`

.

fnR <- function (x, gs=100.0) { n <- length(x) x1 <- x[2:n] x2 <- x[1:(n - 1)] sum(gs * (x1 - x2^2)^2 + (1 - x2)^2) } grR <- function (x, gs=100.0) { n <- length(x) g <- rep(NA, n) g[1] <- 2 * (x[1] - 1) + 4*gs * x[1] * (x[1]^2 - x[2]) if (n > 2) { ii <- 2:(n - 1) g[ii] <- 2 * (x[ii] - 1) + 4 * gs * x[ii] * (x[ii]^2 - x[ii + 1]) + 2 * gs * (x[ii] - x[ii - 1]^2) } g[n] <- 2 * gs * (x[n] - x[n - 1]^2) g } pm <- rbind(rep(1,4), rep(pi, 4), rep(-2,4), rep(0,4), rep(20,4)) pm <- as.matrix(pm) cat("multistart matrix:\n") print(pm) ans <- multistart(pm, fnR, grR, method="Rvmmin", control=list(trace=0)) ans

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