BFGS_special: An implementation of BFGS method for posterior maximization.

Description Usage Arguments Details Value Author(s) Examples

View source: R/BFGS_special.R

Description

Gradients are computed using finite differences.

Usage

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BFGS_special(init, knobj, fun_like, verbose = FALSE)

Arguments

init

An initial value of the parameter to be optimized.

knobj

A knowledge list. See knobjs.

fun_like

A function to compute posterior value. See eval_log_like_knobj

verbose

Print progresses of the local search?

Details

The step size are chosen using Armijo's rule. Special checks are performed to avoid numerical instabilities in the differential equation solver.

Value

A list with the following entries:

theta

The local optimum found by the method.

fail

A boolean representing wither the local search failed or not due to numerical problems.

Author(s)

Edouard Pauwels

Examples

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data(experiment_list1)
data(observables)

## Generate the knowledge object with correct parameter value
knobj <- generate_our_knowledge(transform_params)

## Initialize with some data
knobj$datas[[1]] <- list(
 manip = experiment_list1$nothing,
 data = add_noise(
  simulate_experiment(knobj$global_parameters$true_params_T, knobj, experiment_list1$nothing)[
   knobj$global_parameters$tspan %in% observables[["mrnaLow"]]$reso, 
   observables[["mrnaLow"]]$obs
  ]
 )
)
knobj$experiments <- paste("nothing", "mrnaLow")

theta <- rep( 50, length(knobj$global_parameters$param_names) )
names(theta) <- knobj$global_parameters$param_names

## Only perform 5 iterations
knobj$global_parameters$max_it <- 5

temp <- BFGS_special(theta, knobj, eval_log_like_knobj)
temp$theta

pauwels2014 documentation built on May 29, 2017, 9:03 a.m.