BFGS_special: An implementation of BFGS method for posterior maximization.
In pauwels2014: Bayesian Experimental Design for Systems Biology.

Description Usage Arguments Details Value Author(s) Examples

Gradients are computed using finite differences.

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

`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?

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

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.

Edouard Pauwels

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