Nothing
\dontrun{
##############
# typically one will use poped_optimize
# This then calls Doptim for continuous optimization problems
##############
# RS+SG+LS optimization of sample times
# optimization with just a few iterations
# only to check that things are working
output <- poped_optimize(poped.db,opt_xt=T,
rsit=5,sgit=5,ls_step_size=5)
# RS+SG+LS optimization of sample times
# (longer run time than above but more likely to reach a maximum)
output <- poped_optimize(poped.db,opt_xt=T)
get_rse(output$fmf,output$poped.db)
plot_model_prediction(output$poped.db)
# Random search (just a few samples here)
rs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,rsit=20,
bUseRandomSearch= 1,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0)
# line search, DOSE and sample time optimization
ls.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 0,
bUseLineSearch = 1,
ls_step_size=10)
# Stochastic gradient search, DOSE and sample time optimization
sg.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 1,
bUseBFGSMinimizer = 0,
bUseLineSearch = 0,
sgit=20)
# BFGS search, DOSE and sample time optimization
bfgs.output <- poped_optimize(poped.db,opt_xt=1,opt_a=1,
bUseRandomSearch= 0,
bUseStochasticGradient = 0,
bUseBFGSMinimizer = 1,
bUseLineSearch = 0)
##############
# If you really want to you can use Doptim dirtectly
##############
dsl <- downsizing_general_design(poped.db)
poped.db$settings$optsw[2] <- 1 # sample time optimization
output <- Doptim(poped.db,dsl$ni, dsl$xt, dsl$model_switch, dsl$x, dsl$a,
dsl$bpop, dsl$d, dsl$maxxt, dsl$minxt,dsl$maxa,dsl$mina)
}
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