snomadr {crs}

## Not run:
## List all options
snomadr(information=list("help"="-h"))
 
## Print given option,  for example,  MESH_SIZE
snomadr(information=list("help"="-h MESH_SIZE"))
 
## Print the version of NOMAD
snomadr(information=list("version"="-v"))
 
## Print usage and copyright
snomadr(information=list("info"="-i"))
 
## This is the example found in
## NOMAD/examples/basic/library/single_obj/basic_lib.cpp
 
eval.f <- function ( x ) {
 
    f <- c(Inf, Inf, Inf);
    n <- length (x);
 
    if ( n == 5 && ( is.double(x) || is.integer(x) ) ) {
        f[1] <- x[5];
        f[2] <- sum ( (x-1)^2 ) - 25;
        f[3] <- 25 - sum ( (x+1)^2 );
    }  
 
    return ( as.double(f) );
}
 
## Initial values
x0 <- rep(0.0, 5 )
 
bbin <-c(1, 1, 1, 1, 1)
## Bounds
lb <- rep(-6.0,5 )
ub <- c(5.0, 6.0, 7.0, 1000000, 100000)
 
bbout <-c(0, 2, 1)
## Options
opts <-list("MAX_BB_EVAL"=500,
            "MIN_MESH_SIZE"=0.001,
            "INITIAL_MESH_SIZE"=0.1,
            "MIN_POLL_SIZE"=0.0001)
 
snomadr(eval.f=eval.f,n=5,  x0=x0, bbin=bbin, bbout=bbout, lb=lb, ub=ub, opts=opts)
 
 
## How to transfer other parameters into eval.f
##
## First example: supply additional arguments in user-defined functions
##
 
## objective function and gradient in terms of parameters
eval.f.ex1 <- function(x, params) { 
    return( params[1]*x^2 + params[2]*x + params[3] ) 
}
 
## Define parameters that we want to use
params <- c(1,2,3)
 
## Define initial value of the optimization problem
x0 <- 0
 
## solve using snomadr 
snomadr( n          =1, 
        x0          = x0, 
        eval.f      = eval.f.ex1, 
        params      = params )
 
 
##
## Second example: define an environment that contains extra parameters
##
 
## Objective function and gradient in terms of parameters
## without supplying params as an argument
eval.f.ex2 <- function(x) { 
    return( params[1]*x^2 + params[2]*x + params[3] ) 
}
 
## Define initial value of the optimization problem
x0 <- 0
 
## Define a new environment that contains params
auxdata        <- new.env()
auxdata$params <- c(1,2,3)
 
## pass The environment that should be used to evaluate functions to snomadr 
snomadr(n                  =1, 
        x0                 = x0, 
        eval.f             = eval.f.ex2, 
        snomadr.environment = auxdata )
 
## Solve using algebra
cat( paste( "Minimizing f(x) = ax^2 + bx + c
" ) )
cat( paste( "Optimal value of control is -b/(2a) = ", -params[2]/(2*params[1]), "
" ) )
cat( paste( "With value of the objective function f(-b/(2a)) = ", eval.f.ex1( -params[2]/(2*params[1]), params ), "
" ) )
 
 
## The following example is NOMAD/examples/advanced/multi_start/multi.cpp
## This will call smultinomadRSolve to resolve the problem.  
eval.f.ex1 <- function(x, params) { 
    M<-as.numeric(params$M)
    NBC<-as.numeric(params$NBC)
 
    f<-rep(0, M+1)
    x<-as.numeric(x)
 
    x1 <- rep(0.0, NBC)
    y1 <- rep(0.0, NBC)
 
    x1[1]<-x[1]
    x1[2]<-x[2]
    y1[3]<-x[3]
    x1[4]<-x[4]
    y1[4]<-x[5]
 
    epi <- 6
    for(i in 5:NBC){
        x1[i]<-x[epi]
        epi <- epi+1
        y1[i]<-x[epi]
        epi<-epi+1
    }
    constraint <- 0.0
    ic <- 1
    f[ic]<-constraint
    ic <- ic+1
 
    constraint <- as.numeric(1.0)
    distmax <- as.numeric(0.0)
    avg_dist <- as.numeric(0.0)
    dist1<-as.numeric(0.0)
 
    for(i in 1:(NBC-1)){
        for (j in (i+1):NBC){
            dist1 <- as.numeric((x1[i]-x1[j])*(x1[i]-x1[j])+(y1[i]-y1[j])*(y1[i]-y1[j]))
 
            if((dist1 > distmax)) {distmax <- as.numeric(dist1)}
            if((dist1[1]) < 1) {constraint <- constraint*sqrt(dist1)}
            else if((dist1) > 14) {avg_dist <- avg_dist+sqrt(dist1)}
        }
    }
 
    if(constraint < 0.9999) constraint <- 1001.0-constraint
    else constraint = sqrt(distmax)+avg_dist/(10.0*NBC)
 
    f[2] <- 0.0
    f[M+1] <- constraint 
 
 
    return(as.numeric(f) ) 
}
 
## Define parameters that we want to use
params<-list()
NBC <- 5
M <- 2
n<-2*NBC-3
 
params$NBC<-NBC
params$M<-M
x0<-rep(0.1, n)
lb<-rep(0, n)
ub<-rep(4.5, n)
 
eval.f.ex1(x0, params)
 
bbout<-c(2, 2, 0)
nmulti=5
bbin<-rep(0, n)
## Define initial value of the optimization problem
 
## Solve using snomadRSolve
snomadr(n            = as.integer(n), 
        x0           = x0, 
        eval.f       = eval.f.ex1, 
        bbin         = bbin, 
        bbout        = bbout, 
        lb           = lb, 
        ub           = ub, 
        params       = params )
 
## Solve using smultinomadRSolve, if x0 is provided,  x0 will
## be the first initial point,  otherwise,  the program will
## check best_x.txt,  if it exists,  it will be read in as
## the first initial point. Other initial points will be
## generated by uniform distribution.
## nmulti represents the number of mads will run.
##
snomadr(n            = as.integer(n), 
        eval.f       = eval.f.ex1, 
        bbin         = bbin, 
        bbout        = bbout, 
        lb           = lb, 
        ub           = ub, 
        nmulti = as.integer(nmulti), 
        print.output = TRUE, 
        params       = params )
## End(Not run)