crs (version 0.15-33)

## Description

snomadr is an R interface to NOMAD (Nonsmooth Optimization by Mesh Adaptive Direct Search, Abramson, Audet, Couture and Le Digabel (2011)), an open source software C++ implementation of the Mesh Adaptive Direct Search (MADS, Le Digabel (2011)) algorithm designed for constrained optimization of blackbox functions.

NOMAD is designed to find (local) solutions of mathematical optimization problems of the form

   min     f(x)
x in R^ns.t.       g(x) <= 0
x_L <=  x   <= x_U


where $$f(x)\colon R^n \to R^k$$ is the objective function, and $$g(x)\colon R^n \to R^m$$ are the constraint functions. The vectors $$x_L$$ and $$x_U$$ are the bounds on the variables $$x$$. The functions $$f(x)$$ and $$g(x)$$ can be nonlinear and nonconvex. The variables can be integer, continuous real number, binary, and categorical.

## Usage

snomadr(eval.f,
n,
bbin = NULL,
bbout = NULL,
x0 = NULL,
lb = NULL,
ub = NULL,
nmulti = 0,
random.seed = 0,
opts = list(),
print.output = TRUE,
information = list(),
... )

## Arguments

eval.f

function that returns the value of the objective function

n

the number of variables

bbin

types of variables. Variable types are 0 (CONTINUOUS), 1 (INTEGER), 2 (CATEGORICAL), 3 (BINARY)

bbout

types of output of eval.f. See the NOMAD User Guide https://www.gerad.ca/nomad/Downloads/user_guide.pdf

x0

vector with starting values for the optimization. If it is provided and nmulti is bigger than 1, x0 will be the first initial point for multiple initial points

lb

vector with lower bounds of the controls (use -1.0e19 for controls without lower bound)

ub

vector with upper bounds of the controls (use 1.0e19 for controls without upper bound)

nmulti

when it is missing, or it is equal to 0 and x0 is provided, snomadRSolve will be called to solve the problem. Otherwise, smultinomadRSolve will be called

random.seed

when it is not missing and not equal to 0, the initial points will be generated using this seed when nmulti > 0

opts

list of options for NOMAD, see the NOMAD user guide https://www.gerad.ca/nomad/Downloads/user_guide.pdf. Options can also be set by nomad.opt which should be in the folder where R (snomadr) is executed. Options that affect the solution and their defaults and some potential values are

"MAX_BB_EVAL"=10000

"INITIAL_MESH_SIZE"=1

"MIN_MESH_SIZE"="r1.0e-10"

"MIN_POLL_SIZE"=1

Note that the "r..." denotes relative measurement (relative to lb and ub)

Note that decreasing the maximum number of black box evaluations ("MAX_BB_EVAL") will terminate search sooner and may result in a less accurate solution. For complicated problems you may want to increase this value. When experimenting it is desirable to set "DISPLAY_DEGREE"=1 (or a larger integer) to get some sense for how the algorithm is progressing

print.output

when FALSE, no output from snomadr is displayed on the screen. If the NOMAD option "DISPLAY_DEGREE"=0, is set, there will also be no output from NOMAD. Higher integer values for "DISPLAY_DEGREE"= provide successively more detail

information

is a list. snomadr will call snomadRInfo to return the information about NOMAD according to the values of "info", "version" and "help".

"info"="-i": display the usage and copyright of NOMAD

"version"="-v": display the version of NOMAD you are using

"help"="-h": display all options

You also can display a specific option, for example, "help"="-h x0", this will tell you how to set x0

environment that is used to evaluate the functions. Use this to pass additional data or parameters to a function

...

arguments that will be passed to the user-defined objective and constraints functions. See the examples below

## Value

The return value contains a list with the inputs, and additional elements

call

the call that was made to solve

status

integer value with the status of the optimization

message

iterations

number of iterations that were executed, if multiple initial points are set, this number will be the sum for each initial point.

objective

value if the objective function in the solution

solution

optimal value of the controls

## Details

snomadr is used in the crs package to numerically minimize an objective function with respect to the spline degree, number of knots, and optionally the kernel bandwidths when using crs with the option cv="nomad" (default). This is a constrained mixed integer combinatoric problem and is known to be computationally ‘hard’. See frscvNOMAD and krscvNOMAD for the functions called when cv="nomad" while using crs.

However, the user should note that for simple problems involving one predictor exhaustive search may be faster and potentially more accurate, so please bear in mind that cv="exhaustive" can be useful when using crs.

Naturally, exhaustive search is also useful for verifying solutions returned by snomadr. See frscv and krscv for the functions called when cv="exhaustive" while using crs.

## References

Abramson, M.A. and C. Audet and G. Couture and J.E. Dennis Jr. and S. Le Digabel (2011), “The NOMAD project”. Software available at https://www.gerad.ca/nomad.

Le Digabel, S. (2011), “Algorithm 909: NOMAD: Nonlinear Optimization With The MADS Algorithm”. ACM Transactions on Mathematical Software, 37(4):44:1-44:15.

optim, nlm, nlminb

## Examples

# NOT RUN {
## List all options

## Print given option,  for example,  MESH_SIZE

## Print the version of NOMAD

## This is the example found in

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"=1)

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

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$params <- c(1,2,3) ## pass The environment that should be used to evaluate functions to snomadr snomadr(n =1, x0 = x0, eval.f = eval.f.ex2, snomadr.environment = auxdata ) ## Solve using algebra cat( paste( "Minimizing f(x) = ax^2 + bx + c\n" ) ) cat( paste( "Optimal value of control is -b/(2a) = ", -params[2]/(2*params[1]), "\n" ) ) cat( paste( "With value of the objective function f(-b/(2a)) = ", eval.f.ex1( -params[2]/(2*params[1]), params ), "\n" ) ) ## The following example is NOMAD/examples/advanced/multi_start/multi.cpp ## This will call smultinomadRSolve to resolve the problem. eval.f.ex1 <- function(x, params) { M<-as.numeric(params$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

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.
##
eval.f       = eval.f.ex1,
bbin         = bbin,
bbout        = bbout,
lb           = lb,
ub           = ub,
nmulti = as.integer(nmulti),
print.output = TRUE,
params       = params )
# }
# NOT RUN {

# }
# NOT RUN {
<!-- %% End dontrun -->
# }