R interface to NOMAD
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^n
s.t. g(x) <= 0
x_L <= x <= x_U
where is the objective function, and are the constraint functions. The vectors and
are the bounds on the variables . The functions and can be nonlinear and nonconvex. The variables can be integer, continuous real number, binary, and categorical.
Kindly see https://www.gerad.ca/en/software/nomad and the references below for details.
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(), snomadr.environment = new.env(), ... )
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://nomad-4-user-guide.readthedocs.io/en/latest/#
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://nomad-4-user-guide.readthedocs.io/en/latest/#. 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
snomadr.environment: 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
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.
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: more informative message with the status of the optimization
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
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/en/software/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.
Zhenghua Nie niez@mcmaster.ca
optim, nlm, nlminb
## 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"=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 ## 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$params <- c(1,2,3) ## pass The environment that should be used to evaluate functions to snomadr snomadr(n =1, x0 = x0, eval.f = eval.f.ex2, snomadr.environment = auxdata ) ## Solve using algebra cat( paste( "Minimizing f(x) = ax^2 + bx + c\n" ) ) cat( paste( "Optimal value of control is -b/(2a) = ", -params[2]/(2*params[1]), "\n" ) ) cat( paste( "With value of the objective function f(-b/(2a)) = ", eval.f.ex1( -params[2]/(2*params[1]), params ), "\n" ) ) ## The following example is NOMAD/examples/advanced/multi_start/multi.cpp ## This will call smultinomadRSolve to resolve the problem. eval.f.ex1 <- function(x, params) { M<-as.numeric(params$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 ## 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)
Useful links