# R/kantorovich_glpk.R In kantorovich: Kantorovich Distance Between Probability Measures

#### Documented in kantorovich_glpk

```#' Computes Kantorovich distance with GLPK
#'
#' Kantorovich distance using the \code{Rglpk} package
#'
#' @param mu (row margins) probability measure in numeric mode
#' @param nu (column margins) probability measure in numeric mode
#' @param dist matrix, the distance to be minimized on average;
#' if \code{NULL}, the 0-1 distance is used.
#' @param solution logical; if \code{TRUE} the solution is returned in the
#' \code{"solution"} attributes of the output
#' @param stop_if_fail logical; if \code{TRUE}, an error is returned in the
#' case when no solution is found; if \code{FALSE}, the output of
#' \code{\link[Rglpk]{Rglpk_solve_LP}} is returned with a warning
#' @param ... arguments passed to \code{\link[Rglpk]{Rglpk_solve_LP}}
#'
#' @examples
#' mu <- c(1/7,2/7,4/7)
#' nu <- c(1/4,1/4,1/2)
#' kantorovich_glpk(mu, nu)
#'
#' @import Rglpk
#' @importFrom slam as.simple_triplet_matrix
#' @importFrom methods is
#' @export
#'
kantorovich_glpk <- function(mu, nu, dist=NULL, solution=FALSE, stop_if_fail=TRUE, ...){
m <- length(mu)
n <- length(nu)
# checks
if(m != n) stop("mu and nu do not have the same length")
if(!is.null(dist)){
if(!is(dist, "matrix") || mode(dist) != "numeric")
stop("dist must be a numeric matrix")
if(nrow(dist)!=m || ncol(dist)!=m)
stop("invalid dimensions of the dist matrix")
}
if(sum(mu)!=1 || sum(nu)!=1 || any(mu<0) || any(nu<0)){
message("Warning: mu and/or nu are not probability measures")
}
#
if(is.null(dist)) dist <- 1-diag(m)
kanto <-
Rglpk_solve_LP(
obj = c(t(dist)),
mat = as.simple_triplet_matrix(rbind(-diag(m*n),
rbind(t(model.matrix(~0+gl(m,n)))[,],
t(model.matrix(~0+factor(rep(1:n,m))))[,]))),
dir = c(rep("<=", m*n), rep("==", m+n)),
rhs = c(rep(0,m*n), c(mu, nu)), ...)
# status
if(kanto\$status != 0){
if(stop_if_fail){
stop(sprintf("No optimal solution found: status %s \n", kanto\$status))
}else{
warning(sprintf("No optimal solution found: status %s \n", kanto\$status))
return(kanto)
}
}
# output
out <- kanto\$optimum
if(solution) attr(out, "solution") <- matrix(kanto\$solution, nrow=m, byrow=TRUE)
out
}
```

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kantorovich documentation built on Aug. 10, 2022, 5:08 p.m.