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I have 2 lists with X,Y coordinates of points. List 1 contains more points than list 2. The task is to find pairs of points in a way that the overall euclidean distance is minimized. I have a working code, but i don't know if this is the best way and I would like to get hint what I can improve for result (better algorithm to find the minimum ) or speed, because the list are about 2000 elements each. The round in the sample vectors is implemented to get also points with same distances. With the "rdist" function all distances are generated in "distances". Than the minimum in the matrix is used to link 2 point ("dist_min"). All distances of these 2 points are now replaced by NA and the loop continues by searching the next minimum until all points of list 2 have a point from list 1. At the end I have added a plot for visualization. require(fields) set.seed(1) x1y1.data <- matrix(round(runif(200*2),2), ncol = 2) # generate 1st set of points x2y2.data <- matrix(round(runif(100*2),2), ncol = 2) # generate 2nd set of points distances <- rdist(x1y1.data, x2y2.data) dist_min <- matrix(data=NA,nrow=ncol(distances),ncol=7) # prepare resulting vector with 7 columns for(i in 1:ncol(distances)) { inds <- which(distances == min(distances,na.rm = TRUE), arr.ind=TRUE) dist_min[i,1] <- inds[1,1] # row of point(use 1st element of inds if points have same distance) dist_min[i,2] <- inds[1,2] # column of point (use 1st element of inds if points have same distance) dist_min[i,3] <- distances[inds[1,1],inds[1,2]] # distance of point dist_min[i,4] <- x1y1.data[inds[1,1],1] # X1 ccordinate of 1st point dist_min[i,5] <- x1y1.data[inds[1,1],2] # Y1 coordinate of 1st point dist_min[i,6] <- x2y2.data[inds[1,2],1] # X2 coordinate of 2nd point dist_min[i,7] <- x2y2.data[inds[1,2],2] # Y2 coordinate of 2nd point distances[inds[1,

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