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Recently I have coauthored a new localsolver package that can be used to solve large scale optimization problems from R. It is a wrapper around commercial solver that is free for academia. If you are interested why it is worthwhile to give it a look – read on.

### Before we begin

### The test problem

Let us start with code that generates test matrices for our examples:

kmedoids.generate.data <- function(seed, N, P) {

if(!is.null(seed)) {

set.seed(seed)

}

InfiniteDist <- 10000.0

Dist <- matrix(round(runif(n=N * N) * 100, digits = 0),

nrow = N)

diag(Dist) <- 0

return(list(

N = as.integer(N),

P = as.integer(P),

Dist = Dist,

InfiniteDist = InfiniteDist))

}

data <- kmedoids.generate.data(seed = 1234, N = 20L, P = 5L)

One thing that is worth explaining is that in the code we define InfiniteDist which will serve us as surrogate for infinity (all actual distances are shorter than it).

Now let me present how the problem can be solved with localsolver and Rglpk.

### localsolver approach

library(localsolver)

lsp.model <- “function model() {

x[1..N] <- bool() ; // point i is in P iff x[i] = 1

constraint sum[i in 1..N](x[i]) == P ;

minDist[i in 1..N] <- min[j in 1..N](

x[j] ? Dist[j][i] : InfiniteDist);

// minimize sum of distances

objective <- sum[i in 1..N]( minDist[i] ) ;

minimize objective;

}”

lsp <- ls.problem(model.text.lsp = lsp.model)

lsp <- set.params(lsp = lsp, lsTimeLimit = 1)

lsp <- set.params(lsp = lsp, lsNbThreads = 1)

lsp <- add.output.expr(lsp=lsp, expr.text.lsp = “x”,

dimensions = data$N)

lsp <- add.output.expr(lsp=lsp, expr.text.lsp = “objective”,

dimensions = 1)

lsp.solution <- ls.solve(lsp = lsp, data = data)

Notice that we can define the optimization problem using a simple domain specific language as a string. Interestingly in the string we can use all variables defined in data list as localsolver is able to interact with extrernal data source. Additionally using set.params and add.output.expr we can set up the parameters of optimization engine and optimization output we will want to collect.

As a result we get a list with optimization output. Importantly the contents of the list can be controlled programatically as mentioned above.

### Rglpk approach

In my opinion the code looks cryptic in comparison to localsolver specification. There are two reasons for such a situation:

- we have to have two mix two types of decision variables: location of depots and assignment of locations to depots
- we have to construct a complex constraints matrix combining different types of conditions in one big data structure

### Summary

- using natural (mathematical) formulation of objective function and constraints
- allowing to dynamically update reference data: that lsp.model string did not have any hard coded constants – everything is loaded from metadata provided as R list.

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