Nothing

```
lp.assign <- function(cost.mat, direction = c("min", "max"), presolve = 0,
compute.sens = 0)
{
# lp.assign: solves the assignment problem. This
# is a linear program with an ixj matrix of decision variables,
# and i+j constraints: that the rows and columns all add up to one.
#
# Arguments:
# cost.mat: matrix or data.frame of costs
# direction: "min" (default) or "max"
# presolve: numeric. Presolve? Default 0. Currently ignored.
# compute.sens: numeric. Compute sensitivities? Default 0 (no).
# Any non-zero number means "yes" and, in that
# case, presolving is attempted.
#
# Return value:
# a list from lpsolve, including objective and assignments.
direction <- match.arg(direction)
nr <- nrow(cost.mat)
nc <- ncol(cost.mat)
rnum.signs <- rep("=", nr)
row.rhs <- rep(1, nr)
cnum.signs <- rep("=", nc)
col.rhs <- rep(1, nc)
lp.transport(cost.mat, direction = direction, row.signs = rnum.signs,
row.rhs = row.rhs, col.signs = cnum.signs, col.rhs = col.rhs,
presolve = presolve, compute.sens = compute.sens)
}
```

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