Start the next search from a known solution

The service's search starts from each variable's start value, which is 0 unless set otherwise. When a model is solved again after a small change, such as a tightened constraint or more scenarios, starting from the previous answer usually gets to a good solution sooner than starting from scratch. set_start() sets the start values from a result, or from a named vector of values. Variables it is not given keep their start value.

Usage

set_start(m, values)

Arguments

m

A model().

values

A result from solve(), or a named numeric vector, with names as in a solution: "x" for a single variable, "x[1]", "x[2]", ... for the elements of a vector variable.

Value

The model, invisibly.

Details

The service rounds the start of a whole-number variable, and moves a start that lies outside the variable's bounds to the nearest bound. A result also holds the ordering found for each permutation (see perm_var()) and the groups found for each partition (see part_var()), and these become their starts.

Examples

if (FALSE) { # \dontrun{
m <- model()
stock  <- num_var(m, "stock", lower = 0, upper = 200)
demand <- rand_var(m, "demand", normal(100, 15))
set_scenarios(m, 512, seed = 42)
minimize(m, 3 * stock + 10 * expectation(max(demand - stock, 0)))
res <- solve(m)

set_scenarios(m, 1000, seed = 42)    # the same model, more scenarios
set_start(m, res)                    # start where the last search ended
solve(m)
} # }