Generate a portfolio of solutions for a conservation planning
`problem()`

by finding a certain number of solutions that
are all within a pre-specified optimality gap. This method is useful for
generating multiple solutions that can be used to calculate selection
frequencies for moderate and large-sized problems (similar to
*Marxan*).

`add_gap_portfolio(x, number_solutions, pool_gap = 0.1)`

- x
`problem()`

(i.e.,`ConservationProblem`

) object.- number_solutions
`integer`

number of solutions required.- pool_gap
`numeric`

gap to optimality for solutions in the portfolio. This relative gap specifies a threshold worst-case performance for solutions in the portfolio. For example, value of 0.1 will result in the portfolio returning solutions that are within 10% of an optimal solution. Note that the gap specified in the solver (i.e.,`add_gurobi_solver()`

must be less than or equal to the gap specified to generate the portfolio. Defaults to 0.1.

Object (i.e., `ConservationProblem`

) with the portfolio
added to it.

This strategy for generating a portfolio requires problems to
be solved using the *Gurobi* software suite (i.e., using
`add_gurobi_solver()`

. Specifically, version 9.0.0 (or greater)
of the gurobi package must be installed.
Note that the number of solutions returned may be less than the argument to
`number_solutions`

, if the total number of solutions that
meet the optimality gap is less than the number of solutions requested.
Also, note that this portfolio function only works with problems
that have binary decisions (i.e., specified using
`add_binary_decisions()`

).

See portfolios for an overview of all functions for adding a portfolio.

Other portfolios:
`add_cuts_portfolio()`

,
`add_extra_portfolio()`

,
`add_shuffle_portfolio()`

,
`add_top_portfolio()`

```
# \dontrun{
# set seed for reproducibility
set.seed(600)
# load data
data(sim_pu_raster, sim_features)
# create minimal problem with a portfolio containing 10 solutions within 20%
# of optimality
p1 <- problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.05) %>%
add_gap_portfolio(number_solutions = 5, pool_gap = 0.2) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem and generate portfolio
s1 <- solve(p1)
# print number of solutions found
print(length(s1))
#> [1] 5
# plot solutions
plot(stack(s1), axes = FALSE, box = FALSE)
# create multi-zone problem with a portfolio containing 10 solutions within
# 20% of optimality
p2 <- problem(sim_pu_zones_stack, sim_features_zones) %>%
add_min_set_objective() %>%
add_relative_targets(matrix(runif(15, 0.1, 0.2), nrow = 5,
ncol = 3)) %>%
add_gap_portfolio(number_solutions = 5, pool_gap = 0.2) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem and generate portfolio
s2 <- solve(p2)
# print number of solutions found
print(length(s2))
#> [1] 5
# plot solutions in portfolio
plot(stack(lapply(s2, category_layer)),
main = "solution", axes = FALSE, box = FALSE)
# }
```