This function uses Gurobi to find prioritizations using the input parameter
and data stored in a `RapUnsolved()`

object, and returns a
`RapSolved()`

object with outputs in it.

```
# S4 method for RapUnsolOrSol,missing
solve(a, b, ..., verbose = FALSE)
# S4 method for RapUnsolOrSol,GurobiOpts
solve(a, b, verbose = FALSE)
# S4 method for RapUnsolOrSol,matrix
solve(a, b, verbose = FALSE)
# S4 method for RapUnsolOrSol,numeric
solve(a, b, verbose = FALSE)
# S4 method for RapUnsolOrSol,logical
solve(a, b, verbose = FALSE)
```

- a
`RapUnsolved()`

or`RapSolved()`

object.- b
`missing`

to generate solutions using Gurobi. Prioritizations can be specified using`logical`

,`numeric`

, or`base::matrix()`

objects. This may be useful for evaluating the performance of solutions obtained using other software.- ...
not used.

- verbose
`logical`

should messages be printed during creation of the initial model matrix?.

`RapSolved()`

object

This function is used to solve a `RapUnsolved()`

object that
has all of its inputs generated. The rap function (without lower case 'r')
provides a more general interface for generating inputs and outputs.

```
# \dontrun{
# load RapUnsolved object
data(sim_ru)
# solve it using Gurobi
sim_rs <- solve(sim_ru)
# evaluate manually specified solution using planning unit indices
sim_rs2 <- solve(sim_ru, seq_len(10))
#> Warning: some species have space.held values less than 0, and thus are poorly represented
# evaluate manually specifed solution using binary selections
sim_rs3 <- solve(sim_ru, c(rep(TRUE, 10), rep(FALSE, 90)))
#> Warning: some species have space.held values less than 0, and thus are poorly represented
# evaluate multiple manually specified solutions
sim_rs4 <- solve(sim_ru, matrix(sample(c(0, 1), size = 500, replace = TRUE),
ncol = 100, nrow = 5))
# }
```