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Compile a problem

Source: R/compile.R, R/multi_compile.R
compile.Rd

Compile a project prioritization problem() into a general purpose format for optimization.

Usage

compile(x, ...)

# S3 method for class 'ProjectProblem'
compile(x, n_approx = 100, ...)

# S3 method for class 'MultiObjProjectProblem'
multi_compile(x, ...)

# S3 method for class 'list'
multi_compile(x, ...)

Arguments

x

ProjectProblem object.

...

not used.

n_approx

integer number of points to use for piece-wise linear approximations of non-linear terms. Defaults to 100.

Value

An OptimizationProblem object.

Details

This function might be useful for those interested in understanding how their project prioritization problem() is expressed as a mathematical problem. However, if the problem just needs to be solved, then the solve() function should be used instead.

Examples

# load data
data(sim_projects, sim_features, sim_actions)

# build problem with maximum weighted sum objective, $200 budget, and
# binary decisions
p <-
  problem(
    sim_projects, sim_actions, sim_features,
    "name", "success", "name", "cost", "name"
  ) %>%
  add_max_wtd_sum_objective(budget = 200) %>%
  add_binary_decisions()

# print problem
print(p)
#> Project Prioritization Problem
#> actions:         F1_action, F2_action, F3_action, ... (6 actions)
#> projects:        F1_project, F2_project, F3_project, ... (6 projects)
#> features:        F1, F2, F3, ... (5 features)
#> action costs:    continuous values (between 0 and 103.226)
#> project success: proportion values (between 0.814 and 1)
#> objective:       maximum weighted sum objective
#> targets:         none specified
#> weights:         none specified
#> constraints:     none specified
#> decisions:       binary decision
#> solver:          none specified

# compile problem
o <- compile(p)

# print compiled problem
print(o)
#> optimization problem
#>   objective:   linear
#>   model sense: max
#>   dimensions:  27, 27, 63 (nrow, ncol, ncell)
#>   variables:   22 (B), 5 (C)