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Solve a JuMP Model

Source: R/solve_jump.R
solve_jump.Rd

Execute a Julia/JuMP optimization model with configurable backend options. Results can be optionally loaded back into the model object.

Usage

solve_jump(
  model = NULL,
  model_dir,
  method = c("system", "juliacall", "background"),
  verbose = TRUE,
  load_results = !is.null(model),
  julia_args = NULL,
  timeout = NULL
)

Arguments

model

A multimod model object (optional, used for loading results back)

model_dir

Path to the model directory containing solvers/jump/

method

Execution method: "system" (default), "juliacall", or "background"

verbose

Print Julia output to console

load_results

Load solution values back into model$variables

julia_args

Additional arguments to pass to Julia (e.g., "–threads=4")

timeout

Maximum execution time in seconds (NULL for no limit)

Value

List with solution information:

exit_code

Julia process exit code (0 = success)

success

Logical indicating successful execution

objective

Objective function value (if available)

status

Solver termination status (if available)

solve_time

Solution time in seconds (if available)

output

Julia stdout/stderr (if verbose=TRUE)

constraints

Constraint statistics data.frame

variables

Variable statistics data.frame

Details

The function supports three execution methods:

system (default): Uses system2() to call Julia. No extra dependencies. Simple, robust, works everywhere Julia is installed.

juliacall: Uses JuliaCall package for R-Julia integration. Allows bi-directional data transfer and access to Julia objects from R. Requires JuliaCall package.

background: Uses callr package for non-blocking execution. Allows running multiple models in parallel. Requires callr package.

Solution values are saved by the Julia model to solvers/jump/solution/ (in Arrow IPC or CSV format). If load_results=TRUE and model is provided, solutions are loaded into result$model and moved to model_dir/variables/. A log file (solution_log.txt) in model_dir tracks all solution loads. You must reassign your model variable to access the solutions:

result <- solve_jump(my_model, "path/to/model", load_results = TRUE)

my_model <- result$model # Update with loaded solutions

Examples

if (FALSE) { # \dontrun{
# Generate and solve model
model <- as_multimod(read_gams("model.gms"))
save_model(model, "tmp/my_model", format = "ipc")
write_jump(model, model_dir = "tmp/my_model")

# Solve with default settings
result <- solve_jump(model_dir = "tmp/my_model")

# Solve and load results back into model
result <- solve_jump(model, model_dir = "tmp/my_model", load_results = TRUE)

# Access solution values
vTechCap_solution <- model$variables$vTechCap$solution

# Use different backend
result <- solve_jump(model_dir = "tmp/my_model", method = "juliacall")

# Parallel execution with timeout
result <- solve_jump(model_dir = "tmp/my_model", 
                     method = "background", 
                     timeout = 3600)
} # }