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Configure solver settings

Source: R/set_solver.R
set_solver.Rd

Store solver configuration inside a Problem object so that solve can later run using the stored backend and runtime options.

This function does not build or solve the optimization model. It only updates the solver configuration stored in x$data$solve_args.

Usage

set_solver(
  x,
  solver = c("auto", "gurobi", "cplex", "cbc", "symphony"),
  gap_limit = NULL,
  time_limit = NULL,
  solution_limit = NULL,
  cores = NULL,
  verbose = FALSE,
  log_file = NULL,
  write_log = NULL,
  solver_params = list(),
  ...
)

Arguments

x

A Problem object.

solver

Character string indicating the solver backend to use. Must be one of "auto", "gurobi", "cplex", "cbc", or "symphony".

gap_limit

Optional numeric value in \([0,1]\) giving the relative optimality gap for mixed-integer optimization. If NULL, the previously stored value is kept unchanged.

time_limit

Optional non-negative numeric value giving the maximum solving time in seconds. If NULL, the previously stored value is kept unchanged.

solution_limit

Optional logical flag controlling backend-specific early stopping after feasible solution discovery. If NULL, the previously stored value is kept unchanged.

cores

Optional positive integer giving the number of CPU cores to use. If NULL, the previously stored value is kept unchanged.

verbose

Optional logical flag indicating whether the solver should print log output. If NULL, the previously stored value is kept unchanged.

log_file

Optional character string giving the name of the solver log file. If NULL, the previously stored value is kept unchanged.

write_log

Optional logical flag indicating whether solver output should be written to a file. If NULL, the previously stored value is kept unchanged.

solver_params

Named list of solver-specific parameters. These are merged with previously stored backend-specific parameters rather than replacing them completely.

...

Additional named solver-specific parameters. These are merged into solver_params. For example, MIPFocus = 1 for Gurobi.

Value

An updated Problem object with modified solver settings stored in x$data$solve_args.

Details

Purpose

The multiscape workflow separates problem specification from solver configuration. Problem data, actions, effects, targets, objectives, and methods are stored in the Problem object, and solver settings are stored separately in x$data$solve_args.

This function allows solver options to be configured once and reused later through solve(x) without repeating the same arguments each time.

Stored fields

The solver configuration is stored in x$data$solve_args. Typical entries include:

  • solver,

  • gap_limit,

  • time_limit,

  • solution_limit,

  • cores,

  • verbose,

  • write_log,

  • log_file,

  • solver_params.

Incremental update semantics

This function updates solver settings incrementally.

If an argument is supplied as NULL, the previously stored value is kept unchanged. Therefore, repeated calls can be used to modify only selected components of the solver configuration.

For example, a user may first configure the solver backend and time limit, and later update only the optimality gap or only a backend-specific parameter.

Gap limit

The argument gap_limit is interpreted as a relative optimality gap for mixed-integer optimization. It must lie in \([0,1]\).

If the solver stops with incumbent value \(z^{\mathrm{inc}}\) and best bound \(z^{\mathrm{bd}}\), then the exact stopping rule depends on the solver backend, but conceptually gap_limit controls the maximum accepted relative difference between the incumbent and the bound.

Time limit

The argument time_limit is interpreted as a maximum wall-clock time in seconds allowed for the solver.

Solution limit

The argument solution_limit is stored as a logical flag. Its exact meaning depends on the backend-specific solving layer, but conceptually it requests early termination after finding a feasible solution according to the behaviour supported by the chosen solver.

Cores

The argument cores specifies the number of CPU cores to use. If the requested number exceeds the number of detected cores, it is capped to the detected maximum with a warning.

Verbose output and log files

The arguments verbose, write_log, and log_file control how solver logging is handled. These options are stored and later interpreted by the solving layer for the selected backend.

Solver-specific parameters

Additional backend-specific parameters can be passed in two ways:

  • through the named list solver_params,

  • through additional named arguments in ....

These two sources are merged, and the result is then merged with any previously stored solver_params. Existing parameters are therefore preserved unless explicitly overwritten.

This is particularly useful for backend-specific controls such as node selection, emphasis parameters, tolerances, or heuristics.

Supported backends

The solver argument selects the backend to be used later by solve. Supported values are:

  • "auto": let the solving layer choose an available backend,

  • "gurobi",

  • "cplex",

  • "cbc",

  • "symphony".

This function only stores the requested backend. Availability of the backend is checked later when solving.

See also

solve, set_solver_gurobi, set_solver_cplex, set_solver_cbc, set_solver_symphony

Examples

pu_tbl <- data.frame(
  id = 1:4,
  cost = c(1, 2, 3, 4)
)

feat_tbl <- data.frame(
  id = 1:2,
  name = c("feature_1", "feature_2")
)

dist_feat_tbl <- data.frame(
  pu = c(1, 1, 2, 3, 4),
  feature = c(1, 2, 2, 1, 2),
  amount = c(5, 2, 3, 4, 1)
)

x <- create_problem(
  pu = pu_tbl,
  features = feat_tbl,
  dist_features = dist_feat_tbl,
  cost = "cost"
)

x1 <- set_solver(
  x,
  solver = "cbc",
  gap_limit = 0.01,
  time_limit = 300,
  cores = 2,
  verbose = TRUE
)

x1$data$solve_args
#> $solver
#> [1] "cbc"
#> 
#> $gap_limit
#> [1] 0.01
#> 
#> $time_limit
#> [1] 300
#> 
#> $cores
#> [1] 2
#> 
#> $verbose
#> [1] TRUE
#> 
#> $solver_params
#> list()
#> 

# Update only selected settings
x2 <- set_solver(
  x1,
  gap_limit = 0.05,
  solver_params = list(randomSeed = 123)
)

x2$data$solve_args
#> $solver
#> [1] "auto"
#> 
#> $gap_limit
#> [1] 0.05
#> 
#> $time_limit
#> [1] 300
#> 
#> $cores
#> [1] 2
#> 
#> $verbose
#> [1] FALSE
#> 
#> $solver_params
#> $solver_params$randomSeed
#> [1] 123
#> 
#>