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 = NULL,
gap_limit = NULL,
time_limit = NULL,
solution_limit = NULL,
cores = NULL,
verbose = NULL,
log_file = NULL,
write_log = NULL,
solver_params = list(),
...
)Arguments
- x
A
Problemobject.- 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 requesting early termination after a feasible solution is found. Supported by Gurobi, CBC, and SYMPHONY, but not by CPLEX through Rcplex. If
NULL, the previously stored value is kept unchanged.- cores
Optional positive integer giving the maximum number of solver threads. Currently supported by Gurobi. 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 complete path or file name of the solver log. Currently supported by Gurobi. If
NULL, the previously stored value is kept unchanged.- write_log
Optional logical flag indicating whether solver output should be written to a file. Currently supported by Gurobi. If
NULL, the previously stored value is kept unchanged.- solver_params
Named list of solver-specific parameters. These are merged with previously stored parameters. Rcplex parameters are validated against its supported control names; Rsymphony does not currently receive arbitrary solver-specific parameters.
- ...
Additional named solver-specific parameters. These are merged into
solver_params. For example,MIPFocus = 1for Gurobi.
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 maximum number of solver threads.
It is supported by Gurobi. Rcplex, rcbc, and Rsymphony do not reliably
expose thread control through the interfaces used here; multiscape therefore
warns and ignores cores for CPLEX, CBC, and SYMPHONY. If
the requested number exceeds the number of detected logical processors, 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 log files
are currently available only with Gurobi. A parameter that is not available
through the selected R solver interface is reported with a warning and is
not stored as if it had been applied.
Backend capabilities
Common parameters are translated only when the selected backend supports them:
Gurobi supports
cores,solution_limit, andwrite_log.CBC supports
solution_limit. Thread control and CBC log files are not exposed reliably through the current rcbc integration.CPLEX through Rcplex does not expose
cores,solution_limit, or solver log files.SYMPHONY through Rsymphony supports
solution_limit, but does not exposecoresor solver log files.
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.
Examples
# Load a complete simulated planning problem.
example_data <- load_sim_multiaction()
x <- create_problem(
pu = example_data$planning_units,
features = example_data$features,
dist_features = example_data$dist_features,
cost = "cost"
)
x1 <- set_solver(
x,
solver = "cbc",
gap_limit = 0.01,
time_limit = 300,
cores = 2,
verbose = TRUE
)
#> Warning: Parameter(s) not available for solver 'cbc' through its current R interface: cores (thread control is not available through rcbc). The unsupported setting(s) will be ignored.
x1$data$solve_args
#> $solver
#> [1] "cbc"
#>
#> $gap_limit
#> [1] 0.01
#>
#> $time_limit
#> [1] 300
#>
#> $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] "cbc"
#>
#> $gap_limit
#> [1] 0.05
#>
#> $time_limit
#> [1] 300
#>
#> $verbose
#> [1] TRUE
#>
#> $solver_params
#> $solver_params$randomSeed
#> [1] 123
#>
#>
