Package 'scip'

Print method for scip_control objects

Description

Print method for scip_control objects

Usage

## S3 method for class 'scip_control'
print(x, ...)

Arguments

x	A scip_control object.
...	Ignored.

Value

Invisible x.

---

Add an indicator constraint to a SCIP model

Description

If binvar = 1 then sum(coefs * x[vars]) <= rhs.

Usage

scip_add_indicator_cons(model, binvar, vars, coefs, rhs, name = NULL)

Arguments

model	A SCIP model.
binvar	Integer; 1-based index of the binary indicator variable.
vars	Integer vector; 1-based variable indices.
coefs	Numeric vector; coefficients.
rhs	Numeric; right-hand side.
name	Character; constraint name.

Value

Integer; 1-based constraint index.

---

Add a linear constraint to a SCIP model

Description

Adds lhs <= sum(coefs * x[vars]) <= rhs.

Usage

scip_add_linear_cons(model, vars, coefs, lhs = -Inf, rhs = Inf, name = NULL)

Arguments

model	A SCIP model.
vars	Integer vector; 1-based variable indices.
coefs	Numeric vector; coefficients (same length as vars).
lhs	Numeric; left-hand side. Default -Inf.
rhs	Numeric; right-hand side. Default Inf.
name	Character; constraint name. Default auto-generated.

Value

Integer; 1-based constraint index.

---

Add a quadratic constraint to a SCIP model

Description

Adds lhs <= linexpr + quadexpr <= rhs where quadexpr = sum(quadcoefs[k] * x[quadvars1[k]] * x[quadvars2[k]]).

Usage

scip_add_quadratic_cons(
  model,
  linvars = integer(0),
  lincoefs = double(0),
  quadvars1 = integer(0),
  quadvars2 = integer(0),
  quadcoefs = double(0),
  lhs = -Inf,
  rhs = Inf,
  name = NULL
)

Arguments

model	A SCIP model.
linvars	Integer vector; 1-based variable indices for linear part.
lincoefs	Numeric vector; linear coefficients.
quadvars1, quadvars2	Integer vectors; 1-based variable indices for quadratic terms.
quadcoefs	Numeric vector; quadratic coefficients.
lhs, rhs	Numeric; constraint bounds.
name	Character; constraint name.

Value

Integer; 1-based constraint index.

---

Add a SOS1 constraint to a SCIP model

Description

At most one variable in the set can be nonzero.

Usage

scip_add_sos1_cons(model, vars, weights = NULL, name = NULL)

Arguments

model	A SCIP model.
vars	Integer vector; 1-based variable indices.
weights	Numeric vector; weights determining branching order.
name	Character; constraint name.

Value

Integer; 1-based constraint index.

---

Add a SOS2 constraint to a SCIP model

Description

At most two adjacent variables in the set can be nonzero.

Usage

scip_add_sos2_cons(model, vars, weights = NULL, name = NULL)

Arguments

model	A SCIP model.
vars	Integer vector; 1-based variable indices.
weights	Numeric vector; weights determining adjacency order.
name	Character; constraint name.

Value

Integer; 1-based constraint index.

---

Add a variable to a SCIP model

Description

Add a variable to a SCIP model

Usage

scip_add_var(model, obj, lb = 0, ub = Inf, vtype = "C", name = NULL)

Arguments

model	A SCIP model (external pointer from scip_model).
obj	Numeric; objective coefficient.
lb	Numeric; lower bound. Default 0.
ub	Numeric; upper bound. Default Inf.
vtype	Character; variable type: "C" (continuous), "B" (binary), or "I" (integer). Default "C".
name	Character; variable name. Default auto-generated.

Value

Integer; 1-based variable index.

---

Add multiple variables to a SCIP model

Description

Add multiple variables to a SCIP model

Usage

scip_add_vars(model, obj, lb = 0, ub = Inf, vtype = "C", names = NULL)

Arguments

model	A SCIP model.
obj	Numeric vector; objective coefficients.
lb	Numeric; lower bounds (scalar or vector). Default 0.
ub	Numeric; upper bounds (scalar or vector). Default Inf.
vtype	Character; variable types (scalar or vector). Default "C".
names	Character vector; variable names. Default auto-generated.

Value

Integer; 1-based index of first variable added.

---

SCIP solver control parameters

Description

Create a list of control parameters for the SCIP solver. Parameters are organized into logical groups: output, limits, tolerances, presolving, LP, branching, heuristics, separating, and emphasis. Any SCIP parameter can also be set directly using its native path via ....

Usage

scip_control(
  verbose = TRUE,
  verbosity_level = 3L,
  display_freq = 100L,
  time_limit = Inf,
  node_limit = -1L,
  stall_node_limit = -1L,
  sol_limit = -1L,
  best_sol_limit = -1L,
  mem_limit = Inf,
  restart_limit = -1L,
  gap_limit = 0,
  abs_gap_limit = 0,
  feastol = 1e-06,
  dualfeastol = 1e-07,
  epsilon = 1e-09,
  presolving = TRUE,
  presolve_rounds = -1L,
  presolve_emphasis = "default",
  lp_threads = 1L,
  lp_iteration_limit = -1L,
  lp_scaling = TRUE,
  branching_score = "p",
  heuristics_emphasis = "default",
  separating_emphasis = "default",
  emphasis = "default",
  threads = 1L,
  ...
)

Arguments

verbose	Logical; print solver output. Default TRUE.
verbosity_level	Integer 0–5; verbosity detail. Default 3.
display_freq	Integer; node display frequency. Default 100.
time_limit	Numeric; time limit in seconds. Default Inf.
node_limit	Integer; max nodes. Default -1L.
stall_node_limit	Integer; stall nodes. Default -1L.
sol_limit	Integer; solution limit. Default -1L.
best_sol_limit	Integer; improving solution limit. Default -1L.
mem_limit	Numeric; memory limit in MB. Default Inf.
restart_limit	Integer; restart limit. Default -1L.
gap_limit	Numeric; relative MIP gap. Default 0.
abs_gap_limit	Numeric; absolute gap. Default 0.
feastol	Numeric; feasibility tolerance. Default 1e-6.
dualfeastol	Numeric; dual feasibility tolerance. Default 1e-7.
epsilon	Numeric; zero tolerance. Default 1e-9.
presolving	Logical; enable presolving. Default TRUE.
presolve_rounds	Integer; presolve rounds. Default -1L.
presolve_emphasis	Character; presolving emphasis. Default "default".
lp_threads	Integer; LP solver threads. Default 1L.
lp_iteration_limit	Integer; LP iteration limit. Default -1L.
lp_scaling	Logical; LP scaling. Default TRUE.
branching_score	Character; score function. Default "p".
heuristics_emphasis	Character; heuristic emphasis. Default "default".
separating_emphasis	Character; separating emphasis. Default "default".
emphasis	Character; global solver emphasis. Default "default". See the Emphasis section.
threads	Integer; parallel solving threads. Default 1L. See Parallel section for caveats.
...	Additional SCIP parameters as name-value pairs, using SCIP's native hierarchical parameter paths (e.g., "lp/fastmip" = 1, "conflict/enable" = FALSE). See the SCIP parameter documentation for the full list.

Details

The four emphasis arguments (emphasis, presolve_emphasis, heuristics_emphasis, separating_emphasis) are SCIP meta-settings: each one adjusts a whole family of native parameters at once, the same way SCIP's interactive shell commands set emphasis, set presolving emphasis, set heuristics emphasis and set separating emphasis do. They are applied before any individual parameter, in that order, so a native parameter given via ... always overrides whatever the emphasis chose for it.

Value

A named list of class "scip_control" with components:

verbose

Logical; verbosity flag.

scip_params

Named list; all parameters as SCIP native paths.

Output

verbose

Logical; print solver output. Default TRUE.

verbosity_level

Integer 0–5; verbosity detail (0 = none, 3 = normal, 5 = full). Default 3. Ignored if verbose = FALSE.

display_freq

Integer; display a status line every this many nodes (-1 = never). Default 100.

Termination Limits

time_limit

Numeric; maximum solving time in seconds. Default Inf (no limit).

node_limit

Integer; maximum number of branch-and-bound nodes. Default -1L (no limit).

stall_node_limit

Integer; nodes without improvement before stopping. Default -1L (no limit).

sol_limit

Integer; stop after finding this many feasible solutions. Default -1L (no limit).

best_sol_limit

Integer; stop after this many improving solutions. Default -1L (no limit).

mem_limit

Numeric; memory limit in MB. Default Inf (no limit).

restart_limit

Integer; maximum restarts. Default -1L (no limit).

Tolerances

gap_limit

Numeric; relative MIP gap tolerance; solver stops when the gap falls below this value. Default 0 (prove optimality).

abs_gap_limit

Numeric; absolute gap between primal and dual bound. Default 0.

feastol

Numeric; feasibility tolerance for LP constraints. Default 1e-6.

dualfeastol

Numeric; dual feasibility tolerance. Default 1e-7.

epsilon

Numeric; absolute values below this are treated as zero. Default 1e-9.

Presolving

presolving

Logical; enable presolving. Default TRUE.

presolve_rounds

Integer; maximum presolving rounds (-1 = unlimited). Default -1L.

presolve_emphasis

Character; presolving emphasis setting: "default", "aggressive", "fast", or "off". Default "default".

LP

lp_threads

Integer; number of threads for LP solver. Default 1L.

lp_iteration_limit

Integer; LP iteration limit per solve (-1 = no limit). Default -1L.

lp_scaling

Logical; enable LP scaling. Default TRUE.

Branching

branching_score

Character; branching score function: "s" (sum), "p" (product), "q" (quotient). Default "p".

Heuristics

heuristics_emphasis

Character; heuristic emphasis setting: "default", "aggressive", "fast", or "off". Default "default".

Separating

separating_emphasis

Character; cutting-plane separation emphasis setting: "default", "aggressive", "fast", or "off". Default "default".

Emphasis

emphasis

Character; global solver emphasis, choosing a predefined parameter profile for a problem class or solving goal. One of "default", "cpsolver" (constraint-programming style search, no LP relaxation), "easycip" (solve easy problems fast), "feasibility" (find a feasible solution fast), "hardlp" (cope with hard LP relaxations), "optimality" (prove optimality fast), "counter" (solution counting), "phasefeas", "phaseimprove", "phaseproof" (the three phases of SCIP's three-phase approach), "numerics" (increased numerical safety), or "benchmark" (do not try to avoid the memory limit). Default "default".

Parallel

threads

Integer; number of threads for concurrent solving. Default 1L. Note: concurrent solving may require a SCIP build compiled with parallel support (e.g., PARASCIP=true); not all installations provide this.

See Also

scip_solve, scip_set_param

Examples

## Quick solve with 60-second time limit
ctrl <- scip_control(time_limit = 60)

## Quiet solve with 1% gap tolerance
ctrl <- scip_control(verbose = FALSE, gap_limit = 0.01)

## Aggressive heuristics, no presolving
ctrl <- scip_control(heuristics_emphasis = "aggressive", presolving = FALSE)

## Aggressive presolving and separation, as for hard combinatorial MIPs
ctrl <- scip_control(presolve_emphasis = "aggressive",
                     separating_emphasis = "aggressive")

## Global profile: find a feasible solution quickly
ctrl <- scip_control(emphasis = "feasibility")

## Pass a native SCIP parameter directly
ctrl <- scip_control("conflict/enable" = FALSE, "separating/maxrounds" = 5L)

---

Get solver information

Description

Get solver information

Usage

scip_get_info(model)

Arguments

model

A SCIP model (after scip_optimize).

Value

A list with solve_time, nodes, iterations, gap, sol_count.

---

Get number of solutions found

Description

Get number of solutions found

Usage

scip_get_nsols(model)

Arguments

model

A SCIP model (after scip_optimize).

Value

Integer.

---

Get objective value of best solution

Description

Get objective value of best solution

Usage

scip_get_objval(model)

Arguments

model

A SCIP model (after scip_optimize).

Value

Numeric; objective value, or NA if no solution.

---

Get the k-th solution from the solution pool

Description

Get the k-th solution from the solution pool

Usage

scip_get_sol(model, k)

Arguments

model	A SCIP model.
k	Integer; 1-based solution index (1 = best).

Value

A list with objval and x.

---

Get the best solution

Description

Get the best solution

Usage

scip_get_solution(model)

Arguments

model

A SCIP model (after scip_optimize).

Value

A list with objval and x.

---

Get solver status

Description

Get solver status

Usage

scip_get_status(model)

Arguments

model

A SCIP model (after scip_optimize).

Value

Character; status string (e.g., "optimal", "infeasible").

---

Create a SCIP model

Description

Creates a new SCIP optimization model for incremental problem construction.

Usage

scip_model(name = "scip_model")

Arguments

name	Character; problem name. Default "scip_model".

Value

An external pointer representing the SCIP model.

---

Free a SCIP model

Description

Explicitly frees the SCIP model and all associated memory. The model is also freed automatically when garbage collected.

Usage

scip_model_free(model)

Arguments

model

A SCIP model.

Value

Invisible NULL.

---

Solve a SCIP model

Description

Solve a SCIP model

Usage

scip_optimize(model)

Arguments

model

A SCIP model.

Value

Invisible NULL. Use scip_get_status and scip_get_solution to retrieve results.

---

Set objective sense

Description

Set objective sense

Usage

scip_set_objective_sense(model, sense = "minimize")

Arguments

model	A SCIP model.
sense	Character; "minimize" (default) or "maximize".

Value

Invisible NULL.

---

Set a SCIP parameter

Description

Set a SCIP parameter

Usage

scip_set_param(model, name, value)

Arguments

model	A SCIP model.
name	Character; SCIP parameter name (e.g., "limits/time").
value	The parameter value (type is auto-detected by SCIP).

Value

Invisible NULL.

---

Solve a linear or mixed-integer program using SCIP

Description

One-shot interface to the SCIP solver. Formulates and solves:

$\min_{x} \; obj' x$

subject to constraint rows defined by A, b, sense, with variable types vtype and bounds lb, ub.

Usage

scip_solve(obj, A, b, sense, vtype = "C", lb = 0, ub = Inf, control = list())

Arguments

obj	Numeric vector of length n; objective coefficients.
A	Constraint matrix (m x n). Can be a dense matrix, dgCMatrix, or simple_triplet_matrix.
b	Numeric vector of length m; constraint right-hand side.
sense	Character vector of length m; constraint sense. Each element must be "<=", ">=", or "==".
vtype	Character; variable types. Either a single value applied to all variables, or a vector of length n. Values: "C" (continuous), "B" (binary), "I" (integer). Default "C".
lb	Numeric; lower bounds for variables. Single value or vector of length n. Default 0.
ub	Numeric; upper bounds for variables. Single value or vector of length n. Default Inf.
control	A list of solver parameters, typically from scip_control.

Value

A named list with components:

status

Character; solver status (e.g., "optimal", "infeasible", "unbounded").

objval

Numeric; optimal objective value (or NA if no solution).

x

Numeric vector; primal solution (or NULL if no solution).

sol_count

Integer; number of solutions found.

gap

Numeric; relative optimality gap.

info

List with additional solver information (solve_time, iterations, nodes).

---