Help for package scip

Title:

Interface to the SCIP Optimization Suite

Version:

1.10.0-2

Description:

Provides an R interface to SCIP (Solving Constraint Integer Programs), a framework for mixed-integer programming (MIP), mixed-integer nonlinear programming (MINLP), and constraint integer programming (2025, <doi:10.48550/arXiv.2511.18580>). Supports linear, quadratic, SOS, indicator, and knapsack constraints with continuous, binary, and integer variables. Includes a one-shot solver interface and a model-building API for incremental problem construction.

License:

Apache License (≥ 2)

URL:

https://github.com/bnaras/scip, https://scipopt.org/

BugReports:

https://github.com/bnaras/scip/issues

Depends:

R (≥ 4.0)

Imports:

methods, Matrix

Suggests:

tinytest, slam, knitr, rmarkdown

VignetteBuilder:

knitr

SystemRequirements:

CMake (>= 3.11), GNU make, C++17

Encoding:

UTF-8

RoxygenNote:

7.3.3

NeedsCompilation:

yes

Packaged:

2026-03-31 01:10:17 UTC; naras

Author:

Balasubramanian Narasimhan

[aut, cre], SCIP Optimization Suite Authors [cph] (SCIP, SoPlex, PaPILO libraries)

Maintainer:

Balasubramanian Narasimhan <naras@stanford.edu>

Repository:

CRAN

Date/Publication:

2026-04-03 09:00:02 UTC

scip: R Interface to the SCIP Optimization Suite

Description

Provides an R interface to SCIP (Solving Constraint Integer Programs), a framework for mixed-integer programming (MIP), mixed-integer nonlinear programming (MINLP), and constraint integer programming. Supports linear, quadratic, SOS, indicator, and knapsack constraints with continuous, binary, and integer variables. Includes a one-shot solver interface and a model-building API for incremental problem construction.

Author(s)

Maintainer: Balasubramanian Narasimhan naras@stanford.edu (ORCID)

Other contributors:

SCIP Optimization Suite Authors (SCIP, SoPlex, PaPILO libraries) [copyright holder]

Convert a matrix to CSC (Compressed Sparse Column) format

Description

Convert a matrix to CSC (Compressed Sparse Column) format

Usage

make_csc_matrix(x)

Arguments

x

A matrix, dgCMatrix, or simple_triplet_matrix

Value

A list with components i (row indices, 0-based), p (column pointers, 0-based), x (values), nrow, ncol

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, and heuristics. 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,
  lp_threads = 1L,
  lp_iteration_limit = -1L,
  lp_scaling = TRUE,
  branching_score = "p",
  heuristics_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.

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".

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.

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.

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".

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.

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)

## 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).

scip: R Interface to the SCIP Optimization Suite

Description

Author(s)

See Also

Convert a matrix to CSC (Compressed Sparse Column) format

Description

Usage

Arguments

Value

Print method for scip_control objects

Description

Usage

Arguments

Value

Add an indicator constraint to a SCIP model

Description

Usage

Arguments

Value

Add a linear constraint to a SCIP model

Description

Usage

Arguments

Value

Add a quadratic constraint to a SCIP model

Description

Usage

Arguments

Value

Add a SOS1 constraint to a SCIP model

Description

Usage

Arguments

Value

Add a SOS2 constraint to a SCIP model

Description

Usage

Arguments

Value

Add a variable to a SCIP model

Description

Usage

Arguments

Value

Add multiple variables to a SCIP model

Description

Usage

Arguments

Value

SCIP solver control parameters

Description

Usage

Arguments

Value

Output

Termination Limits

Tolerances

Presolving

LP

Branching

Heuristics

Parallel

See Also

Examples

Get solver information

Description

Usage

Arguments

Value

Get number of solutions found

Description

Usage

Arguments

Value

Get objective value of best solution

Description

Usage

Arguments

Value

Get the k-th solution from the solution pool