Struct LinearModel

pub struct LinearModel { /* private fields */ }

Represents a complete linear programming model including variables, constraints, and objective function.

Example

use rooc::{OptimizationType, VariableType, Comparison, LinearModel};
use rooc::model_transformer::DomainVariable;
let mut model = LinearModel::new();

// Add variables
model.add_variable("x", VariableType::non_negative_real());
model.add_variable("y", VariableType::non_negative_real());

// Set objective function: minimize x + 2y
model.set_objective(vec![1.0, 2.0], OptimizationType::Min);

// Add constraint: x + y <= 10
model.add_constraint(vec![1.0, 1.0], Comparison::LessOrEqual, 10.0);

Implementations

impl LinearModel

pub fn new_from_parts( objective: Vec<f64>, optimization_type: OptimizationType, objective_offset: f64, constraints: Vec<LinearConstraint>, variables: Vec<String>, domain: IndexMap<String, DomainVariable>, ) -> LinearModel

Creates a new LinearModel from its constituent parts.

Arguments

• objective - Vector of objective function coefficients
• optimization_type - Whether to minimize or maximize
• objective_offset - Constant term in objective function
• constraints - Vector of linear constraints
• variables - Vector of variable names
• domain - Map of variable domains

pub fn new() -> LinearModel

Creates a new empty LinearModel.

pub fn into_parts( self, ) -> (Vec<f64>, OptimizationType, f64, Vec<LinearConstraint>, Vec<String>, IndexMap<String, DomainVariable>)

Decomposes the model into its constituent parts.

Returns

A tuple containing (objective coefficients, optimization type, objective offset, constraints, variables, domain)

pub fn add_variable(&mut self, name: &str, domain: VariableType)

Adds a new variable to the model.

Arguments

• name - Name of the variable
• domain - Type/domain of the variable (e.g., Boolean, Integer, etc.)

pub fn add_constraint( &mut self, coefficients: Vec<f64>, constraint_type: Comparison, rhs: f64, )

Adds a new constraint to the model.

Arguments

• coefficients - Vector of coefficients for the constraint
• constraint_type - Type of comparison operator
• rhs - Right-hand side value

Returns

• Ok(()) if successful
• Err(LinearModelError) if too many coefficients provided

Panics

If there are more coefficient than how many variables there are

pub fn set_objective( &mut self, objective: Vec<f64>, optimization_type: OptimizationType, )

Sets the objective function of the model.

Arguments

• objective - Vector of objective function coefficients
• optimization_type - Whether to minimize or maximize

Returns

• Ok(()) if successful
• Err(LinearModelError) if too many coefficients provided

Panics

If there are more coefficient than how many variables there are

pub fn optimization_type(&self) -> &OptimizationType

Returns the optimization type (minimize/maximize).

pub fn into_standard_form(self) -> Result<StandardLinearModel, SolverError>

Converts the model to standard form.

pub fn objective(&self) -> &Vec<f64>

Returns a reference to the objective function coefficients.

pub fn constraints(&self) -> &Vec<LinearConstraint>

Returns a reference to the model’s constraints.

pub fn variables(&self) -> &Vec<String>

Returns a reference to the variable names.

pub fn objective_offset(&self) -> f64

Returns the constant term in the objective function.

pub fn domain(&self) -> &IndexMap<String, DomainVariable>

Returns a reference to the variable domains.

Trait Implementations

impl Clone for LinearModel

fn clone(&self) -> LinearModel

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for LinearModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for LinearModel

fn default() -> Self

Returns the “default value” for a type.

impl Display for LinearModel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.