Struct Problem

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pub struct Problem { /* private fields */ }

Expand description

A specification of a linear programming problem.

Implementations§

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impl Problem

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pub fn new(direction: OptimizationDirection) -> Self

Create a new problem instance.

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pub fn add_var(&mut self, obj_coeff: f64, (min, max): (f64, f64)) -> Variable

Add a new real variable to the problem.

obj_coeff is a coefficient of the term in the objective function corresponding to this variable, min and max are the minimum and maximum (inclusive) bounds of this variable. If one of the bounds is absent, use f64::NEG_INFINITY for minimum and f64::INFINITY for maximum.

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pub fn add_integer_var( &mut self, obj_coeff: f64, (min, max): (i32, i32), ) -> Variable

Add a new integer variable to the problem.

obj_coeff is a coefficient of the term in the objective function corresponding to this variable, min and max are the minimum and maximum (inclusive) bounds of this variable. If one of the bounds is absent, use f64::NEG_INFINITY for minimum and f64::INFINITY for maximum.

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pub fn has_integer_vars(&self) -> bool

Check if the problem has any integer variables.

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pub fn add_binary_var(&mut self, obj_coeff: f64) -> Variable

Add a new binary variable to the problem.

obj_coeff is a coefficient of the term in the objective function corresponding to this variable.

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pub fn add_constraint( &mut self, expr: impl Into<LinearExpr>, cmp_op: ComparisonOp, rhs: f64, )

Add a linear constraint to the problem.

§Panics

Will panic if a variable was added more than once to the left-hand side expression.

§Examples

Left-hand side of the constraint can be specified in several ways:

let mut problem = Problem::new(OptimizationDirection::Minimize);
let x = problem.add_var(1.0, (0.0, f64::INFINITY));
let y = problem.add_var(1.0, (0.0, f64::INFINITY));

// Add an x + y >= 2 constraint, specifying the left-hand side expression:

// * by passing a slice of pairs (useful when explicitly enumerating variables)
problem.add_constraint(&[(x, 1.0), (y, 1.0)], ComparisonOp::Ge, 2.0);

// * by passing an iterator of variable-coefficient pairs.
let vars = [x, y];
problem.add_constraint(vars.iter().map(|&v| (v, 1.0)), ComparisonOp::Ge, 2.0);

// * by manually constructing a LinearExpr.
let mut lhs = LinearExpr::empty();
for &v in &vars {
    lhs.add(v, 1.0);
}
problem.add_constraint(lhs, ComparisonOp::Ge, 2.0);