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//! A [Variable] is the base element used to create an [Expression].
//! The goal of the solver is to find optimal values for all variables in a problem.
//!
//! Each variable has a [VariableDefinition] that sets its bounds.
use std::collections::Bound;
use std::fmt::{Debug, Display, Formatter};
use std::hash::Hash;
use std::ops::{Div, Mul, Neg, Not, RangeBounds};
use fnv::FnvHashMap as HashMap;
use crate::affine_expression_trait::IntoAffineExpression;
use crate::expression::{Expression, LinearExpression};
use crate::solvers::{ObjectiveDirection, Solver};
/// A variable in a problem. Use variables to create [expressions](Expression),
/// to express the [objective](ProblemVariables::optimise)
/// and the [Constraints](crate::Constraint) of your model.
///
/// Variables are created using [ProblemVariables::add]. They implement std::ops basic math operations on f64 and i32 values.
///
/// ## Warning
/// `Eq` is implemented on this type, but
/// `v1 == v2` is true only if the two variables represent the same object,
/// not if they have the same definition.
///
/// ```
/// # use good_lp::{variable, variables};
/// let mut vars = variables!();
/// let v1 = vars.add(variable().min(1).max(8));
/// let v2 = vars.add(variable().min(1).max(8));
/// assert_ne!(v1, v2);
///
/// let v1_copy = v1;
/// assert_eq!(v1, v1_copy);
/// ```
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub struct Variable {
/// A variable is nothing more than an index into the `variables` field of a ProblemVariables
/// That's why it can be `Copy`.
/// All the actual information about the variable (name, type, bounds, ...) is stored in ProblemVariables
index: usize,
}
impl IntoAffineExpression for Variable {
type Iter = std::iter::Once<(Self, f64)>;
#[inline]
fn linear_coefficients(self) -> Self::Iter {
std::iter::once((self, 1.))
}
}
/// Use an optional variable as an expression
///
/// ```
/// # use good_lp::variables;
/// variables! {problem: 0 <= v};
/// let maybe = Some(v);
/// problem.minimise(v + maybe);
/// ```
impl IntoAffineExpression for Option<Variable> {
#[allow(clippy::type_complexity)]
type Iter = std::iter::Map<std::option::IntoIter<Variable>, fn(Variable) -> (Variable, f64)>;
#[inline]
fn linear_coefficients(self) -> Self::Iter {
self.into_iter().map(|v| (v, 1.))
}
}
impl<'a> IntoAffineExpression for &'a Variable {
type Iter = std::iter::Once<(Variable, f64)>;
#[inline]
fn linear_coefficients(self) -> Self::Iter {
(*self).linear_coefficients()
}
}
impl Variable {
/// No one should use this method outside of [VariableDefinition]
fn at(index: usize) -> Self {
Self { index }
}
}
impl Variable {
pub(super) fn index(&self) -> usize {
self.index
}
}
/// An element that can be displayed if you give a variable display function
pub trait FormatWithVars {
/// Write the element to the formatter. See [std::fmt::Display]
fn format_with<FUN>(&self, f: &mut Formatter<'_>, variable_format: FUN) -> std::fmt::Result
where
FUN: FnMut(&mut Formatter<'_>, Variable) -> std::fmt::Result;
/// Write the elements, naming the variables v0, v1, ... vn
fn format_debug(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
self.format_with(f, |f, var| write!(f, "v{}", var.index()))
}
}
impl FormatWithVars for Variable {
fn format_with<FUN>(&self, f: &mut Formatter<'_>, mut variable_format: FUN) -> std::fmt::Result
where
FUN: FnMut(&mut Formatter<'_>, Variable) -> std::fmt::Result,
{
variable_format(f, *self)
}
}
/// Defines the properties of a variable, such as its lower and upper bounds.
#[derive(Clone, PartialEq, Debug)]
pub struct VariableDefinition {
pub(crate) min: f64,
pub(crate) max: f64,
pub(crate) name: String,
pub(crate) is_integer: bool,
}
impl VariableDefinition {
/// Creates an unbounded continuous linear variable
pub fn new() -> Self {
VariableDefinition {
min: f64::NEG_INFINITY,
max: f64::INFINITY,
name: String::new(),
is_integer: false,
}
}
/// Define the variable as an integer.
/// The variable will only be able to take an integer value in the solution.
///
/// **Warning**: not all solvers support integer variables.
/// Refer to the documentation of the solver you are using.
///
/// ```
/// # use good_lp::{ProblemVariables, variable, default_solver, SolverModel, Solution};
/// let mut problem = ProblemVariables::new();
/// let x = problem.add(variable().integer().min(0).max(2.5));
/// # if cfg!(not(any(feature = "minilp", feature="clarabel"))) {
/// let solution = problem.maximise(x).using(default_solver).solve().unwrap();
/// // x is bound to [0; 2.5], but the solution is x=2 because x needs to be an integer
/// assert_eq!(solution.value(x), 2.);
/// # }
/// ```
pub fn integer(mut self) -> Self {
self.is_integer = true;
self
}
/// Define the variable as an integer that can only take the value 0 or 1.
///
/// **Warning**: not all solvers support integer variables.
/// Refer to the documentation of the solver you are using.
///
/// ```
/// # use good_lp::{ProblemVariables, variable, default_solver, SolverModel, Solution};
/// let mut problem = ProblemVariables::new();
/// let x = problem.add(variable().binary());
/// let y = problem.add(variable().binary());
/// if cfg!(not(any(feature = "minilp", feature="clarabel"))) {
/// let solution = problem.maximise(x + y).using(default_solver).solve().unwrap();
/// assert_eq!(solution.value(x), 1.);
/// assert_eq!(solution.value(y), 1.);
/// }
/// ```
pub fn binary(mut self) -> Self {
self.is_integer = true;
self.min = 0.;
self.max = 1.;
self
}
/// Set the name of the variable. This is useful in particular when displaying the problem
/// for debugging purposes.
///
/// ```
/// # use good_lp::{ProblemVariables, variable};
/// let mut pb = ProblemVariables::new();
/// let x = pb.add(variable().name("x"));
/// assert_eq!("x", pb.display(&x).to_string());
/// ```
pub fn name<S: Into<String>>(mut self, name: S) -> Self {
self.name = name.into();
self
}
/// Set the lower and/or higher bounds of the variable
///
/// ## Examples
/// ```
/// # use good_lp::variable;
/// assert_eq!(
/// variable().bounds(1..2),
/// variable().min(1).max(2)
/// );
///
/// assert_eq!(
/// variable().bounds(1..),
/// variable().min(1)
/// );
///
/// assert_eq!(
/// variable().bounds(..=2),
/// variable().max(2)
/// );
///
/// # assert_eq!(variable().bounds::<f64, _>(..), variable());
/// ```
pub fn bounds<N: Into<f64> + Copy, B: RangeBounds<N>>(self, bounds: B) -> Self {
self.min(match bounds.start_bound() {
Bound::Included(&x) => x.into(),
Bound::Excluded(&x) => x.into(),
Bound::Unbounded => f64::NEG_INFINITY,
})
.max(match bounds.end_bound() {
Bound::Included(&x) => x.into(),
Bound::Excluded(&x) => x.into(),
Bound::Unbounded => f64::INFINITY,
})
}
/// Set the lower bound of the variable
pub fn min<N: Into<f64>>(mut self, min: N) -> Self {
self.min = min.into();
self
}
/// Set the higher bound of the variable
pub fn max<N: Into<f64>>(mut self, max: N) -> Self {
self.max = max.into();
self
}
/// Set both the lower and higher bounds of the variable
pub fn clamp<N1: Into<f64>, N2: Into<f64>>(self, min: N1, max: N2) -> Self {
self.min(min).max(max)
}
}
/// Creates an unbounded continuous linear variable
impl Default for VariableDefinition {
fn default() -> Self {
VariableDefinition::new()
}
}
/// Returns an anonymous unbounded continuous variable definition
pub fn variable() -> VariableDefinition {
VariableDefinition::default()
}
/// Represents the variables for a given problem.
/// Each problem has a unique type, which prevents using the variables
/// from one problem inside an other one.
/// Instances of this type should be created exclusively using the [crate::variables!] macro.
#[derive(Default)]
pub struct ProblemVariables {
variables: Vec<VariableDefinition>,
}
impl ProblemVariables {
/// Create an empty list of variables
pub fn new() -> Self {
ProblemVariables { variables: vec![] }
}
/// Add a anonymous unbounded continuous variable to the problem
pub fn add_variable(&mut self) -> Variable {
self.add(variable())
}
/// Add a variable with the given definition
///
/// ```
/// # use good_lp::*;
/// variables!{problem: y >= 0;}
/// ```
/// is equivalent to
/// ```
/// # use good_lp::*;
/// let mut problem = ProblemVariables::new();
/// let y = problem.add(variable().min(0));
/// ```
pub fn add(&mut self, var_def: VariableDefinition) -> Variable {
let index = self.variables.len();
self.variables.push(var_def);
Variable::at(index)
}
/// Adds a list of variables with the given definition
///
/// ```
/// use good_lp::*;
/// // Solve a problem with 11 variables: x, y0, y1, ..., y9
/// variables!{problem: 2 <= x <= 3;}
/// let y: Vec<Variable> = problem.add_vector(variable().min(0), 10);
/// let objective: Expression = y.iter().sum(); // Minimise sum(y_i for i in [0; 9])
/// let mut model = problem.minimise(objective).using(default_solver);
/// // for all i, we must have y_i >= x
/// for y_i in y.iter() {
/// model = model.with(constraint!(y_i >= x));
/// }
/// let solution = model.solve().unwrap();
/// # use float_eq::assert_float_eq;
/// assert_float_eq!(solution.value(y[3]), 2., abs <= 1e-8);
/// ```
pub fn add_vector(&mut self, var_def: VariableDefinition, len: usize) -> Vec<Variable> {
(0..len).map(|_i| self.add(var_def.clone())).collect()
}
/// Creates an optimization problem with the given objective. Don't solve it immediately.
///
/// ```
/// use good_lp::{variables, variable, default_solver, SolverModel, Solution};
/// use good_lp::solvers::ObjectiveDirection;
/// fn solve(sense: ObjectiveDirection) -> f64 {
/// variables!{problem: 2 <= x <= 3;}
/// let solution = problem.optimise(sense, x).using(default_solver).solve().unwrap();
/// solution.value(x)
/// }
///
/// # use float_eq::assert_float_eq;
/// assert_float_eq!(solve(ObjectiveDirection::Minimisation), 2., abs<=1e-8);
/// assert_float_eq!(solve(ObjectiveDirection::Maximisation), 3., abs<=1e-8);
/// ```
pub fn optimise<E: IntoAffineExpression>(
self,
direction: ObjectiveDirection,
objective: E,
) -> UnsolvedProblem {
let objective = Expression::from_other_affine(objective);
assert!(
objective.linear.coefficients.len() <= self.variables.len(),
"There should not be more variables in the objective function than in the problem. \
You probably used variables from a different problem in this one."
);
UnsolvedProblem {
objective,
direction,
variables: self,
}
}
/// Creates an maximization problem with the given objective. Don't solve it immediately
///
/// ```
/// use good_lp::{variables, variable, default_solver, SolverModel, Solution};
/// variables!{problem: x <= 7;}
/// let solution = problem.maximise(x).using(default_solver).solve().unwrap();
/// # use float_eq::assert_float_eq;
/// assert_float_eq!(solution.value(x), 7., abs <= 1e-8);
/// ```
pub fn maximise<E: IntoAffineExpression>(self, objective: E) -> UnsolvedProblem {
self.optimise(ObjectiveDirection::Maximisation, objective)
}
/// Creates an minimization problem with the given objective. Don't solve it immediately
/// ```
/// use good_lp::{variables, variable, default_solver, SolverModel, Solution};
/// variables!{problem: x >= -8;}
/// let solution = problem.minimise(x).using(default_solver).solve().unwrap();
/// # use float_eq::assert_float_eq;
/// assert_float_eq!(solution.value(x), -8., abs <= 1e-8);
/// ```
pub fn minimise<E: IntoAffineExpression>(self, objective: E) -> UnsolvedProblem {
self.optimise(ObjectiveDirection::Minimisation, objective)
}
/// Iterates over the couples of variables with their properties
pub fn iter_variables_with_def(&self) -> impl Iterator<Item = (Variable, &VariableDefinition)> {
self.variables
.iter()
.enumerate()
.map(|(i, def)| (Variable::at(i), def))
}
/// The number of variables
pub fn len(&self) -> usize {
self.variables.len()
}
/// Returns true when no variables have been added
pub fn is_empty(&self) -> bool {
self.variables.is_empty()
}
/// Display the given expression or constraint with the correct variable names
///
/// ```
/// use good_lp::variables;
/// variables! {problem: 0 <= x; 0 <= y;}
/// let expression = x + 2*y;
/// let str = problem.display(&expression).to_string();
/// assert!(str == "x + 2 y" || str == "2 y + x"); // The ordering is not guaranteed
/// ```
pub fn display<'a, V: FormatWithVars>(&'a self, value: &'a V) -> impl Display + 'a {
DisplayExpr {
problem: self,
value,
}
}
}
struct DisplayExpr<'a, 'b, V> {
problem: &'a ProblemVariables,
value: &'b V,
}
impl<'a, 'b, V: FormatWithVars> Display for DisplayExpr<'a, 'b, V> {
fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
self.value.format_with(f, |f, var| {
let mut name = &self.problem.variables[var.index].name;
let alternative_name: String;
if name.is_empty() {
alternative_name = format!("v{}", var.index);
name = &alternative_name;
}
write!(f, "{}", name)
})
}
}
impl IntoIterator for ProblemVariables {
type Item = VariableDefinition;
type IntoIter = std::vec::IntoIter<VariableDefinition>;
fn into_iter(self) -> Self::IntoIter {
self.variables.into_iter()
}
}
/// A problem without constraints.
/// Created with [ProblemVariables::optimise].
pub struct UnsolvedProblem {
pub(crate) objective: Expression,
pub(crate) direction: ObjectiveDirection,
pub(crate) variables: ProblemVariables,
}
impl UnsolvedProblem {
/// Create a solver instance and feed it with this problem
pub fn using<S: Solver>(self, mut solver: S) -> S::Model {
solver.create_model(self)
}
}
impl<N: Into<f64>> Mul<N> for Variable {
type Output = Expression;
fn mul(self, rhs: N) -> Self::Output {
let mut coefficients = HashMap::with_capacity_and_hasher(1, Default::default());
coefficients.insert(self, rhs.into());
Expression {
linear: LinearExpression { coefficients },
constant: 0.0,
}
}
}
impl Mul<Variable> for f64 {
type Output = Expression;
fn mul(self, rhs: Variable) -> Self::Output {
let mut coefficients = HashMap::with_capacity_and_hasher(1, Default::default());
coefficients.insert(rhs, self);
Expression {
linear: LinearExpression { coefficients },
constant: 0.0,
}
}
}
impl Mul<Variable> for i32 {
type Output = Expression;
fn mul(self, rhs: Variable) -> Self::Output {
rhs.mul(f64::from(self))
}
}
impl Div<f64> for Variable {
type Output = Expression;
fn div(self, rhs: f64) -> Self::Output {
self * (1. / rhs)
}
}
impl Div<i32> for Variable {
type Output = Expression;
fn div(self, rhs: i32) -> Self::Output {
self * (1. / f64::from(rhs))
}
}
impl Neg for Variable {
type Output = Expression;
fn neg(self) -> Self::Output {
-Expression::from(self)
}
}
/// Useful for binary variables. `!x` is equivalent to `1-x`.
///
/// ```
/// #[cfg(any(feature = "coin_cbc", feature = "lpsolve"))] {
/// use good_lp::*;
/// variables! {pb: x (binary); y (binary); }
/// let solution = pb.maximise(!x + y)
/// .using(default_solver)
/// .solve().unwrap();
/// assert_eq!(solution.value(x), 0.);
/// assert_eq!(solution.value(y), 1.);
/// # }
/// ```
impl Not for Variable {
type Output = Expression;
fn not(self) -> Self::Output {
1. - self
}
}