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#![forbid(missing_docs)]
//! Safe rust binding to the [HiGHS](https://highs.dev) linear programming solver.
//!
//! ## Usage example
//!
//! ### Building a problem constraint by constraint with [RowProblem]
//!
//! Useful for traditional problem modelling where you first declare your variables, then add
//!constraints one by one.
//!
//! ```
//! use highs::{Sense, Model, HighsModelStatus, RowProblem};
//! // max: x + 2y + z
//! // under constraints:
//! // c1: 3x + y <= 6
//! // c2: y + 2z <= 7
//! let mut pb = RowProblem::default();
//! // Create a variable named x, with a coefficient of 1 in the objective function,
//! // that is bound between 0 and +∞.
//! let x = pb.add_column(1., 0..);
//! let y = pb.add_column(2., 0..);
//! let z = pb.add_column(1., 0..);
//! // constraint c1: x*3 + y*1 is bound to ]-∞; 6]
//! pb.add_row(..=6, &[(x, 3.), (y, 1.)]);
//! // constraint c2: y*1 + z*2 is bound to ]-∞; 7]
//! pb.add_row(..=7, &[(y, 1.), (z, 2.)]);
//!
//! let solved = pb.optimise(Sense::Maximise).solve();
//!
//! assert_eq!(solved.status(), HighsModelStatus::Optimal);
//!
//! let solution = solved.get_solution();
//! // The expected solution is x=0 y=6 z=0.5
//! assert_eq!(solution.columns(), vec![0., 6., 0.5]);
//! // All the constraints are at their maximum
//! assert_eq!(solution.rows(), vec![6., 7.]);
//! ```
//!
//! ### Building a problem variable by variable with [ColProblem]
//!
//! Useful for resource allocation problems and other problems when you know in advance the number
//! of constraints and their bounds, but dynamically add new variables to the problem.
//!
//! This is slightly more efficient than building the problem constraint by constraint.
//!
//! ```
//! use highs::{ColProblem, Sense};
//! let mut pb = ColProblem::new();
//! // We cannot use more then 5 units of sugar in total.
//! let sugar = pb.add_row(..=5);
//! // We cannot use more then 3 units of milk in total.
//! let milk = pb.add_row(..=3);
//! // We have a first cake that we can sell for 2€. Baking it requires 1 unit of milk and 2 of sugar.
//! pb.add_integer_column(2., 0.., &[(sugar, 2.), (milk, 1.)]);
//! // We have a second cake that we can sell for 8€. Baking it requires 2 units of milk and 3 of sugar.
//! pb.add_integer_column(8., 0.., &[(sugar, 3.), (milk, 2.)]);
//! // Find the maximal possible profit
//! let solution = pb.optimise(Sense::Maximise).solve().get_solution();
//! // The solution is to bake 1 cake of each sort
//! assert_eq!(solution.columns(), vec![1., 1.]);
//! ```
//!
//! ```
//! use highs::{Sense, Model, HighsModelStatus, ColProblem};
//! // max: x + 2y + z
//! // under constraints:
//! // c1: 3x + y <= 6
//! // c2: y + 2z <= 7
//! let mut pb = ColProblem::default();
//! let c1 = pb.add_row(..6.);
//! let c2 = pb.add_row(..7.);
//! // x
//! pb.add_column(1., 0.., &[(c1, 3.)]);
//! // y
//! pb.add_column(2., 0.., &[(c1, 1.), (c2, 1.)]);
//! // z
//! pb.add_column(1., 0.., vec![(c2, 2.)]);
//!
//! let solved = pb.optimise(Sense::Maximise).solve();
//!
//! assert_eq!(solved.status(), HighsModelStatus::Optimal);
//!
//! let solution = solved.get_solution();
//! // The expected solution is x=0 y=6 z=0.5
//! assert_eq!(solution.columns(), vec![0., 6., 0.5]);
//! // All the constraints are at their maximum
//! assert_eq!(solution.rows(), vec![6., 7.]);
//! ```
//!
//! ### Integer variables
//!
//! HiGHS supports mixed integer-linear programming.
//! You can use `add_integer_column` to add an integer variable to the problem,
//! and the solution is then guaranteed to contain a whole number as a value for this variable.
//!
//! ```
//! use highs::{Sense, Model, HighsModelStatus, ColProblem};
//! // maximize: x + 2y under constraints x + y <= 3.5 and x - y >= 1
//! let mut pb = ColProblem::default();
//! let c1 = pb.add_row(..3.5);
//! let c2 = pb.add_row(1..);
//! // x (continuous variable)
//! pb.add_column(1., 0.., &[(c1, 1.), (c2, 1.)]);
//! // y (integer variable)
//! pb.add_integer_column(2., 0.., &[(c1, 1.), (c2, -1.)]);
//! let solved = pb.optimise(Sense::Maximise).solve();
//! // The expected solution is x=2.5 y=1
//! assert_eq!(solved.get_solution().columns(), vec![2.5, 1.]);
//! ```
use std::convert::{TryFrom, TryInto};
use std::ffi::{c_void, CString};
use std::num::TryFromIntError;
use std::ops::{Bound, RangeBounds, Index};
use std::os::raw::c_int;
use highs_sys::*;
pub use matrix_col::{ColMatrix, Row};
pub use matrix_row::{Col, RowMatrix};
pub use status::{HighsModelStatus, HighsStatus};
use crate::options::HighsOptionValue;
/// A problem where variables are declared first, and constraints are then added dynamically.
/// See [`Problem<RowMatrix>`](Problem#impl-1).
pub type RowProblem = Problem<RowMatrix>;
/// A problem where constraints are declared first, and variables are then added dynamically.
/// See [`Problem<ColMatrix>`](Problem#impl).
pub type ColProblem = Problem<ColMatrix>;
mod matrix_col;
mod matrix_row;
mod options;
mod status;
/// A complete optimization problem.
/// Depending on the `MATRIX` type parameter, the problem will be built
/// constraint by constraint (with [ColProblem]), or
/// variable by variable (with [RowProblem])
#[derive(Debug, Clone, PartialEq, Default)]
pub struct Problem<MATRIX = ColMatrix> {
// columns
colcost: Vec<f64>,
collower: Vec<f64>,
colupper: Vec<f64>,
// rows
rowlower: Vec<f64>,
rowupper: Vec<f64>,
integrality: Option<Vec<HighsInt>>,
matrix: MATRIX,
}
impl<MATRIX: Default> Problem<MATRIX>
where
Problem<ColMatrix>: From<Problem<MATRIX>>,
{
/// Number of variables in the problem
pub fn num_cols(&self) -> usize {
self.colcost.len()
}
/// Number of constraints in the problem
pub fn num_rows(&self) -> usize {
self.rowlower.len()
}
fn add_row_inner<N: Into<f64> + Copy, B: RangeBounds<N>>(&mut self, bounds: B) -> Row {
let r = Row(self.num_rows().try_into().expect("too many rows"));
let low = bound_value(bounds.start_bound()).unwrap_or(f64::NEG_INFINITY);
let high = bound_value(bounds.end_bound()).unwrap_or(f64::INFINITY);
self.rowlower.push(low);
self.rowupper.push(high);
r
}
fn add_column_inner<N: Into<f64> + Copy, B: RangeBounds<N>>(
&mut self,
col_factor: f64,
bounds: B,
is_integral: bool,
) {
if is_integral && self.integrality.is_none() {
self.integrality = Some(vec![0; self.num_cols()]);
}
if let Some(integrality) = &mut self.integrality {
integrality.push(if is_integral { 1 } else { 0 });
}
self.colcost.push(col_factor);
let low = bound_value(bounds.start_bound()).unwrap_or(f64::NEG_INFINITY);
let high = bound_value(bounds.end_bound()).unwrap_or(f64::INFINITY);
self.collower.push(low);
self.colupper.push(high);
}
/// Create a model based on this problem. Don't solve it yet.
/// If the problem is a [RowProblem], it will have to be converted to a [ColProblem] first,
/// which takes an amount of time proportional to the size of the problem.
/// If the problem is invalid (according to HiGHS), this function will panic.
pub fn optimise(self, sense: Sense) -> Model {
self.try_optimise(sense).expect("invalid problem")
}
/// Create a model based on this problem. Don't solve it yet.
/// If the problem is a [RowProblem], it will have to be converted to a [ColProblem] first,
/// which takes an amount of time proportional to the size of the problem.
pub fn try_optimise(self, sense: Sense) -> Result<Model, HighsStatus> {
let mut m = Model::try_new(self)?;
m.set_sense(sense);
Ok(m)
}
/// Create a new problem instance
pub fn new() -> Self {
Self::default()
}
}
fn bound_value<N: Into<f64> + Copy>(b: Bound<&N>) -> Option<f64> {
match b {
Bound::Included(v) | Bound::Excluded(v) => Some((*v).into()),
Bound::Unbounded => None,
}
}
fn c(n: usize) -> HighsInt {
n.try_into().expect("size too large for HiGHS")
}
macro_rules! highs_call {
($function_name:ident ($($param:expr),+)) => {
try_handle_status(
$function_name($($param),+),
stringify!($function_name)
)
}
}
/// A model to solve
#[derive(Debug)]
pub struct Model {
highs: HighsPtr,
}
/// A solved model
#[derive(Debug)]
pub struct SolvedModel {
highs: HighsPtr,
}
/// Whether to maximize or minimize the objective function
#[repr(C)]
#[derive(Clone, Copy, Eq, PartialEq, Debug)]
pub enum Sense {
/// max
Maximise = OBJECTIVE_SENSE_MAXIMIZE as isize,
/// min
Minimise = OBJECTIVE_SENSE_MINIMIZE as isize,
}
impl Model {
/// Set the optimization sense (minimize by default)
pub fn set_sense(&mut self, sense: Sense) {
let ret = unsafe { Highs_changeObjectiveSense(self.highs.mut_ptr(), sense as c_int) };
assert_eq!(ret, STATUS_OK, "changeObjectiveSense failed");
}
/// Create a Highs model to be optimized (but don't solve it yet).
/// If the given problem is a [RowProblem], it will have to be converted to a [ColProblem] first,
/// which takes an amount of time proportional to the size of the problem.
/// Panics if the problem is incoherent
pub fn new<P: Into<Problem<ColMatrix>>>(problem: P) -> Self {
Self::try_new(problem).expect("incoherent problem")
}
/// Create a Highs model to be optimized (but don't solve it yet).
/// If the given problem is a [RowProblem], it will have to be converted to a [ColProblem] first,
/// which takes an amount of time proportional to the size of the problem.
/// Returns an error if the problem is incoherent
pub fn try_new<P: Into<Problem<ColMatrix>>>(problem: P) -> Result<Self, HighsStatus> {
let mut highs = HighsPtr::default();
highs.make_quiet();
let problem = problem.into();
log::debug!(
"Adding a problem with {} variables and {} constraints to HiGHS",
problem.num_cols(),
problem.num_rows()
);
let offset = 0.0;
unsafe {
if let Some(integrality) = &problem.integrality {
highs_call!(Highs_passMip(
highs.mut_ptr(),
c(problem.num_cols()),
c(problem.num_rows()),
c(problem.matrix.avalue.len()),
MATRIX_FORMAT_COLUMN_WISE,
OBJECTIVE_SENSE_MINIMIZE,
offset,
problem.colcost.as_ptr(),
problem.collower.as_ptr(),
problem.colupper.as_ptr(),
problem.rowlower.as_ptr(),
problem.rowupper.as_ptr(),
problem.matrix.astart.as_ptr(),
problem.matrix.aindex.as_ptr(),
problem.matrix.avalue.as_ptr(),
integrality.as_ptr()
))
} else {
highs_call!(Highs_passLp(
highs.mut_ptr(),
c(problem.num_cols()),
c(problem.num_rows()),
c(problem.matrix.avalue.len()),
MATRIX_FORMAT_COLUMN_WISE,
OBJECTIVE_SENSE_MINIMIZE,
offset,
problem.colcost.as_ptr(),
problem.collower.as_ptr(),
problem.colupper.as_ptr(),
problem.rowlower.as_ptr(),
problem.rowupper.as_ptr(),
problem.matrix.astart.as_ptr(),
problem.matrix.aindex.as_ptr(),
problem.matrix.avalue.as_ptr()
))
}
.map(|_| Self { highs })
}
}
/// Prevents writing anything to the standard output or to files when solving the model
pub fn make_quiet(&mut self) {
self.highs.make_quiet()
}
/// Set a custom parameter on the model.
/// For the list of available options and their documentation, see:
/// <https://www.maths.ed.ac.uk/hall/HiGHS/HighsOptions.html>
///
/// ```
/// # use highs::ColProblem;
/// # use highs::Sense::Maximise;
/// let mut model = ColProblem::default().optimise(Maximise);
/// model.set_option("presolve", "off"); // disable the presolver
/// model.set_option("solver", "ipm"); // use the ipm solver
/// model.set_option("time_limit", 30.0); // stop after 30 seconds
/// model.set_option("parallel", "on"); // use multiple cores
/// model.set_option("threads", 4); // solve on 4 threads
/// ```
pub fn set_option<STR: Into<Vec<u8>>, V: HighsOptionValue>(&mut self, option: STR, value: V) {
self.highs.set_option(option, value)
}
/// Find the optimal value for the problem, panic if the problem is incoherent
pub fn solve(self) -> SolvedModel {
self.try_solve().expect("HiGHS error: invalid problem")
}
/// Find the optimal value for the problem, return an error if the problem is incoherent
pub fn try_solve(mut self) -> Result<SolvedModel, HighsStatus> {
unsafe { highs_call!(Highs_run(self.highs.mut_ptr())) }
.map(|_| SolvedModel { highs: self.highs })
}
/// Adds a new constraint to the highs model.
///
/// Returns the added row index.
///
/// # Panics
///
/// If HIGHS returns an error status value.
pub fn add_row(
&mut self,
bounds: impl RangeBounds<f64>,
row_factors: impl IntoIterator<Item=(Col, f64)>,
) -> Row {
self.try_add_row(bounds, row_factors)
.unwrap_or_else(|e| panic!("HiGHS error: {:?}", e))
}
/// Tries to add a new constraint to the highs model.
///
/// Returns the added row index, or the error status value if HIGHS returned an error status.
pub fn try_add_row(
&mut self,
bounds: impl RangeBounds<f64>,
row_factors: impl IntoIterator<Item=(Col, f64)>,
) -> Result<Row, HighsStatus> {
let (cols, factors): (Vec<_>, Vec<_>) = row_factors.into_iter().unzip();
unsafe {
highs_call!(
Highs_addRow(
self.highs.mut_ptr(),
bound_value(bounds.start_bound()).unwrap_or(f64::NEG_INFINITY),
bound_value(bounds.end_bound()).unwrap_or(f64::INFINITY),
cols.len().try_into().unwrap(),
cols.into_iter().map(|c| c.0.try_into().unwrap()).collect::<Vec<_>>().as_ptr(),
factors.as_ptr()
)
)
}?;
Ok(Row((self.highs.num_rows()? - 1) as c_int))
}
/// Adds a new variable to the highs model.
///
/// Returns the added column index.
///
/// # Panics
///
/// If HIGHS returns an error status value.
pub fn add_col(
&mut self,
col_factor: f64,
bounds: impl RangeBounds<f64>,
row_factors: impl IntoIterator<Item=(Row, f64)>,
) -> Col {
self.try_add_column(col_factor, bounds, row_factors)
.unwrap_or_else(|e| panic!("HiGHS error: {:?}", e))
}
/// Tries to add a new variable to the highs model.
///
/// Returns the added column index, or the error status value if HIGHS returned an error status.
pub fn try_add_column(
&mut self,
col_factor: f64,
bounds: impl RangeBounds<f64>,
row_factors: impl IntoIterator<Item=(Row, f64)>,
) -> Result<Col, HighsStatus> {
let (rows, factors): (Vec<_>, Vec<_>) = row_factors.into_iter().unzip();
unsafe {
highs_call!(
Highs_addCol(
self.highs.mut_ptr(),
col_factor,
bound_value(bounds.start_bound()).unwrap_or(f64::NEG_INFINITY),
bound_value(bounds.end_bound()).unwrap_or(f64::INFINITY),
rows.len().try_into().unwrap(),
rows.into_iter().map(|r| r.0.try_into().unwrap()).collect::<Vec<_>>().as_ptr(),
factors.as_ptr()
)
)
}?;
Ok(Col(self.highs.num_cols()? - 1))
}
}
impl From<SolvedModel> for Model {
fn from(solved: SolvedModel) -> Self {
Self {
highs: solved.highs,
}
}
}
#[derive(Debug)]
struct HighsPtr(*mut c_void);
impl Drop for HighsPtr {
fn drop(&mut self) {
unsafe { Highs_destroy(self.0) }
}
}
impl Default for HighsPtr {
fn default() -> Self {
Self(unsafe { Highs_create() })
}
}
impl HighsPtr {
// To be used instead of unsafe_mut_ptr wherever possible
#[allow(dead_code)]
const fn ptr(&self) -> *const c_void {
self.0
}
// Needed until https://github.com/ERGO-Code/HiGHS/issues/479 is fixed
unsafe fn unsafe_mut_ptr(&self) -> *mut c_void {
self.0
}
fn mut_ptr(&mut self) -> *mut c_void {
self.0
}
/// Prevents writing anything to the standard output when solving the model
pub fn make_quiet(&mut self) {
// setting log_file seems to cause a double free in Highs.
// See https://github.com/rust-or/highs/issues/3
// self.set_option(&b"log_file"[..], "");
self.set_option(&b"output_flag"[..], false);
self.set_option(&b"log_to_console"[..], false);
}
/// Set a custom parameter on the model
pub fn set_option<STR: Into<Vec<u8>>, V: HighsOptionValue>(&mut self, option: STR, value: V) {
let c_str = CString::new(option).expect("invalid option name");
let status = unsafe { value.apply_to_highs(self.mut_ptr(), c_str.as_ptr()) };
try_handle_status(status, "Highs_setOptionValue")
.expect("An error was encountered in HiGHS.");
}
/// Number of variables
fn num_cols(&self) -> Result<usize, TryFromIntError> {
let n = unsafe { Highs_getNumCols(self.0) };
n.try_into()
}
/// Number of constraints
fn num_rows(&self) -> Result<usize, TryFromIntError> {
let n = unsafe { Highs_getNumRows(self.0) };
n.try_into()
}
}
impl SolvedModel {
/// The status of the solution. Should be Optimal if everything went well
pub fn status(&self) -> HighsModelStatus {
let model_status = unsafe { Highs_getModelStatus(self.highs.unsafe_mut_ptr()) };
HighsModelStatus::try_from(model_status).unwrap()
}
/// Get the solution to the problem
pub fn get_solution(&self) -> Solution {
let cols = self.num_cols();
let rows = self.num_rows();
let mut colvalue: Vec<f64> = vec![0.; cols];
let mut coldual: Vec<f64> = vec![0.; cols];
let mut rowvalue: Vec<f64> = vec![0.; rows];
let mut rowdual: Vec<f64> = vec![0.; rows];
// Get the primal and dual solution
unsafe {
Highs_getSolution(
self.highs.unsafe_mut_ptr(),
colvalue.as_mut_ptr(),
coldual.as_mut_ptr(),
rowvalue.as_mut_ptr(),
rowdual.as_mut_ptr(),
);
}
Solution {
colvalue,
coldual,
rowvalue,
rowdual,
}
}
/// Number of variables
fn num_cols(&self) -> usize {
self.highs.num_cols().expect("invalid number of columns")
}
/// Number of constraints
fn num_rows(&self) -> usize {
self.highs.num_rows().expect("invalid number of rows")
}
}
/// Concrete values of the solution
#[derive(Clone, Debug)]
pub struct Solution {
colvalue: Vec<f64>,
coldual: Vec<f64>,
rowvalue: Vec<f64>,
rowdual: Vec<f64>,
}
impl Solution {
/// The optimal values for each variables (in the order they were added)
pub fn columns(&self) -> &[f64] {
&self.colvalue
}
/// The optimal values for each variables in the dual problem (in the order they were added)
pub fn dual_columns(&self) -> &[f64] {
&self.coldual
}
/// The value of the constraint functions
pub fn rows(&self) -> &[f64] {
&self.rowvalue
}
/// The value of the constraint functions in the dual problem
pub fn dual_rows(&self) -> &[f64] {
&self.rowdual
}
}
impl Index<Col> for Solution {
type Output = f64;
fn index(&self, col: Col) -> &f64 {
&self.colvalue[col.0]
}
}
fn try_handle_status(status: c_int, msg: &str) -> Result<HighsStatus, HighsStatus> {
let status_enum = HighsStatus::try_from(status)
.expect("HiGHS returned an unexpected status value. Please report it as a bug to https://github.com/rust-or/highs/issues");
match status_enum {
status @ HighsStatus::OK => Ok(status),
status @ HighsStatus::Warning => {
log::warn!("HiGHS emitted a warning: {}", msg);
Ok(status)
}
error => Err(error),
}
}
#[cfg(test)]
mod test {
use super::*;
fn test_coefs(coefs: [f64; 2]) {
// See: https://github.com/rust-or/highs/issues/5
let mut problem = RowProblem::new();
// Minimize x + y subject to x ≥ 0, y ≥ 0.
let x = problem.add_column(1., -1..);
let y = problem.add_column(1., 0..);
problem.add_row(..1, [x, y].iter().copied().zip(coefs)); // 1 ≥ x + c y.
let solution = problem.optimise(Sense::Minimise).solve().get_solution();
assert_eq!([-1., 0.], solution.columns());
}
#[test]
fn test_single_zero_coef() {
test_coefs([1.0, 0.0]);
test_coefs([0.0, 1.0]);
}
#[test]
fn test_all_zero_coefs() {
test_coefs([0.0, 0.0])
}
#[test]
fn test_no_zero_coefs() {
test_coefs([1.0, 1.0])
}
#[test]
fn test_infeasible_empty_row() {
let mut problem = RowProblem::new();
let row_factors: &[(Col, f64)] = &[];
problem.add_row(2..3, row_factors);
let _ = problem.optimise(Sense::Minimise).try_solve();
}
#[test]
fn test_add_row_and_col() {
let mut model = Model::new::<Problem<ColMatrix>>(Problem::default());
let col = model.add_col(1., 1.0.., vec![]);
model.add_row(..1.0, vec![(col, 1.0)]);
let solved = model.solve();
assert_eq!(solved.status(), HighsModelStatus::Optimal);
let solution = solved.get_solution();
assert_eq!(solution.columns(), vec![1.0]);
let mut model = Model::from(solved);
let new_col = model.add_col(1., ..1.0, vec![]);
model.add_row(2.0.., vec![(new_col, 1.0)]);
let solved = model.solve();
assert_eq!(solved.status(), HighsModelStatus::Infeasible);
}
}