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good_lp

(★ 452)

Linear Programming for Rust, with a user-friendly API. This crate allows modeling LP problems, and lets you solve them with various solvers.

파일 탐색기

최종 버전 다운로드 (.zip)

- devcontainer.json
- Dockerfile
- - publish.yml
  - rust.yml
- settings.json
- benchmark.rs
- postsetup.sh
- response.properties
- setup.sh
- - clarabel.rs
  - coin_cbc.rs
  - cplex.rs
  - highs.rs
  - lp_solvers.rs
  - lpsolve.rs
  - microlp.rs
  - mod.rs
  - scip.rs
- affine_expression_trait.rs
- cardinality_constraint_solver_trait.rs
- constraint.rs
- expression.rs
- lib.rs
- variable.rs
- variables_macro.rs
- mipgap_set.rs
- no_macro_example.rs
- readme_example.rs
- resource_allocation_problem.rs
- shadow_price.rs
- solver_scaling.rs
- static_solver.rs
- variables.rs
.gitignore
Cargo.toml
CONTRIBUTING.md
LICENSE
README.md

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/rust-or/good_lp

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd good_lp

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. 공식 설치 스크립트

쉬움 추천

사전 준비물

APT (Debian/Ubuntu 계열) Debian/Ubuntu 계열 리눅스에 기본 내장된 패키지 매니저입니다.
Homebrew macOS/Linux용 패키지 매니저입니다.

sudo apt-get install coinor-cbc coinor-libcbc-dev

APT 패키지 저장소에서 바로 설치합니다 (Debian/Ubuntu 계열).

brew install cbc

Homebrew로 이미 빌드된 패키지를 바로 설치합니다. 소스 빌드가 필요 없습니다.

✅ 설치 후 새 터미널을 열고, 프로그램의 버전 확인 명령(예: --version)으로 정상 설치됐는지 확인하세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Rust

보통

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Rust (rustup) rustup으로 설치하면 cargo도 함께 설치됩니다.

cargo build --release

Rust 프로젝트를 컴파일합니다.

cargo run

빌드 후 바로 실행까지 진행합니다.

✅ cargo build가 에러 없이 끝나면 성공입니다. target/ 폴더에 실행 파일이 생성됩니다.

// repository documentation

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# good_lp

A Mixed Integer Linear Programming modeler that is easy to use, performant with large problems, and well-typed.

[![Crates.io](https://img.shields.io/crates/v/good_lp.svg)](https://crates.io/crates/good_lp)
[![documentation](https://docs.rs/good_lp/badge.svg)](https://docs.rs/good_lp)
[![MIT license](http://img.shields.io/badge/license-MIT-brightgreen.svg)](http://opensource.org/licenses/MIT)

```rust
use std::error::Error;

use good_lp::{constraint, default_solver, Solution, SolverModel, variables};

fn main() -> Result<(), Box<dyn Error>> {
    variables! {
        vars:
               a <= 1;
          2 <= b <= 4;
    } // variables can also be added dynamically with ProblemVariables::add
    let solution = vars.maximise(10 * (a - b / 5) - b)
        .using(default_solver) // IBM's coin_cbc by default
        .with(constraint!(a + 2 <= b))
        .with(constraint!(1 + a >= 4 - b)) // .with_all(iter) is also available
        .solve()?;
    println!("a={}   b={}", solution.value(a), solution.value(b));
    println!("a + b = {}", solution.eval(a + b));
    Ok(())
}
```

For a more complex example, see the [resource allocation problem](https://github.com/rust-or/good_lp/blob/main/tests/resource_allocation_problem.rs).

## Features and limitations

- **Linear programming**. This crate currently supports only the definition of linear programs. You cannot use it with
  quadratic functions. For instance:
  you can maximise `3 * x + y`, but not `3 * x * y`.
- **Continuous and integer variables**. good_lp itself supports mixed integer-linear programming (MILP),
  but not all underlying solvers support integer variables. (see also [variable types](#variable-types))
- **Not a solver**. This crate uses other rust crates to provide the solvers.
  There is no solving algorithm in good_lp itself. If you have an issue with a solver,
  report it to the solver directly. See below for the list of supported solvers.

### Contributing

Pull requests are welcome !
If you need a feature that is not yet implemented, get in touch.
Also, do not hesitate to open issues to discuss the implementation.

### Alternatives

If you need non-linear programming, you can use
[lp-modeler](https://crates.io/crates/lp-modeler).
However, it is currently very slow with large problems.

You can also directly use the underlying solver libraries, such as
[coin_cbc](https://docs.rs/coin_cbc/) or
[microlp](https://crates.io/crates/microlp)
if you don't need a way to express your objective function and
constraints using an idiomatic rust syntax.

## Usage examples

You can find a resource allocation problem example in
[`resource_allocation_problem.rs`](https://github.com/rust-or/good_lp/blob/main/tests/resource_allocation_problem.rs).

The macros (`variables!`, `constraint!`) are convenient, but not required.
If your problem is built dynamically (its variables and constraints are only known at
run time), see the
[macro-free example](https://github.com/rust-or/good_lp/blob/main/tests/no_macro_example.rs)
which uses `ProblemVariables::new`, `Variable`, `Expression` and
`SolverModel::add_constraint` directly.

## Solvers

This library offers an abstraction over multiple solvers. By default, it uses [cbc][cbc], but
you can also activate other solvers using cargo features.

| solver feature name    | integer variables | no C compiler\* | no additional libs\*   | fast\* | WASM\* |
| ---------------------- | ----------------- | --------------- | ---------------------- | ---- | ---- |
| [`coin_cbc`][cbc]      | ✅                | ✅              | ❌                     | ✅   | ❌   |
| [`highs`][highs]       | ✅                | ❌              | ✅¹                    | ✅   | ❌   |
| [`lpsolve`][lpsolve]   | ✅                | ❌              | ✅                     | ❌   | ❌   |
| [`microlp`][microlp]   | ✅                | ✅              | ✅                     | ❌   | ✅   |
| [`lp-solvers`][lps]    | ✅                | ✅              | ✅                     | ❌   | ❌   |
| [`scip`][scip]         | ✅                | ✅              | ✅²                    | ✅   | ❌   |
| [`cplex-rs`][cplex]    | ✅                | ❌              | ✅³                    | ✅   | ❌   |
| [`clarabel`][clarabel] | ❌                | ✅              | ✅                     | ✅   | ✅   |

- \* *no C compiler*: builds with only cargo, without requiring you to install a C compiler
- \* *no additional libs*: works without additional libraries at runtime, all the dependencies are statically linked
- \* *fast*: the solver does good on large problems according to published benchmarks ([*caveats*](https://github.com/rust-or/good_lp/issues/68))
- \* *WASM*: the solver can compile to WASM targets and run in web browsers
- ¹ highs itself is statically linked and does not require manual installation. However, on some systems, you may have to [install dependencies of highs itself](https://github.com/rust-or/good_lp/issues/29).
- ² using the precompiled binary is possible by enabling the optional `scip_bundled` feature
- ³ the [cplex_rs crate](https://crates.io/crates/cplex-rs) links statically to a local installation of the proprietary [IBM ILOG CPLEX Optimizer](https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer).

To use an alternative solver, put the following in your `Cargo.toml`:

```toml
good_lp = { version = "*", features = ["your solver feature name"], default-features = false }
```

Note that the `lpsolve` and `cplex-rs` features are mutually exclusive, and they will produce a compilation error when simultaneously activated. In particular, this means that the building with the `--all-features` option will produce a compilation error.

### [cbc][cbc]

Used by default, performant, but requires to have the cbc C library headers available on the build machine,
and the cbc dynamic library available on any machine where you want to run your program.

In ubuntu, you can install it with:

```bash
sudo apt-get install coinor-cbc coinor-libcbc-dev
```

In MacOS, using [homebrew](https://brew.sh/) :

```bash
brew install cbc
```

Be careful if you disable the default features of this crate and activate the cbc feature manually.
In this case, you have to also activate `singlethread-cbc`,
unless you compiled Cbc yourself with the [`CBC_THREAD_SAFE`](https://github.com/coin-or/Cbc/issues/332)
option. Otherwise, using Cbc from multiple threads would be unsafe.

[cbc]: https://www.coin-or.org/Cbc/

### [microlp](https://docs.rs/microlp)

Microlp is a fork of [minilp](https://docs.rs/minilp), a pure rust solver, which means it works out of the box without installing anything else.

[microlp]: https://docs.rs/microlp

Microlp is written in pure rust, so you can use it without having to install a C compiler on your machine,
or having to install any external library, but it is slower than other solvers.

It performs very poorly when compiled in debug mode, so be sure to compile your code
in `--release` mode when solving large problems. This solver can compile to WASM targets.

### [HiGHS][highs]

HiGHS is a free ([MIT](https://github.com/ERGO-Code/HiGHS/blob/master/LICENSE)) parallel mixed integer linear programming
solver written in C++.
It is able to fully leverage all the available processor cores to solve a problem.

good_lp uses the [highs crate](https://docs.rs/highs) to call HiGHS.
You will need a C compiler, but you shouldn't have to install any additional library on linux
(it depends only on the C++ standard library).
More information in the [highs-sys crate](https://crates.io/crates/highs-sys).

[highs]: https://highs.dev

### [lpsolve][lpsolve]

lp_solve is a free ([LGPL](http://lpsolve.sourceforge.net/5.5/LGPL.htm)) linear (integer) programming solver
written in C and based on the revised simplex method.

good_lp uses the [lpsolve crate](https://docs.rs/lpsolve/) to call lpsolve.
You will need a C compiler, but you won't have to install any additional library.

[lpsolve]: http://lpsolve.sourceforge.net/5.5/

### [lp-solvers][lps]

The `lp-solvers` feature is particular: it doesn't contain any solver.
Instead, it calls other solvers at runtime.
It writes the given problem to a `.lp` file, and launches an external solver command
(such as **gurobi**, **cplex**, **cbc**, or **glpk**) to solve it.

There is some overhead associated to this method: it can take a few hundred milliseconds
to write the problem to a file, launch the external solver, wait for it to finish, and then parse its solution.
If you are not solving a few large problems but many small ones (in a web server, for instance),
then this method may not be appropriate.

Additionally, the end user of your program will have to install the desired solver on his own.

[lps]: https://crates.io/crates/lp-solvers

### [SCIP][scip]

SCIP is currently one of the fastest open-source solvers for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP). It is also a framework for constraint integer programming and branch-cut-and-price. It allows for total control of the solution process and the access of detailed information down to the guts of the solver.

`good_lp` uses SCIP through its rust interface [russcip](https://github.com/mmghannam/russcip) which can ship a precompiled binary. The easiest way to use SCIP with `good_lp` is therefore to enable both the `scip` and the `scip_bundled` features.

You can also use a custom installation of SCIP by enabling only the `scip` feature. A good way of installing SCIP is by downloading a precompiled package from [here](https://scipopt.org/index.php#download) or through conda by running

```sh
conda install --channel conda-forge scip
```

[scip]: https://scipopt.org/

### [cplex-rs][cplex]

The IBM ILOG CPLEX Optimizer is a commercial high-performance optimization solver for linear, mixed-integer and quadratic programming.

good_lp uses the [cplex-rs](https://github.com/mbiggio/cplex-rs/tree/main) crate to call CPLEX through safe rust bindings, which in turn uses the [cplex-rs-sys](https://crates.io/crates/highs-sys) crate to call the raw bindings to the CPLEX C API.

You will need a valid CPLEX installation to use this feature. CPLEX should be installed in its default installation directory, or alternatively you can specify its installation directory through the `CPLEX_PATH` environment variable at compile time

Since cplex-rs-sys uses [bindgen](https://github.com/rust-lang/rust-bindgen) to generate the raw C bindings, you will also need need an installation of clang and llvm as indicated in the [bindgen requirements](https://rust-lang.github.io/rust-bindgen/requirements.html).

[cplex]: https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer

### [Clarabel][clarabel]

**Clarabel** is a free
([Apache 2.0](https://github.com/oxfordcontrol/Clarabel.rs/blob/main/LICENSE.md))
linear programming solver written in Rust by the
[Oxford Control group](https://eng.ox.ac.uk/control/).

It does not support integer variables, but it is fast and easy to install.
It does implement the [SolutionWithDual](https://docs.rs/good_lp/latest/good_lp/solvers/trait.SolutionWithDual.html)
trait, which allows you to access the dual values of the constraints (the shadow prices).

[clarabel]: https://github.com/oxfordcontrol/Clarabel.rs

## Variable types

`good_lp` internally represents all [variable](https://docs.rs/good_lp/latest/good_lp/variable/struct.Variable.html) values and coefficients as `f64`.
It lets you express constraints on the range of possible values using either `f64` or `i32` (in the latter case, the integer will be losslessly converted to a floating point number).
The solution's [values are `f64`](https://docs.rs/good_lp/latest/good_lp/solvers/trait.Solution.html#tymethod.value) as well.

For instance:

```rust
// Correct use of f64 and i32 to specify feasible ranges for Variables
  variables! {
    problem:
      a <= 10.0;
      2 <= b (integer) <= 4;  // Variables can be restricted using qualifiers like (integer)
  };
  let model = problem
    .maximise(b)
    .using(default_solver)
    .with(constraint!(a + 2 <= b))
    .with(constraint!(1 + a >= 4.0 - b));
```

Here, `a` and `b` are `Variable` instances that can take either continuous (floating-point) or [integer values](https://docs.rs/good_lp/latest/good_lp/variable/struct.VariableDefinition.html#method.integer).
Constraints on possible values can be expressed using either `f64` or `i32`, as shown in the example (but replacing for example `4.0` with a `usize` variable would fail, because an usize cannot be converted to an f64 losslessly).
The [`variables!` macro](https://docs.rs/good_lp/latest/good_lp/macro.variables.html) also allows constraining variables to integer values using qualifiers like `2 <= b (integer) <= 4` above.

Solution values will always be `f64`, regardless of whether the variables were defined with `f64` or `i32`.
So, even if you use integer variables, the solution object will store the integer variable values as `f64`.

For example, when printing the solution:

```rust
// Correct use of f64 for solution values
println!("a={}   b={}", solution.value(a), solution.value(b));
println!("a + b = {}", solution.eval(a + b));

// Incorrect use of i32 in combination with solution value (Will fail!)
println!("a + 1 = {}", solution.value(a) + 1); // This will cause a compilation error!
```

The `solution.value(a)` and `solution.value(b)` will return `f64` values, and `solution.eval(a + b)` will also provide an `f64` value.

### License

This library is published under the MIT license.
The solver themselves have various licenses, please refer to their individual documentation.