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<img src="logo.png" alt="Dantzig logo" width="40%">
</p>
<h1 align="center">Dantzig: A Rust-powered LP library for Python</h1>
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Dantzig is a lightweight and concise linear programming solver suitable for small
and large-scale problems alike.
Dantzig is implemented in both Rust and Python, meaning you get the expressiveness
and flexibility of a Python frontend plus the raw computing speed of a Rust backend.
Dantzig supports
- A solver featuring a parametric self-dual algorithm
- Arbitrarily restricted variables, including completely unrestricted free variables
- `==`, `<=`, and `>=` constraints
- Both minimization and maximization problems
- A numerically stable LU factorization with partial pivoting routine for robust linear algebra operations
- Memory-efficient sparse matrix representations
- Modern Python type-checking
:warning: Dantzig is under active development. Please help us improve the library by reporting any issues!.
### Installation
Dantzig supports Python 3.10+ and can be installed with `pip`.
```shell
pip install dantzig
```
### Design Philosophies
Dantzig prides itself on being both **lightweight** (zero-dependency) and **concise**.
The API is designed to be extremely expressive and terse, saving you keystrokes without
sacrificing clarity. To this end, Dantzig provides several short aliases for common
classes and methods.
A few examples are listed below,
- `Var == Variable`
- `Min == Minimize`
- `Max == Maximize`
- `Var.free() == Variable(lb=0.0, ub=0.0)`
- `Var.nn() == Var.nonneg() == Variable(lb=0.0, ub=None)`
- `Var.np() == Var.nonpos() == Variable(lb=None, ub=0.0)`
### Examples
```python
import dantzig as dz
x = dz.Variable(lb=0.0, ub=None)
y = dz.Variable(lb=0.0, ub=None)
z = dz.Variable(lb=0.0, ub=None)
soln = dz.Minimize(x + y - z).subject_to(x + y + z == 1).solve()
assert soln.objective_value == -1.0
assert soln[x] == 0.0
assert soln[y] == 0.0
assert soln[z] == 1.0
```
Using aliases, the previous example can alternately be written
```python
from dantzig import Min, Var
x = Var.nn()
y = Var.nn()
z = Var.nn()
soln = Min(x + y - z).st(x + y + z == 1)
```
### Road Map
- [ ] Mixed integer linear programing (MILP)
- [ ] SIMD-accelerated linear algebra operations
- [ ] General optimizations to make the library competitive with `ortools`
