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Description:

dantzig 0.2.0

Dantzig: A Rust-powered LP library for Python

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.
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
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
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