los-lang 3.3.2

Write Math, Run Python. A Domain-Specific Language for Mathematical Optimization.

LosPy — Mathematical Optimization Specification

LOS is a Domain-Specific Language (DSL) for mathematical optimization. It compiles human-readable model definitions into executable Python/PuLP code, keeping your business logic clean and your data pipeline separate.

"Write Math, Run Python."

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Installation

pip install los-lang

Or install from source:

git clone https://github.com/jowpereira/los.git
cd los
pip install -e .

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Quick Start

1. Write a Model (production.los)

import "products.csv"
import "factories.csv"

set Products
set Factories

param Cost[Products]
param Capacity[Factories]

var qty[Products, Factories] >= 0

minimize:
    sum(qty[p,f] * Cost[p] for p in Products, f in Factories)

subject to:
    capacity_limit:
        sum(qty[p,f] for p in Products) <= Capacity[f]
        for f in Factories

2. Prepare Data

products.csv

Products,Cost
WidgetA,10
WidgetB,15

factories.csv

Factories,Capacity
Factory1,1000
Factory2,2000

3. Solve (solve.py)

import los

result = los.solve("production.los")

if result.is_optimal:
    print(f"Optimal Cost: {result.objective}")
    print(result.get_variable("qty", as_df=True))

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Why LOS?

Feature	LOS	Raw PuLP/Pyomo
Readability	Whiteboard-like syntax	Python boilerplate
Data Binding	Native CSV imports	Manual DataFrame wrangling
Security	Sandboxed execution	Full Python access
Debug	Inspect generated code (model.code())	Black box
Solver	CBC, GLPK, Gurobi, CPLEX (via PuLP)	Same

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Advanced: Manual Data Binding

For dynamic data (APIs, databases), inject DataFrames directly:

import los
import pandas as pd

df = pd.DataFrame({"Products": ["A", "B"], "Cost": [10, 20]})

result = los.solve("model.los", data={"Products": df})

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Documentation

Document	Description
User Manual	Full syntax reference and API guide
Security Policy	Sandbox details and threat model
Changelog	Version history
Contributing	How to contribute

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License

MIT © Jonathan Pereira