simple-simplex 0.0.3

A lightweight and educational Python package for solving linear programming problems using the Simplex method.

simple_simplex

A lightweight Python package for solving linear programming problems using the simplex method.

🚀 Overview

simple_simplex is a minimal, NumPy-based Simplex solver that provides an easy interface for defining linear programs and solving them. It supports both maximization and minimization problems and includes features for logging pivot steps and returning results in JSON-friendly format — making it ideal for integration with web backends, educational tools, or visualization apps.

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📦 Features

• 🧮 Solve both maximization and minimization problems
• ✅ Support for ≤ and ≥ constraints
• 📊 Pivot step tracking for visualization/debugging
• 🔗 Returns results as JSON-compatible dicts
• 📝 Display results as a neatly-formatted ASCII table
• 🧩 Easy to embed in Flask or FastAPI backends

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📥 Installation

pip install simple_simplex

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🛠 Usage

1. Create a tableau

Pass in the number of variables, and number of constraints in the problem.

from simple_simplex import *

tableau = create_tableau(number_of_variables=2, number_of_constraints=2)

2. Add constraints

Each constraint is passed as a string in the format:

"coeff1,coeff2,...,<inequality>,rhs"

Example:

add_constraint(tableau, "2,1,L,18")  # 2x + y ≤ 18
add_constraint(tableau, "2,3,L,42")  # 2x + 3y ≤ 42

Use "L" for ≤, "G" for ≥

3. Add objective function

Objective format: "coeff1,coeff2,...,objective"
(Set objective value to 0)

add_objective(tableau, "3,2,0")  # Maximize 3x + 2y = z

4. Solve

Option 1 (print results, no return value)

optimize_max(tableau) #prints results to stdout

optimize_min(tableau) #prints results to stdout

Option 2 (generate and return JSON-compatible results)

See Output Format for more details

result = optimize_json_format(tableau, maximize=True)
print(result["optimalValue"])       # => Optimal Z value
print(result["solutionValues"])     # => Variable values
print(result["pivotSteps"])         # => Step-by-step history
# See Output Format for more details

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📄 Output Format

The optimize_json_format() function returns a Python dictionary in the form of:

{
  "status": "optimal",
  "objectiveType": "max",
  "optimalValue": 36.0,
  "solutionValues": {
    "x1": 6.0,
    "x2": 6.0
  },
  "pivotSteps": [...],
  "finalTableau": [...],
  "numSteps": 2
}

The optimize_max() and optimize_min() functions display results as well as the final tableau.

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📚 Example Problem

Maximize:
Z = 3x + 2y

Subject to:

2x + y   ≤ 18  
2x + 3y  ≤ 42  
x, y ≥ 0

from simple_simplex import *

tableau = create_tableau(2, 2)
add_constraint(tableau, "2,1,L,18")
add_constraint(tableau, "2,3,L,42")
add_objective(tableau, "3,2,0")

solution_dict = optimize_json_format(tableau, maximize=True)
# OR to print solution to stdout
optimize_max(tableau)

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📏 To-Do

• Better input handling
• Better error handling

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📝 License

This project is licensed under the MIT License.

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👤 Author

Joshua Emralino
[e-mail]

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🌐 Keywords

simplex, linear programming, optimization, math, solver, numpy, backend, json, flask