Linear Programming Optimization Tool

An interactive web application for solving linear programming problems using the Simplex method. Built with Streamlit and Python, this tool provides a user-friendly interface for defining and solving optimization problems.

Originally, this project started with trying to create my own optimization problem-solving algorithm but pivoted to focus on building a user interface for existing solvers. This pivot allowed for a deeper exploration of front-end development and application architecture.

Features

• Interactive UI for defining optimization problems
• Support for both maximization and minimization problems
• Dynamic variable and constraint management (-/+)
• Variable bounds configuration
• Real-time LaTeX rendering of mathematical expressions
• Utilizes HiGHS solver for optimization

Learning Outcomes

Streamlit Development

• Built interactive web apps with Streamlit's component system
• Managed application state using st.session_state
• Created dynamic UI elements with real-time updates
• Implemented form validation and error handling

Frontend Design

• Rendered mathematical expressions using LaTeX in Streamlit
• Designed responsive layouts with column systems
• Created intuitive interfaces for complex mathematical input
• Developed real-time result visualization

Architecture Skills

• Structured modular Python applications[1]
• Separated concerns between UI, business logic, and computation
• Integrated third-party optimization solvers
• Managed state across multiple components

Installation

1. Clone the repository:

git clone https://github.com/yourusername/optimizer-tool.git
cd optimizer-tool

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

Usage

1. Run the Streamlit application:

streamlit run app.py

2. In the web interface:
   • Select optimization type (maximize/minimize)
   • Set number of variables
   • Define objective function coefficients
   • Add constraints with coefficients and bounds
   • Set variable bounds
   • Click "Solve" to obtain the solution

Next Steps

1. Implement custom Simplex solver to replace current implementation
2. Add support for:
   • Integer programming
   • Problem visualization
   • Solution sensitivity analysis
3. Implement solution visualization
4. Add problem validation and error checking

Dependencies

• Streamlit
• NumPy
• SciPy
• SymPy (for LaTeX rendering)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.