Cashback Optimizer

An algorithms course project that finds the optimal assignment of spending categories to credit cards in order to maximize total cashback. Compares Brute Force and Greedy approaches side-by-side.

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Tech Stack

• Language: Java, JavaScript
• Frontend: HTML, CSS, JavaScript
• Build (Java): Single-file compilation (javac)
• Libraries: AOS (scroll animations)

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Features

• Brute Force Algorithm: Tries every possible category-to-card assignment to guarantee the optimal solution. Skips automatically if the search space exceeds 10 million.
• Greedy Algorithm: Assigns categories largest-spend-first to the card with the best rate, running in polynomial time.
• Interactive Web Demo: Enter your own cards, categories, and rates in the browser and run both algorithms side-by-side in real time.
• Side-by-side comparison: Formatted results tables for both algorithms with a comparison summary highlighting differences.

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Process

1. Defined the problem: given N cards, M spending categories, per-card limits, and a rates matrix, maximize total cashback.
2. Implemented Brute Force by enumerating all base-N assignments and Greedy by sorting categories by spend and picking the best available card.
3. Added a hard cap to Brute Force to prevent blowout on large inputs.
4. Re-implemented both algorithms in JavaScript and built an interactive web demo with editable inputs and side-by-side results.

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Running the Project

• Web Demo

Open docs/index.html in a browser; no build step required.

• Java Console App

# Compile
javac Algorithms_Cashback_Project.java

# Run
java Algorithms_Cashback_Project

Follow the console prompts to enter:

• Number of cards and categories
• Spending amounts per category
• Per-card spending limits
• Cashback rates matrix (cards x categories)

Both Brute Force and Greedy results will be printed automatically.

Live at: cashback-optimizer.khalidaldosari.workers.dev

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Team

• Khalid Al Dosari
• Zeyad bin Nazir
• Abdulellah Altowaijri