🚀 UPC - FIB | Algorithmic Problem Solving

A comprehensive collection of algorithmic solutions and data structure implementations developed for the Jutge.org platform at FIB (Universitat Politècnica de Catalunya).

📊 Overview

This repository serves as a portfolio of algorithmic thinking, covering topics from basic iterative logic to complex graph theory and dynamic programming.

• Focus: Efficiency, Code Cleanliness, and Complexity Analysis ($O(n)$, $O(\log n)$).
• Platform: Jutge.org (UPC Online Judge).
• Core Languages: C++ (Primary for performance), Python.

🗂️ Repository Structure & Categorization

The problems are organized by complexity and topic, reflecting the rigorous Computer Science curriculum at FIB:

🔹 1. Advanced Data Structures & Algorithms (EDA)

Focus on Graph Theory, Maps, and Optimization.

• Graph Algorithms: BFS (Breadth-First Search), DFS (Depth-First Search), Dijkstra, Topological Sort.
• Data Structures: Priority Queues (Heaps), BST (Binary Search Trees), Hash Maps (STL maps/sets).
• Divide & Conquer: MergeSort, QuickSort logic.

🔹 2. Modular Programming & Recursion (PRO2)

Focus on clean code, recursion, and abstract data types.

• Recursion: Backtracking solutions, recursive mathematical sequences.
• Backtracking: Solving constraint satisfaction problems (N-Queens, Subsets).
• Modular Design: Separation of interface (.hh) and implementation (.cc).

🔹 3. Fundamentals of Programming (PRO1)

The building blocks of logical thinking.

• Iterative Logic: Complex loops, sequence handling.
• Vectors & Matrices: Multi-dimensional array manipulation.
• Basic Algorithms: Searching (Linear/Binary) and Sorting (Selection/Insertion).

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🧠 Key Algorithmic Concepts Demonstrated

🕸️ Graph Theory

Implementation of traversal algorithms to solve connectivity problems, shortest paths, and cycle detection.

// Example: Graph Traversal concept
void dfs(int u, vector<bool>& vis, const vector<vector<int>>& adj) {
    vis[u] = true;
    for (int v : adj[u]) {
        if (!vis[v]) dfs(v, vis, adj);
    }
}
### ⚡ Dynamic Programming & Greedy
Optimization problems solved by breaking them down into simpler subproblems (Memoization) or making locally optimal choices.

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## 🚀 How to Compile (C++)

Most solutions are written in standard C++11/C++17.

```bash
g++ -O2 -Wall -std=c++11 solution.cc -o solution
./solution < input.txt

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⚠️ Academic Integrity Disclaimer

These solutions are intended for educational review and portfolio purposes. If you are currently a student at FIB, please solve the problems yourself before consulting this repository to strictly adhere to the university's academic regulations.

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Author: Alvaro Rodriguez FIB - Universitat Politècnica de Catalunya