Lectures for the "Problem Solving" Class at Nanjing University.

Efficient zero-knowledge arguments in the discrete log setting, revisited

This repository was setup to help people who believe that they solved the P vs NP problem and to help the people who review proposed solutions.

CA and Problem Sheets assigned to expedite the learning of the Theory of Computation, formally named the Theory of Algorithms. Fourth Year, Theory of Algorithms, Software Development.

A C++ program that efficiently calculates the average values of nodes at each level in a binary tree, employing a level-order traversal approach for accurate and fast computation.

A program that sorts a stack of numbers using a minimal set of operations

Push_swap is a project about sorting data on a stack, with a limited set of instructions, using the lowest possible number of actions. Its goal is reaching an optimized data sorting solution.

A public copy of the trend-prof empirical computational complexity measurement tool

This library is developed to perform efficient and exact computation of Dempster's and Fagin-Halpern conditionals (DS-Conditional-One and DS-Conditional-All in C++)

Python and LaTeX source code of my MSc thesis

P != NP

This repository contains the code used in the CS301 (Algorithms) Project which discusses the Maximum Independent Set Problem

Repositorio de tareas y prácticas de la asignatura Complejidad Computacional Facultad de Ciencias UNAM semestre 2023-1

Pushdown automaton simulator developed as an assignment for Computational Complexity course 📚 💻

This library provides a linear time and space algorithm for computing all the Fagin-Halpern conditional beliefs generated from consonant belief functions

A Java and Python project that implements and analyzes Radix Sort for integers and strings using array-based buckets, tracking primitive operations and visualizing performance trends across varying input sizes.

Python project for analyzing simplex method complexity on LPs in canonical form. Tracks FLOPs, generates problems, fits models, visualizes with Matplotlib, and caches results. Builds on work by Pavlov, Kyselov, et al. Uses Nelder-Mead optimization and MSE loss to fit empirical data to theoretical models like Borgwardt, Smoothed, and Polynomial.

📝 Solutions to the exercise sessions

This repository explores two optimization algorithms: the Traveling Salesman Problem (TSP) and Nearest Neighbor Search (NNS). It features Jupyter notebooks implementing brute-force solutions to both problems, utilizing Euclidean distance calculations and path visualizations. Ideal for learning about algorithm efficiency and optimization techniques.

Packetized Machine Learning