Python implementation of Mercer Features for Efficient Combinatorial Bayesian Optimization

This repository contains various implementations of algorithms related to network flows.

Energy Grid Optimization and Analysis (EGOA)

Teaching material that I prepared as a TA for the laboratories of the Network Dynamics and Learning course for the MSc in Data Science and Engineering at Politecnico di Torino.

A Python module for generating random network flows problem instances in DIMACS graph format.

LaTeX source code, figures, plots, presentations, and scripts of the PhD thesis "Combinatorial Problems in Energy Networks - Graph-theoretic Models and Algorithms".

R package for the evaluation of the (weighted, normalised) efficiency of network flows.

Personal Notes on two papers dealing with approximation algorithms for the multicommodity flow problem

Implementation of 2 apps, using Mininet as a Virtual Network Simulator and OpenDaylight Software-Defined-Networking Controller.

Implementation of maximum flow algorithm from Network flows book by Ahuja, Magnanti, Orlin

A set of presentations and lectures in different fields.

A webapp to visualize the Ford-Fulkerson and Edmonds-Karp max flow algorithms. Below is the link to the website!

Network-flows: a c++ command line tool for network optimization problems

Network traffic analysis, traffic characteristics extraction, flow migration and evaluation

Network Flows Homeworks. January-June 2019. Graduate Program in System Engineering. UANL

A set of posters in different fields.

Monetary Worth Maximization, Cost Minimization and Network Flow cases; where LP approach is applied

Coursera Data Structures and Algorithms Specialization course 5 --> Advanced Algorithms and Complexity... Code is completed in python only.