Renato Maynard Etchepare

Guide to set up DeepStream pyds and run an USB pipeline in Python. Note: NVIDIA Smart Recording isn’t in official Python bindings and is supported only in C++. This repo includes a compatible wheel and tweaks so you can install it easily and enable Smart Recording from Python.

Traveling Salesman Problem DFJ MTZ GG formulations

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VRP-Models-and-Algorithms Models and algorithms for solving Vehicle Routing Problems using optimization and heuristics.

A Python implementation of a branch-and-bound approach (plus a simple greedy heuristic) to solve a variation of the multiple knapsack problem where items have both individual and pairwise benefits.

Guide to set up DeepStream pyds and run an RTSP pipeline in Python. Note: NVIDIA Smart Recording isn’t in official Python bindings and is supported only in C++. This repo includes a compatible wheel and tweaks so you can install it easily and enable Smart Recording from Python.

A mixed-integer programming model for an integrated production planning problem with preventive maintenance in the pulp and paper industry

Multi-camera DeepStream reference with dynamic layouts (mosaic, PiP, focus view), zero-copy paths, and latency-optimized settings. Includes config examples and layout switching without pipeline teardown.

Optimal routing and delivery solutions using Google Maps and Python.

This repository provides a comprehensive implementation of unsupervised machine learning models using PyTorch and Scikit-learn. It includes end-to-end workflows for both classification and regression tasks, covering data preprocessing, model training, evaluation, and comparison between traditional ML models

Statistical Modeling and Regression Analysis for Life Expentancy

A Multiple-population coarse-grained Genetic Algorithm to solve the Quadratic Assignment Problem

This repository contains a Genetic Algorithm (GA) implementation for solving the Traveling Salesman Problem (TSP).

Time Series Forecasting Methods — A collection of Python implementations for essential time series forecasting techniques, including Simple, Double, Triple Exponential Smoothing, and Moving Averages.

A Gurobi implementation that tracks upper and lower bounds for any Mixed-Integer Programming (MIP) problem using callbacks. Includes visualization with Matplotlib.

Linear Programming model for Production Planning with full Sensitivity Analysis, including shadow prices, reduced costs, and resource bounds.

This repository provides a comprehensive implementation of supervised machine learning models using PyTorch and Scikit-learn. It includes end-to-end workflows for both classification and regression tasks, covering data preprocessing, model training, evaluation, and comparison between traditional ML models