Rodrigo Oliveira's Projects
Apache Airflow
C and C++ implementations of well known algorithms and data structures
Auction system using Zookeeper
Automate linux software and environmet setups
A curated list of awesome Python frameworks, libraries, software and resources
The uncompromising Python code formatter
A completely text-based physics sandbox world
Graduation projects in C++ and GLSL made with Qt and OpenGL
Create badges/shields for your Python test coverage!
Dockerfile with Automated Builds that has pyenv built in for easy versioning of Python! :)
Drone plugin for publishing files to Amazon S3
Embeddings as spatial networks
Sistema gerenciador de matrículas UFABC
Extremely Randomized Trees algorithm implementation in Python
The fastai deep learning library, plus lessons and tutorials
Easy Swagger UI for your Flask API
Image Similarity Deep Ranking in Pytorch
Python Dependency Injection for Humans™
Motifs and metamotifs discovering tool
Provide objects that only initializes when actually used
Prometheus mining metrics exporter
Useful extensions to the standard Python unittest.mock features
Modular Genetic and Modular Particle Swarm Optimization Algorithms
Bloom for Spring-Data Mongo
Monogame Daydreams
motionless is a Python library that takes the pain out of generating Google Static Map URLs.
Combinatorial optimization algorithms written in Python 3.4 for solving timetabling problem scenarios. In this work algorithms for solving NP-Complete combinatorial problems of timetabling were made based on literature, which describes the relevant methods created until now for solving these kind of problems. The intention of this project was to compare the main algorithms described in the literature on solving timetabling problems. The scenarios provided for the algorithms are generated by another algorithm, also created in this work. By knowledge limitations of the author the structured programming paradigm was used to make all the algorithms, because of that the scenario generation algorithm demanded a lot of more time than expected to code it, this is also because of the complexity of variables that this algorithm is capable of generating in order to be as close as possible to reality. Provided that, not all studied algorithms were created, in fact just 2 algorithms came to be realeased, which comprimised the main goal of this project: comparing the computational efficiency of the main algorithms described in literature. Although the work on the development of the scenario generation algorithm was successful. This work is product of the undergraduation research program PDPD (in portuguese, Pesquisando Desde o Primeiro Dia) performed by Rodrigo Martins de Oliveira, with guidance of Jesús Pascual Mena-Chalco, at Universidade Federal do ABC, which the author thanks for the financial assitance over the period from November 2013 to July 2014.
SCIP Optimization Models