Na Rinha de Algoritmos você deve utilizar suas habilidades para a criação de algoritmos eficientes para resolver problemas!
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Updated
Mar 3, 2024 - Python
Na Rinha de Algoritmos você deve utilizar suas habilidades para a criação de algoritmos eficientes para resolver problemas!
Parallel Tabu Search and Genetic Algorithm for the Job Shop Schedule Problem with Sequence Dependent Set Up Times
OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.
Repository of scripts and data for the "Robustness and resilience of complex networks" paper by Oriol Artime, Marco Grassia, Manlio De Domenico, James P. Gleeson, Hernán A. Makse, Giuseppe Mangioni, Matjaž Perc and Filippo Radicchi, published at Nature Review Physics (2024). https://doi.org/10.1038/s42254-023-00676-y
Repository of the paper "Machine learning dismantling and early-warning signals of disintegration in complex systems" by M. Grassia, M. De Domenico and G. Mangioni
A particle swarm optimization algorithm implementation with simultaneous pickup and drop for medicines distribution management.
Go (golang) bindings for Picosat, the satisfiability solver
[IEEE TKDE | TITS 2023] "Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling" | "Neural Airport Ground Handling"
A hybrid genetic algorithm for the job shop scheduling problem
Heuristics and metaheuristisc algorithms to the famous np-hard Travelling Salesman Problem (TSP) . Made in C++ and experimented in detail with Jupyter Notebook.
Solving Single Machine Schenduling problem with different approaches
a collection of benchmarks (in DIMACS format) for various NP-Complete problems
A branch-and-bound, and A* type algorithm that solves the NP Hard Scheduling problem with the highest possible performance
A Certifier algorithm to check a particular solution to the NP-Complete 3-Sat problem
Russian Doll Search for Computing Maximum Vertex Weight Hereditary Structures in Graphs. Now with OpenMP support.
A Quantum Approximation algorithm for finding the Max-Cut of directed weighted graphs.
Algorithms Part - I and Part - II Courses by Princeton University - Coursera Community
This repo encapsulates a Python implementation of the Simulated Annealing Algorithm to solve by means of a "minimum energy state" heuristic the NP-hard n-machines|no preemption|C_max job shop scheduling problem, considering n=2 machines and jobs having release dates. The code was designed and wrote by me. The whole heuristic design, complexity a…
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