Designing recurrent neural networks by unfolding an l1-l1 minimization algorithm
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Updated
Oct 4, 2019 - Python
Designing recurrent neural networks by unfolding an l1-l1 minimization algorithm
🧮 Also known as the Quine-McCluskey method
a python code that uses the randomness of neural network training to find the ground state of a harmonic oscillator and its energy
Given a network, equipped with a specific total and strict order, we get the minimal "round trip" traversal length. It always converges, at quadratic cost.
Frontend de la aplicacion de minimizacion de AFD
Optimization Methods in Management Science assignment: Solving a vehicle routing problem
These are my university projects on the discipline "Numerical methods". I implemented various algorithms of numeric calculations such as: interpolation, approximation, integral calculation, solving equations, solving systems of linear algebraic equations (SLAE), minimization of quadratic function
A library for fitting a sequence of electrochemical impedance spectra (JAX version).
This project aims at providing the optimal solution for minimum transportation costing based on two decision variables along with a pictorial overview of the problem. The project uses Excel for dataset cleaning and manipulation as well as python programming for simplex solution and finally, SAS Visual Analytics for visualization.
Automate Quantum Espresso routines
A tic tac toe game with an AI opponent using minimax algorithm and alpha-beta pruning. Reference link : https://en.wikipedia.org/wiki/Minimax#Minimax_algorithm_with_alternate_moves
a lightweight header-only C++17 library of numerical optimization methods for nonlinear functions based on Eigen
Una implementación inicial del algoritmo de Quine McCLuskey para reducir expresiones logicas.
example code for how to run a best-of-random differential evolution optimization method in Fortran
Various algorithms, without explanation of their work
This is a repository associated with the chapter book "Towards optimal sampling for learning sparse approximations in high dimensions" by Ben Adcock, Juan M. Cardenas, Nick Dexter and Sebastian Moraga to be published by Springer in late 2021, available at https://arxiv.org/abs/2202.02360
collection of numerical optimization methods
Project to assist students in writing their lab work for the 1st year, 2nd semester (discrete mathematics).
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