R function bfgs( ) implementing the BFGS quasi-Newton minimization method
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Updated
Nov 19, 2021 - R
R function bfgs( ) implementing the BFGS quasi-Newton minimization method
A Single Layer Neural Network to recognize digits making use of unconstrained, non-linear optimization
Implementation of various optimization algorithms in python and numpy
Quasi-Newton particle Metropolis-Hastings
Material from the course of Static and Dynamic Optimization at ENSEM - Université de Lorraine.
Numerical Optimization Methods coursework | Institute for Applied System Analysis (2017)
Implementation of a few optimization algorithms
Estimating the 2-norm for a rectangular matrix (unconstrained approach) using two optimization algorithms: Standard gradient descent (steepest descent) method, and quasi-Newton method
R code implementing BFGS Quasi-Newton Minimization Method
An Interactive Quasi Newton Method visualization
Implementation of Gradient Type Optimization Algorithms
Basic Implementations of Optimization Algorithms
Binary Logistic Regression Analysis using the Broyden-Fletcher-Goldfarb-Shanno Algorithm on the Quasi-Newton Method
DFP method is studied.
Quasi-Newton optimization methods for Deep Learning using PyTorch-Optimizer interface.
Master 1 student work on " Non linear optimisation"
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Linear Regression and Feature Engineering, Implementation of Gradient Descent, Sub-gradient Descent, Newton Method, Quasi-Newton Method, LBFGS, Determinig Confidence Interval from Bernouli, Uniform and Normal Distribution,Dimensionality Reduction and Classification.
Weber&Davis wind solution
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