Implementation of Gradient Type Optimization Algorithms
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Updated
Apr 24, 2017 - MATLAB
Implementation of Gradient Type Optimization Algorithms
Newton’s second-order optimization methods in python
R function bfgs( ) implementing the BFGS quasi-Newton minimization method
Basic Implementations of Optimization Algorithms
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
Repository for project report of numerical analysis course assignment in Faculty of Computer Science UI
Material from the course of Static and Dynamic Optimization at ENSEM - Université de Lorraine.
Implementation of the vc-sqnm and sqnm optimization algorithms in C++
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
DFP method is studied.
Numerical analysis functions in MATLAB for interpolation, approximation, differentiation, integration, and solving systems of nonlinear equations.
Quasi-Newton optimization methods for Deep Learning using PyTorch-Optimizer interface.
Binary Logistic Regression Analysis using the Broyden-Fletcher-Goldfarb-Shanno Algorithm on the Quasi-Newton Method
Master 1 student work on " Non linear optimisation"
Provides quasi-Newton methods to minimize partially separable functions. The package includes both a header-only C++ interface and a R interface.
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