HPC solver for nonlinear optimization problems

MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

Nonlinear Equation Solver with Modern Fortran

A library that provides routines to compute the solutions to systems of nonlinear equations.

Fast Change Point Detection in R

Unconstrained optimization algorithms in python, line search and trust region methods

Linear regression with the LBFGSB (Limited-memory Broyden-Fletcher-Goldfarb-Shanno BFGS) solver method is a numerical optimization method used to find the minimum of an objective function. It is a gradient descent algorithm that uses an approximation of the Hessian matrix to minimize the function.

Optimization algorithms for inverse problems.

(Python, Tensorflow, R, C, C++) Stochastic, limited-memory quasi-Newton optimizers (adaQN, SQN, oLBFGS)

Newton-type accelerated proximal gradient method in Julia

Trust-region methods with partitioned quasi-Newton approximations

A matlab function for steepest descent optimization using Quasi Newton's method : BGFS & DFP

Quasistatic Fracture using Nonlinear-Nonlocal Elastostatics with an Explicit Tangent Stiffness Matrix

Index of different Optimization Methods

Optimization course assignments under the supervision of Dr. Maryam Amirmazlaghani

Implementation of the vc-sqnm and sqnm optimization algorithms in C++

Stochastic Second-Order Methods in JAX

Newton’s second-order optimization methods in python

A header-only C++ Library for Optimization Algorithms

The BFGS Algorithm is studied.

Stochastic Second-Order Methods in JAX (adahessian)

Repository for machine learning problems implemented in python

Provides quasi-Newton methods to minimize partially separable functions. The package includes both a header-only C++ interface and a R interface.

Implementation of Unconstrained minimization algorithms. These are listed below:

Basic Implementations of Optimization Algorithms

Repository for project report of numerical analysis course assignment in Faculty of Computer Science UI

stepopt: A pedagogical optimisation library

Implementation of the quasi Cauchy optimizer, an optimization method from the quasi Newton family. It uses a diagonal approximation of the Hessian and therefore has a small memory footprint.

Numerical analysis functions in MATLAB for interpolation, approximation, differentiation, integration, and solving systems of nonlinear equations.

Implementation of Gradient Type Optimization Algorithms