Newton’s second-order optimization methods in python
Stochastic Second-Order Methods in JAX
This was a project case study on nonlinear optimization. We implemented the Stochastic Quasi-Newton method, the Stochastic Proximal Gradient method and applied both to a dictionary learning problem.
Implementation of Unconstrained minimization algorithms. These are listed below:
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.
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Final project for the course O4DS at università di Pisa for the A.Y 2023/2024. In this project we explore the problem of estimating the matrix 2-norm as an unconstrained optimization problem using Steepest Descent and BFGS method.
A Python library for large-scale convex composite optimization.
Quasi-Newton particle Metropolis-Hastings
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