Abstract: We propose a nanophotonic device inverse design method based on the gradient descent algorithm. The method is similar to the adjoint method, while the gradient is calculated by the python ...

This repo includes an implementation of the penalty-based bilevel gradient descent (PBGD) algorithm presented in the paper On Penalty-based Bilevel Gradient Descent Method, along with several other ...

Abstract: Aitken gradient descent (AGD) algorithm takes some advantages over the standard gradient descent and Newton methods: 1) can achieve at least quadratic convergence in general; 2) does not ...

Gradient ascent aims to identify and maximise the peak of a function by moving in the direction of the gradient. The method requires a specified and differentiable function to evaluate its gradients ...

This article is about the gradient descent algorithm and the different alternatives that can be used instead of the gradient descent algorithm. Gradient descent is a popular optimisation technique in ...

One key ingredient in deep learning is the stochastic gradient descent (SGD) algorithm, which allows neural nets to find generalizable solutions at flat minima of the high-dimensional loss function.

The Rosenbrock function optimization belongs to unconstrained optimization problems, and its global minimum value is located at the bottom of a smooth and narrow valley of the parabolic shape. It is ...