Gradient descent is a widely used paradigm for solving many optimization problems. Gradient descent aims to minimize a target function in order to reach a local minimum. In machine learning or data ...

In a recent study, BYU professors Jacob Steffen and Taylor Wells explored why some people are still reluctant to use GenAI tools. While some people might worry about an AI apocalypse, Steffen and ...

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 require the ...

Computer Scientists Discover Limits of Major Research Algorithm The most widely used technique for finding the largest or smallest values of a math function turns out to be a fundamentally difficult ...

Alternating Direction Method of Multipliers (ADMM) has been considered to be a useful alternative to Stochastic Gradient Descent (SGD).

For the proposed robust adaptive stochastic gradient descent method, a second-order directional Newton is developed to determine the step update k. Where f is the objective function, k is a unit ...