Discover a powerful gradient-based iterative algorithm for solving complex matrix equations. Explore convergence proofs, optimal conditions, and a numerical example to validate its efficacy.

This method reduces gradient storage and processing requirements brought by MBGD and is composed of a batch of randomly generated rank-1 matrices of forward propagated activations and backpropagated ...

The effects of thermal gradient on dynamical behavior of nanoparticles dispersed in polymer matrix were studied by X-ray photon correlation spectroscopy.

PolarGrad (Polar Gradient methods; Lau et al., 2025) is a class of matrix-gradient optimizers based on the concept of gradient-anisotropy preconditioning in optimization.

In this paper, by constructing an objective function and using the gradient search, three gradient-based iterations are established for solving generalized coupled Sylvester matrix equations, when the ...

Learn how to use matrix factorization and gradient descent to build recommender systems in Python. Discover the advantages and disadvantages of this technique.

The FLUX neural network framework is designed around a custom matrix mathematical library. The core idea is to manage and process data in the form of matrices, allowing for efficient and scalable ...