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. It has close relation to Muon ...

The code loads training and validation datasets from CSV files. It creates user-item rating matrices and centers the ratings by subtracting user and item means. Gradient Descent Model: Initializes ...

Nonlinear estimation algorithms are required for obtaining estimates of the parameters of a regression model with innovations having an ARMA structure. The three estimation methods employed by the ...

Abstract: With the rapid development of big data technology, sensitive data leakage has brought great privacy risks, which has become one of the severe challenges faced by both academia and industry.

Abstract: Low-rank matrix factorization is a particularly effective method which finds numerous applications in signal processing, machine learning and imaging science. Recent literature has proposed ...

This is a preview. Log in through your library . Abstract The sparsity constrained rank-one matrix approximation problem is a difficult mathematical optimization problem which arises in a wide array ...