IEEE

Variable Order Fractional Gradient Descent Method and Its Application in Neural Networks Optimization

Abstract: In this paper, a novel variable order fractional gradient descent optimization algorithm is proposed, which generalizes the classical gradient descent method by introducing a kind of ...
IEEE

Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize

Abstract: This paper proposes an adaptive metric selection strategy called diagonal Barzilai-Borwein (DBB) stepsize for the popular Variable Metric Proximal Gradient (VM-PG) algorithm [1], [2]. The ...
Frontiers

The impact of gradient variable temperature fermentation on the quality of cigar tobacco leaves

Introduction: In order to enhance the quality of cigar tobacco leaves (CTLs), a gradient variable temperature fermentation approach was employed. Methods: The temperature gradient demonstrated a ...
JSTOR Daily

On the Convergence of a New Conjugate Gradient Algorithm

SIAM Journal on Numerical Analysis, Vol. 15, No. 6 (Dec., 1978), pp. 1247-1257 (11 pages) This paper studies the convergence of a conjugate gradient algorithm proposed in a recent paper by Shanno. It ...
JSTOR Daily

AN AUGMENTED MIXED FINITE ELEMENT METHOD FOR THE NAVIER-STOKES EQUATIONS WITH VARIABLE VISCOSITY

A new mixed variational formulation for the Navier-Stokes equations with constant density and variable viscosity depending nonlinearly on the gradient of velocity, is proposed and analyzed here. Our ...
marktechpost

Privacy Implications and Comparisons of Batch Sampling Methods in Differentially Private Stochastic Gradient Descent (DP-SGD)

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent ...