Optimization algorithms can see their local convergence rates deteriorate when the Hessian
at the optimum is singular. These singularities are inescapable when the optima are non …

Variational quantum algorithms rely on the optimization of parameterized quantum circuits in
noisy settings. The commonly used back-propagation procedure in classical machine …

In this paper, we present a novel stochastic normal map-based algorithm ($\mathsf
{norM}\text {-}\mathsf {SGD} $) for nonconvex composite-type optimization problems and …

In recent years, the realms of deep learning and variational quantum algorithms have
undergone significant advancements. These innovative algorithms have proven to be …

Abstract Stochastic Gradient Descent (SGD) is a widely used, foundational algorithm in data
science and machine learning. As a result, analyses of SGD abound making use of a variety …