Abstract The Extreme Learning Machine is a single-hidden-layer feedforward learning
algorithm, which has been successfully applied in regression and classification problems in …

Gradient-based optimization algorithms can be studied from the perspective of limiting
ordinary differential equations (ODEs). Motivated by the fact that existing ODEs do not …

In a real Hilbert space H, in order to develop fast optimization methods, we analyze the
asymptotic behavior, as time t tends to infinity, of a large class of autonomous dissipative …

In a Hilbert space setting, for convex optimization, we analyze the convergence rate of a
class of first-order algorithms involving inertial features. They can be interpreted as discrete …

We study stochastic gradient descent (SGD) and the stochastic heavy ball method (SHB,
otherwise known as the momentum method) for the general stochastic approximation …

Accelerated optimization methods, such as Nesterov's accelerated gradient method, play a
significant role in optimization. Several accelerated methods are provably optimal under …

In a Hilbert space setting ℋ, given Φ: ℋ→ ℝ a convex continuously differentiable function,
and α a positive parameter, we consider the inertial dynamic system with Asymptotic …

We study gradient-based optimization methods obtained by directly discretizing a second-
order ordinary differential equation (ODE) related to the continuous limit of Nesterov's …

We propose\sf ZeroFPR, a nonmonotone linesearch algorithm for minimizing the sum of two
nonconvex functions, one of which is smooth and the other possibly nonsmooth.\sf ZeroFPR …

The paper presents four modifications of the inertial forward–backward splitting method for
monotone inclusion problems in the framework of real Hilbert spaces. The advantages of our …