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

This monograph covers some recent advances in a range of acceleration techniques
frequently used in convex optimization. We first use quadratic optimization problems to …

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 this paper, we propose a distributed algorithm for computation of a generalized Nash
equilibrium (GNE) in noncooperative games over networks. We consider games in which the …

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 this article, we introduce an inertial projection and contraction algorithm by combining
inertial type algorithms with the projection and contraction algorithm for solving a variational …

The paper presents a fully adaptive algorithm for monotone variational inequalities. In each
iteration the method uses two previous iterates for an approximation of the local Lipschitz …

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 consider optimization problems over the Stiefel manifold whose objective function is the
summation of a smooth function and a nonsmooth function. Existing methods for solving this …

We present a dynamical system framework for understanding Nesterov's accelerated
gradient method. In contrast to earlier work, our derivation does not rely on a vanishing step …