When solving certain evolution type PDE such as the Schrödinger equation, the Interaction Picture method is a valuable alternative to Split-Step methods. The Interaction Picture method has good computational features when used together with the standard 4th order Runge-Kutta scheme (giving rise to the RK4-IP method). In this paper we present an embedded Runge-Kutta scheme with orders 3 and 4wit...

Fejer processes are frequently used models for many iterative algorithms in optimization and related areas. They can be combined with different kinds of decomposition schemes and generate various projectiontype methods suitable for parallel computations. This paper reviews some recent results on Fejer processes with diminishing disturbances and suggests a new adaptive parameter-free stepsize co...

A Solutions for the sub-SDEs We provide analytic solutions for the split sub-SDEs in Section 4.1. For stepsize h, the solutions are given in (6). A :      θ t = θ t−1 + G 1 (θ) p h p t = p t−1

A convergence analysis of the blind adaptive step-size constant modulus algorithm (AS-CMA) for direct-sequence code division multiple access (DS-CDMA) receivers is presented. Analytical results show similar convergence behaviours of the AS-CMA receiver and the adaptive step-size stochastic approximation (ASSA) receiver. Simulations show that the blind AS-CMA algorithm performs comparably with t...

We revisit the problem of solving two-player zerosum games in the decentralized setting. We propose a simple algorithmic framework that simultaneously achieves the best rates for honest regret as well as adversarial regret, and in addition resolves the open problem of removing the logarithmic terms in convergence to the value of the game. We achieve this goal in three steps. First, we provide a...

Abstract We study distributed algorithms for solving global optimization problems in which the objective function is the sum of local objective functions of agents and the constraint set is given by the intersection of local constraint sets of agents. We assume that each agent knows only his own local objective function and constraint set, and exchanges information with the other agents over a ...

It is demonstr ated th at the chaotic properties of neural networks (in this case networks defined by the generalized delta procedur e) can be used to improve th eir learning performance. By adapt ively varying the learning-rate parameter an annealing mechanism can be introduced that is founded in chaos. T he prop osed mechanism, chaos-based learning, provides faster convergence than standard b...

Because of wake effects and geographical constraints, the search for optimal positions of wind turbines has an important part to play for their efficiency. The determination of their positions can be treated as optimization problem and can be solved by various methods. In this paper, we propose optimization approaches based on Simulated Annealing (SA) to improve solutions for the wind turbines ...

<p style='text-indent:20px;'>A generalized conditional gradient method for minimizing the sum of two convex functions, one them differentiable, is presented. This iterative relies on main ingredients: First, minimization a partially linearized objective functional to compute descent direction and, second, stepsize choice based an Armijo-like condition ensure sufficient in every iteration....