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Newton’s method is usually preferred when solving optimization problems due to its superior convergence properties compared to gradient-based or derivative-free optimization algorithms. However, de...

Having the capability to estimate parametric and non-parametric uncertainties, this paper investigates a regressor-free adaptive model-reference scheme for impedance control of robotic systems inte...

The purpose of this paper is to study a new viscosity iterative algorithm for finding a common element of the set of fixed points of an asymptotically nonexpansive mapping and the set of solutions ...

In this paper, we propose a generalized viscosity explicit method for finding zeros of the sum of two accretive operators in the framework of Banach spaces. The strong convergence theorem of such m...

In this paper, we introduce two inertial Tseng's extragradient algorithms with the Armijo-like step size rule for solving variational inequality problems involving monotone and Lipschitz continuous...

We consider a quadratic optimal control problem for the 3D Navier-Stokes-Voigt equations with periodic inputs. We prove the existence of optimal solutions, then establish necessary and sufficient o...

In this article, complete and complete integral convergence theorems are obtained for arrays of row wise widely negative dependent random variables under the sub-linear expectations. We improve the...

In this paper, we study the convergence of a coefficient inverse problem for the two dimensional semi-discrete damped wave equation. Based on the new discrete Carleman estimates for the semi-discre...

In this paper, △-convergence and strong convergence theorems for asymptotically quasi-nonexpansive sequences in Hadamard spaces are derived. The results extend and improve recent results in the lit...

Large sectors of the recent optimization literature focused in the last decade on the development of optimal stochastic first-order schemes for constrained convex models under progressively relaxed...

Let {Yi;−∞

We study the locally uniform convergence from a family of pullback random attractors to a deterministic attractor. We establish criteria by using joint convergence of the cocycles, collective local...

Waveform relaxation method at the PDE level can simplify the original differential system by several decoupling approaches, which is very useful for the iterative solution of nonlinear differential...

In this paper, we study the long-time behavior of the chemotaxis-Navier–Stokes system ∂tn+u⋅∇n=λΔn−∇⋅(χ(c)n∇c),∂tc+u⋅∇c=μΔc−f(c)n,∂tu+u⋅∇u+∇P=ζΔu−n∇φ,∇⋅u=0,t>0,x∈Ω posed in a strip domain Ω:=R2×[0,...

In this paper, a novel variable structure control (VSC) approach is investigated for a class of mismatched uncertain systems. The VSC approach, which includes output variables only, shows finite-ti...

In this paper, based on inequalities for the maximum of the partial sums of m-asymptotically almost negatively associated random vectors in Hilbert space, we establish various kinds of strong laws ...

In this paper, we propose a new family of continuous-time optimisation algorithms based on discontinuous second-order gradient optimisation flows, with finite-time convergence guarantees to local o...

SIMSEK, Necip/0000-0003-3061-5770; Erturk, Muzeyyen/0000-0002-5328-7995 WOS: 000532204500001 In this paper we propose an S-iterative algorithm for finding the common element of the set of solutions of the variational inequalities and the set of fixed points of nonexpansive mappings. We study the convergence criteria of the mentioned algorithm under some mild conditions. As an application, a modified algorithm is suggested to solve convex minimization problems. Numerical examples are given to validate the theoretical findings obtained herein. Our results may be considered as an improvement, refinement and complement of the previously known results.

The main purpose of this paper is to obtain the complete moment convergence for widely orthant dependent linear processes with random coefficients in form Xt=∑j=−∞∞Ajet−j, where {en,n∈ℤ} is a seque...

The proximal point algorithm (PPA) has been widely used in convex optimization. Many algorithms fall into the framework of PPA. To guarantee the convergence of PPA, however, existing results conven...

In this paper, we first consider the split common null point problem in two Banach spaces. Then, using the Bregman generalized resolvents of maximal monotone operators, we prove strong convergence ...

We study forward asymptotic autonomy of a pullback random attractor for a non-autonomous random lattice system and establish the criteria in terms of convergence, recurrence, forward-pullback absor...

We derive and analyse a new variant of the iteratively regularized Landweber iteration, for solving linear and nonlinear ill-posed inverse problems. The method takes into account training data, which are used to estimate the interior of a black box, which is used to define the iteration process. We prove convergence and stability for the scheme in infinite dimensional Hilbert spaces. These theoretical results are complemented by several numerical experiments for solving linear inverse problems for the Radon transform and a nonlinear inverse problem for Schlieren tomography.

In this paper, using the hybrid method defined by Nakajo and Takahashi, we first obtain a strong convergence theorem for two noncommutative generic skew generalized nonspreading mappings in a Banac...

The modified anomalous sub-diffusion equation (MASDE) describes processes that become less anomalous as time progresses. In this paper, based on Crank-Nicolson and weighted and shifted Grunwald dif...

In this article, the complete convergence for the maximum of weighted sums of widely negative orthant dependent (WNOD, in short) random variables is investigated under some suitable moment conditio...

The impact of various parameters in the coarse mesh finite difference (CMFD) acceleration method on overall convergence behavior is investigated through numerical calculations using the method of c...

Liu, Xiaoping/0000-0003-1808-4233 In this paper, the problem of finite-time attitude-tracking control for a rigid spacecraft is addressed. Uncertainties including unknown inertial parameters, external disturbances, actuator failures and saturation constraints are considered. Firstly, a smooth function which is different from the common saturation treatment is presented to deal with the actuator constraints. Secondly, a fast non-singular terminal sliding mode (FNTSM) manifold composed of tracking errors is constructed. To estimate the unknown function in the sliding surface derivative, Chebyshev neural network (CNN) is introduced and thus the strict assumptions on uncertainties in many related works are abolished. By designing the CNN adaptive laws, a new fault-tolerant control scheme is proposed such that the attitude tracking can be achieved within a limited time interval. Compared with the existing CNN-based achievements with finite-time convergence, the approximation errors are proved to be finite-time stable instead of uniformly ultimately bounded (UUB). Finally, simulation experiments are conducted to demonstrate the satisfactory tracking performance of the attitude controller. National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61773108]; NSERC of CanadaNatural Sciences and Engineering Research Council of Canada (NSERC) This work was supported by the National Natural Science Foundation of China: [Grant Number 61773108]; the NSERC of Canada: [Grant Number Xiaoping Liu].

Butterfly Optimisation Algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence...

We present new numerical schemes for pricing perpetual Bermudan and American options as well as $\alpha$-quantile options. This includes a new direct calculation of the optimal exercise barrier for early-exercise options. Our approach is based on the Spitzer identities for general L\'evy processes and on the Wiener-Hopf method. Our direct calculation of the price of $\alpha$-quantile options combines for the first time the Dassios-Port-Wendel identity and the Spitzer identities for the extrema of processes. Our results show that the new pricing methods provide excellent error convergence with respect to computational time when implemented with a range of L\'evy processes., Comment: 29 pages, 14 figures, 5 tables

The alternating direction method of multipliers (ADMM) is a classical effective method for solving two-block convex optimization subject to linear constraints. However, its convergence may not be g...

The confinement loss plays an important role that connects two two-dimensional planes, cross-section, and longitudinal section. In the analysis of cross-section, the two-dimensional plane–strain co...

This paper presents a novel current injection-based Newton-Raphson power flow algorithm with new PV bus representation (rCINR−PV) for improving the convergence characteristics. In this proposed alg...

In this paper, we propose an over-relaxed proximal scaled gradient algorithm for solving the non-smooth composite optimization problem in Hilbert space. We prove the strong convergence and the boun...

In this paper, we study a different class of bivariate α-fractal functions. First, we introduce the bivariate Bernstein α-fractal functions that are more suitable to approximate both smooth and non...

In this paper, we extend the study of an iteration process introduced by Thakur-Thakur-Postolache [J. Inequal. Appl., (2014), 147–155] from the class of nonexpansive operators to the more general c...

In this paper, the 3-D full compressible micropolar fluid with positive constant viscosity but with zero heat conductivity is considered. Global well-posedness and optimal convergence rates are est...

For a double array {Vm,n, m≥1, n≥1} of random elements taking values in a real separable Banach space, we provide sufficient conditions for the double series ∑i=1m∑j=1nVi,j to converge almost certa...

A sliding mode observer (SMO) design and convergence analysis are presented in this paper, which includes a rigorous treatment to address multiple discontinuities in the resulting estimation error ...

The method of successive under-relaxation (SUR) is an effective way for numerically solving a nonlinear system of equations governing the fluid flow and heat transfer, resulting in stable convergen...

The EM algorithm is an effective reconstruction method for SPECT, because it can incorporate various physical factors into reconstruction. However, the convergence rate is very slow. One of the acc...

In this paper, a novel, dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with reference tracking controllers. This adds a deliberative component to the obstacle avoidance guarantees present in the dynamic window approach as well as allow for the inclusion of complex robot models. The proposed algorithm allows for guaranteed convergence to a goal location while navigating through an unknown environment at relatively high speeds. The framework is applied in both simulation and hardware implementation to demonstrate the computational feasibility and the ability to cope with dynamic constraints and stability concerns.

We consider a neural network approach to an inverse variational inequality which is assumed to have a non-empty set of solutions. In the case of gradient mappings, we prove that every trajectory of...

In the stochastic network model of Britton and Lindholm [Dynamic random networks in dynamic populations. Journal of Statistical Physics, 2010], the number of individuals evolves according to a supercritical linear birth and death process, and a random social index is assigned to each individual at birth, which controls the rate at which connections to other individuals are created. We derive a rate for the convergence of the degree distribution in this model towards the mixed Poisson distribution determined by Britton and Lindholm based on heuristic arguments. In order to do so, we deduce the degree distribution at finite time and derive an approximation result for mixed Poisson distributions to compute an upper bound for the total variation distance to the asymptotic degree distribution., 43 pages

Nonlinear diffusion acceleration (NDA), also known as coarse-mesh finite difference, is a well-known technique that can be applied to accelerate the scattering convergence in neutronics calculation...

In this paper, we will study the complete and complete moment convergence for double-indexed randomly weighted sums of ρ∗-mixing random variables. Several sufficient conditions to prove the complet...

The offering of demanding telecommunication services as promised by the 5G specifications raise the necessity for high capacity, flexible, adaptive, and power conserving fronthaul. Toward t...

In recent years, the Forward-Backward algorithm (FBA) received much attention due to its various applications in image recovery, signal processing, and machine learning. In this paper, we consider ...

The Markov chain approach to the elitist genetic algorithm enables not only to prove its convergence to an equilibrium distribution, but also to establish its convergence rate. These conver...

We consider the Cauchy problem ut=uxx+f(u), x∈R,t>0,u(x,0)=u0(x), x∈R, where f is a C1 function on R with f(0)=0, and u0 is a nonnegative continuous function on R whose limits at ±∞ are equal to 0....