Sufficient descent conjugate gradient methods for large-scale optimization problems

Sufficient descent condition is very crucial in establishing the global convergence of nonlinear conjugate gradient method. In this paper, we modified two conjugate gradient methods such that both methods satisfy this property. Under suitable conditions, we prove the global convergence of the proposed methods. Numerical results show that the proposed methods are efficient for the given test problems.     

Convergence and comparisons of waveform relaxation methods for initial value problems of linearODE systems

In this paper we investigate waveform relaxation (WR) methods for solving initial value problems of linearODE systems. Some sufficient conditions for convergence are proposed. A class ofWR AOR, WR SOR, andWR JOR methods is defined and their convergence is discussed. The asymptotic rates of convergence of two differentWR methods are compared.     

A New Finite Element Analysis for Inhomogeneous Boundary-Value Problems of Space Fractional Differential Equations

In this paper the framework and convergence analysis of finite element methods (FEMs) for space fractional differential equations (FDEs) with inhomogeneous boundary conditions are studied. Since the traditional framework of Gakerkin methods for space FDEs with homogeneous boundary conditions is not true any more for the case of inhomogeneous boundary conditions, this paper develops a technique by introducing a new fractional derivative space in which the Galerkin method works and proves the convergence rates of the FEMs.     

两种混合共轭梯度法的全局收敛性

基于利用修正HS方法提高算法效率和利用DY方法保证算法的全局收敛性等思想,分别在不同条件下提出两种新的混合共轭梯度法求解大规模无约束优化问题.在一般Wlolfe线搜索下不需给定下降条件,证明了两个算法的全局收敛性,数值实验表明所提出算法的有效性,特别对于某些大规模无约束优化问题,数值表现较好.     

Convergence of memory gradient methods

In this paper we present a new class of memory gradient methods for unconstrained optimization problems and develop some useful global convergence properties under some mild conditions. In the new algorithms, trust region approach is used to guarantee the global convergence. Numerical results show that some memory gradient methods are stable and efficient in practical computation. In particular, some memory gradient methods can be reduced to the BB method in some special cases.     

A class of nonmonotone stabilization trust region methods

A class of trust region methods in unconstrained optimization is presented, by adopting a nonmonotone stabilization strategy. Under some regularity conditions, the convergence properties of these methods are discussed. Extensive numerical results which are reported show that these methods are very efficient.     

Effiziente Schrittweitenfunktionen bei unrestringierten Optimierungsaufgaben

In the present paper we discuss several steplength procedures from a general point of view. We consider their influence on the convergence of algorithms for the numerical treatment of optimization problems without constraints. We define efficient step-size functions and show that well known steplength procedures are efficient. Necessary and sufficient conditions for convergence of descent methods with efficient step-size functions and applications to conjugate gradient methods are given.     

Optimal Quadratic and Cubic Spline Collocation on Nonuniform Partitions

We develop optimal quadratic and cubic spline collocation methods for solving linear second-order two-point boundary value problems on non-uniform partitions. To develop optimal nonuniform partition methods, we use a mapping function from uniform to nonuniform partitions and develop expansions of the error at the nonuniform collocation points of some appropriately defined spline interpolants. The existence and uniqueness of the spline collocation approximations are shown, under some conditions. Optimal global and local orders of convergence of the spline collocation approximations and derivatives are derived, similar to those of the respective methods for uniform partitions. Numerical results on a variety of problems, including a boundary-layer problem, and a nonlinear problem, verify the optimal convergence of the methods, even under more relaxed conditions than those assumed by theory.     

一种求解高维复杂函数优化问题的混合粒子群优化算法

对于高维复杂函数,传统的确定性算法易陷入局部最小,而单一的全局随机搜索算法收敛速度慢.本文通过将粒子群优化算法和单纯形法相结合,利用前者搜索速度快、搜索范围广和后者收敛速度快的特 性,提出一种快速、易实现的混合粒子群优化算法.基于典型高维复杂函数的仿真表明:该混合算法效率高、优化性能好、对初值具有很强的鲁棒性,其性能大大优于单一的优化方法.     

Näherungsverfahren zur Ermittlung periodischer Lösungen von Systemen nichtlinearer periodischer Differentialgleichungen

Three methods (iteration, projection (P) and projection-iteration (PI) methods) with the corresponding error estimations are given to compute the forced oscillations of nonlinear periodic systems. It is proved, that the convergence conditions for the P-method are also sufficient for the convergence of the PI-method. The PI-method has the following advantages: 1. The coefficients of the approximation of orderm can be calculated immidiately from the coefficients of the foregoing approximation. 2. It is no longer necessary to solve nonlinear equations.     

Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

The conjugate gradient method is an effective method for large-scale unconstrained optimization problems. Recent research has proposed conjugate gradient methods based on secant conditions to establish fast convergence of the methods. However, these methods do not always generate a descent search direction. In contrast, Y. Narushima, H. Yabe, and J.A. Ford [A three-term conjugate gradient method with sufficient descent property for unconstrained optimization, SIAM J. Optim. 21 (2011), pp. 212–230] proposed a three-term conjugate gradient method which always satisfies the sufficient descent condition. This paper makes use of both ideas to propose descent three-term conjugate gradient methods based on particular secant conditions, and then shows their global convergence properties. Finally, numerical results are given.     

Convergence and numerical analysis of a family of two-step steffensen's methods

We provide sufficient conditions for the semilocal convergence of a family of two-step Steffensen's iterative methods on a Banach space. The main advantage of this family is that it does not need to evaluate neither any Fréchet derivative nor any bilinear operator, but having a high speed of convergence. Some numerical experiments are also presented.     

A note on convergence of semi-implicit Euler methods for stochastic pantograph equations

In the literature [1] [Existence and uniqueness of the solutions and convergence of semi-implicit Euler methods for stochastic pantograph equation, J. Math. Anal. Appl. 325 (2007) 1142–1159], Fan and Liu investigated the existence and uniqueness of the solution for stochastic pantograph equation and proved the convergence of the semi-implicit Euler methods under the Lipschitz condition and the linear growth condition. Unfortunately, the main result of convergence derived by the conditions is somewhat restrictive for the purpose of practical application, because there are many stochastic pantograph equations that only satisfy the local Lipschitz condition. In this note we improve the corresponding results in the above-mentioned reference.     

A note on Newton type iterative methods

Some particular rates of convergence, in the sense of Potra and Pták [4], are related to Newton type iterative methods which solve nonlinear operator equations in Banach spaces. This allows to obtain convergence conditions and a posteriori error estimates at the same time. The applicability of the estimates thus found is studied and their behaviour illustrated by numerical examples.     

On asymptotic properties of a constant-step-size sign-error algorithm for adaptive filtering

This work is devoted to asymptotic properties of a sign-error adaptive filtering algorithm with constant step size. Under much weaker conditions than those that appear in the literature, we obtain convergence and rate of convergence by using weak convergence methods. An example is provided to demonstrate the performance of the algorithm.     

Konvergenz impliziter Mehrschrittverfahren zur numerischen Lösung quasilinearer Anfangswertaufgaben

Generalizing the theory of linear innitial-value problems, formulated by Lax and Richtmyer [7], necessary conditions for solvability and convergence of a large class of implicit finite-difference methods for the numerical solution of certain quasilinear initial-value problems are given. In addition an equivalence theorem for these methods is proved.     

A unified framework for the construction of various matrix multisplitting iterative methods for large sparse system of linear equations

A unified framework for the construction of various synchronous and asynchronous parallel matrix multisplitting iterative methods, suitable to the SIMD and MIMD multiprocessor systems, respectively, is presented, and its convergence theory is established under rather weak conditions. These afford general method models and systematical convergence criterions for studying the parallel iterations in the sense of matrix multisplitting. In addition, how the known parallel matrix multisplitting iterative methods can be classified into this new framework, and what novel ones can be generated by it are shown in detail.     

Recurrence relations for rational cubic methods II: The Chebyshev method

We continue the analysis of rational cubic methods, initiated in [7]. In this paper, we obtain a system of a priori error bounds for the Chebyshev method in Banach spaces through a local convergence theorem that provides sufficient conditions on the initial point in order to ensure the convergence of Chebyshev iterates. The error estimates are exact for second degree polynomials. We also discuss some applications.     

具有参数跟踪自适应观测器的故障检测与诊断系统

本文简单概括了故障检测与诊断的方法,介绍了具有参数自适应跟踪观测器的故障检测与诊断系统,分析了收敛性和渐近稳定性的条件,分别对参数匹配和不匹配两种情况的故障检测与诊断问题进行了讨论.     

Rate of convergence for the Legendre pseudospectral optimal control of feedback linearizable systems

Pseudospectral (PS) computational methods for nonlinear constrained optimal control have been applied to many industrial-strength problems, notably, the recent zero-propellant-maneuvering of the international space station performed by NASA. In this paper, we prove a theorem on the rate of convergence for the optimal cost computed using a Legendre PS method. In addition to the high-order convergence rate, two theorems are proved for the existence and convergence of the approximate solutions. Relative to existing work on PS optimal control as well as some other direct computational methods, the proofs do not use necessary conditions of optimal control. Furthermore, we do not make coercivity type of assumptions. As a result, the theory does not require the local uniqueness of optimal solutions. In addition, a restrictive assumption on the cluster points of discrete solutions made in existing convergence theorems is removed.