A New Class of Nonlinear Conjugate Gradient Methods with Global Convergence Properties

非线性共轭梯度法由于其迭代简单和储存量小,且搜索方向不需要满足正割条件,在求解大规模无约束优化问题时占据及其重要的地位.提出了一类新的共轭梯度法,其搜索方向是目标函数的下降方向.若假设目标函数连续可微且梯度满足Lipschitz条件,线性搜索满足Wolfe原则,讨论了所设计算法的全局收敛性.

ON THE GLOBAL CONVERGENCE OF CONJUGATE GRADIENT METHODS WITH INEXACT LINESEARCH

In this paper we consider the global convergence of any conjugate gradient method of the form d1=-g1,dk+1=-gk+1+βkdk(k≥1)with any βk satisfying sume conditions,and with the strong wolfe line search conditions.Under the convex assumption on the objective function,we preve the descenf property and the global convergence of this method.

GLOBAL CONVERGENCE OF THE GENERAL THREE TERM CONJUGATE GRADIENT METHODS WITH THE RELAXED STRONG WOLFE LINE SEARCH

The global convergence of the general three term conjugate gradient methods with the relaxed strong Wolfe line search is proved.

A CLASSOF NONMONOTONE CONJUGATE GRADIENT METHODSFOR NONCONVEX FUNCTIONS

This paper discusses the global convergence of a class of nonmonotone conjugate gra- dient methods(NM methods) for nonconvex object functions.This class of methods includes the nonmonotone counterpart of modified Polak- Ribière method and modified Hestenes- Stiefel method as special cases

CONVERGENCE ANALYSIS ON A CLASS OF CONJUGATE GRADIENT METHODS WITHOUTSUFFICIENT DECREASE CONDITION

Recently, Gilbert and Nocedal([3]) investigated global convergence of conjugate gradient methods related to Polak-Ribiere formular, they restricted beta(k) to non-negative value. [5] discussed the same problem as that in [3] and relaxed beta(k) to be negative with the objective function being convex. This paper allows beta(k) to be selected in a wider range than [5]. Especially, the global convergence of the corresponding algorithm without sufficient decrease condition is proved.

Conjugate Gradient Methods with Armijo-type Line Searches

Two Armijo-type line searches are proposed in this paper for nonlinear conjugate gradient methods. Under these line searches, global convergence results are established for several famous conjugate gradient methods, including the Fletcher-Reeves method, the Polak-Ribiere-Polyak method, and the conjugate descent method.

A New Nonlinear Conjugate Gradient Method for Unconstrained Optimization Problems

In this paper,an efficient conjugate gradient method is given to solve the general unconstrained optimization problems,which can guarantee the sufficient descent property and the global convergence with the strong Wolfe line search conditions.Numerical results show that the new method is efficient and stationary by comparing with PRP＋ method,so it can be widely used in scientific computation.

An Adaptive Spectral Conjugate Gradient Method with Restart Strategy

As a generalization of the two-term conjugate gradient method(CGM),the spectral CGM is one of the effective methods for solving unconstrained optimization.In this paper,we enhance the JJSL conjugate parameter,initially proposed by Jiang et al.(Computational and Applied Mathematics,2021,40:174),through the utilization of a convex combination technique.And this improvement allows for an adaptive search direction by integrating a newly constructed spectral gradient-type restart strategy.Then,we develop a new spectral CGM by employing an inexact line search to determine the step size.With the application of the weak Wolfe line search,we establish the sufficient descent property of the proposed search direction.Moreover,under general assumptions,including the employment of the strong Wolfe line search for step size calculation,we demonstrate the global convergence of our new algorithm.Finally,the given unconstrained optimization test results show that the new algorithm is effective.

A Scaled Conjugate Gradient Method Based on New BFGS Secant Equation with Modified Nonmonotone Line Search

In this paper, we provide and analyze a new scaled conjugate gradient method and its performance, based on the modified secant equation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method and on a new modified nonmonotone line search technique. The method incorporates the modified BFGS secant equation in an effort to include the second order information of the objective function. The new secant equation has both gradient and function value information, and its update formula inherits the positive definiteness of Hessian approximation for general convex function. In order to improve the likelihood of finding a global optimal solution, we introduce a new modified nonmonotone line search technique. It is shown that, for nonsmooth convex problems, the proposed algorithm is globally convergent. Numerical results show that this new scaled conjugate gradient algorithm is promising and efficient for solving not only convex but also some large scale nonsmooth nonconvex problems in the sense of the Dolan-Moré performance profiles.

A hybrid conjugate gradient method for optimization problems

A hybrid method of the Polak-Ribière-Polyak (PRP) method and the Wei-Yao-Liu (WYL) method is proposed for unconstrained optimization pro- blems, which possesses the following properties: i) This method inherits an important property of the well known PRP method: the tendency to turn towards the steepest descent direction if a small step is generated away from the solution, preventing a sequence of tiny steps from happening;ii) The scalar holds automatically;iii) The global convergence with some line search rule is established for nonconvex functions. Numerical results show that the method is effective for the test problems.

A New Two-Parameter Family of Nonlinear Conjugate Gradient Method Without Line Search for Unconstrained Optimization Problem

This paper puts forward a two-parameter family of nonlinear conjugate gradient(CG)method without line search for solving unconstrained optimization problem.The main feature of this method is that it does not rely on any line search and only requires a simple step size formula to always generate a sufficient descent direction.Under certain assumptions,the proposed method is proved to possess global convergence.Finally,our method is compared with other potential methods.A large number of numerical experiments show that our method is more competitive and effective.

A Descent Gradient Method and Its Global Convergence

Y Liu and C Storey(1992)proposed the famous LS conjugate gradient method which has good numerical results.However,the LS method has very weak convergence under the Wolfe-type line search.In this paper,we give a new descent gradient method based on the LS method.It can guarantee the sufficient descent property at each iteration and the global convergence under the strong Wolfe line search.Finally,we also present extensive preliminary numerical experiments to show the efficiency of the proposed method by comparing with the famous PRP^+method.

Multi-component joint inversion of gravity gradient based on fast forward calculation

With the development of gravity gradient full tensor measurement technique,three-dimensional( 3D) inversion based on gravity gradient tensor can provide more accurate information. But the forward calculation of 3D full tensor sensitivity matrix is very time-consuming,which restricts its development and application.According to the symmetry of the kernel function,the authors reconstruct the underground source of geological body to avoid repeat computation of the same value,and work out the corresponding relationship between the response of geological body to the observation point and the response of reconstructed geological body to the observation point. According to the relationship,rapid calculation of full tensor gravity sensitivity matrix can be achieved. The model calculation shows that this method can increase the speed of 30-45 times compared with the traditional calculation method. The sensitivity matrix is applied to the multi-component inversion of gravity gradient. The application of this method on the measured data provides the basis for the promotion of the method.

一种近似BFGS的自适应双参数共轭梯度法

为了更加有效的求解大规模无约束优化问题,本文基于自调比无记忆BFGS拟牛顿法,提出一个自适应双参数共轭梯度法,设计的搜索方向满足充分下降性,在一般假设和标准Wolfe线搜索准则下,证明该方法具有全局收敛性,数值实验结果证明提出的新算法是有效的.

一种WYL型谱共轭梯度法的全局收敛性

为解决大规模无约束优化问题,该文结合WYL共轭梯度法和谱共轭梯度法,给出了一种WYL型谱共轭梯度法.在不依赖于任何线搜索的条件下,该方法产生的搜索方向均满足充分下降性,且在强Wolfe线搜索下证明了该方法的全局收敛性.与WYL共轭梯度法的收敛性相比,WYL型谱共轭梯度法推广了线搜索中参数σ的取值范围.最后,相应的数值结果表明了该方法是有效的.

标准Wolfe线搜索下改进的HS共轭梯度法

通过对现有的HS共轭梯度法进行修正,提出一个具有下降性质的改进型HS共轭梯度法,该算法的下降性质得到论证.在标准Wolfe线搜索条件下,证明了改进的HS算法具有全局收敛性.最后,通过数值实验结果的对比,发现新算法数值效果是优异的.

一种全局收敛修正DY共轭梯度法

为解决大规模无约束优化问题,通过修正经典Dai-Yuan共轭梯度法,提出一种新的Dai-Yuan型共轭梯度算法。在标准Wolfe线搜索条件下,新算法在每步迭代都可以产生一个下降方向,证明算法全局收敛性,最后进行数值实验,验证了该算法有效性。

Wolfe线搜下改进的FR型谱共轭梯度法

谱共轭梯度法作为经典共轭梯度法的推广,它是求解大规模无约束优化问题的有效方法之一.基于标准Wolfe线搜索准则和充分下降性条件,提出了一种具有充分下降性质的FR型谱共轭梯度法.在温和的假设条件下,该算法具有全局收敛性.最后,将新算法与现存的修正FR型谱共轭梯度法进行比较,数值结果表明提出的算法是极其有效的.

基于非下降线搜索的改进PRP共轭梯度方法及在图像恢复中的应用

PRP方法是最有效的非线性共轭梯度优化方法之一,然而该方法不能保证产生目标函数的下降方向,这给一般函数的全局收敛带来了困难。为了保证PRP方法的全局收敛性,提出了一种改进的PRP共轭梯度方法。文章以非凸优化问题为目标,简要介绍了非下降线搜索技术以及一些适当的假设条件,探讨了改进PRP方法的全局收敛性。基于MATLAB软件工具,验证了新方法在处理无约束优化和图像恢复问题时的有效性和实用性。

基于导热反问题的二维圆管内壁面第三类边界条件的反演

对于诸如核电领域中有特殊安全要求或结构完备性要求较高的管道系统,不允许在管道开孔来安装温度传感器测量管道内壁面温度和流体温度,需要寻求一种间接无损的方法来预测或评估管道内壁面温度的分布及变化。在有限元法基础上建立温度场数学模型,基于共轭梯度法进行反问题分析,利用圆管外壁面可测的有限温度信息同时反演出第三类边界条件,即内壁面温度、近内壁流体温度以及表面传热系数,并能够获得整个壁面温度的全貌及其分布特征。计算结果及误差分析表明,此方法的精度较高,共轭梯度法能够准确地反演出该圆管内壁的第三类边界条件。该方法不仅可以为结构应力分析和热疲劳分析提供准确的瞬时热载荷,而且为实现一种无损测温方法提供坚实的理论基础。