求解非线性等式和不等式约束优化问题的三项记忆梯度广义投影算法

利用广义投影矩阵，对求解无约束规划的三项记忆梯度算法中的参数给一条件，确定它们的取值范围，以保证得到目标函数的三项记忆梯度广义投影下降方向，建立了求解非线性等式和不等式约束优化问题的三项记忆梯度广义投影算法，并证明了算法的收敛性．同时给出了结合FR，PR，HS共轭梯度参数的三项记忆梯度广义投影算法，从而将经典的共轭梯度算法推广用于求解约束规划问题．数值例子表明算法是有效的．     

初始点任意的解非线性不等式约束优化问题的结合共轭梯度参数的超记忆梯度广义投影算法

本文利用广义投影矩阵，对求解无约束规划的超记忆梯度算法中的参数给出一种新的取值范围以保证得到目标函数的超记忆梯度广义投影下降方向，并与处理任意初始点的方法技巧结合建立求解非线性不等式约束优化问题的一个初始点任意的超记忆梯度广义投影算法，在较弱条件下证明了算法的收敛性．同时给出结合FR，PR，HS共轭梯度参数的超记忆梯度广义投影算法，从而将经典的共轭梯度法推广用于求解约束规划问题．数值例子表明算法是有效的．     

求解约束优化问题的记忆梯度Goldstein-Lavintin-Polyak投影算法

给求解无约束规划问题的记忆梯度算法中的参数一个特殊取法,得到目标函数的记忆梯度G o ldste in-L av in tin-Po lyak投影下降方向,从而对凸约束的非线性规划问题构造了一个记忆梯度G o ldste in-L av in tin-Po lyak投影算法,并在一维精确步长搜索和去掉迭代点列有界的条件下,分析了算法的全局收敛性,得到了一些较为深刻的收敛性结果.同时给出了结合FR,PR,HS共轭梯度算法的记忆梯度G o ldste in-L av in tin-Po lyak投影算法,从而将经典共轭梯度算法推广用于求解凸约束的非线性规划问题.数值例子表明新算法比梯度投影算法有效.     

一般线性或非线性约束下的共轭投影变尺度方法

梯度投影法是一类有效的约束最优化算法，在最优化领域中占有重要的地位．但是，梯度投影法所采用的投影是正交投影，不包含目标函数和约束函数的二阶导数信息·因而；收敛速度不太令人满意．本文介绍一种共轭投影概念，利用共轭投影构造了一般线性或非线性约束下的共轭投影变尺度算法，并证明了算法在一定条件下具有全局收敛性．由于算法中的共轭投影恰当地包含了目标函数和约束函数的二阶导数信息，因而收敛速度有希望加快．数值试验的结果表明算法是有效的．     

并行技术在约束凸规划化问题的对偶算法中的应用

用 Rosen(196 1)的投影梯度的方法求解约束凸规划化问题的对偶问题 ,在计算投影梯度方向时 ,涉及求关于原始变量的最小化问题的最优解 .我们用并行梯度分布算法 (PGD)计算出这一极小化问题的近似解 ,证明近似解可以达到任何给定的精度 ,并说明当精度选取合适时 ,Rosen方法仍然是收敛的     

互补约束均衡优化的一个共轭梯度投影法

讨论均衡约束最优化问题,利用一个互补函数和扰动技术将原问题转换为非线性等式约束最优化问题,然后利用共轭梯度投影算法的思想,给出了问题的一个求解算法,在适当的条件下,证明了算法的全局收敛性.     

一个求解非线性不等式约束优化问题的带有共轭梯度参数的广义梯度投影算法

本文提出一个求解非线性不等式约束优化问题的带有共轭梯度参数的广义梯度投影算法.算法中的共轭梯度参数是很容易得到的,且算法的初始点可以任意选取.而且,由于算法仅使用前一步搜索方向的信息,因而减少了计算量.在较弱条件下得到了算法的全局收敛性.数值结果表明算法是有效的.     

约束优化问题的修正共轭梯度投影算法

对闭凸集约束的非线性规划问题构造了一个修正共轭梯度投影下降算法,在去掉迭代点列有界的条件下,分析了算法的全局收敛性.新算法与共轭梯度参数结合,给出了三类结合共轭梯度参数的修正共轭梯度投影算法.数值例子表明算法是有效的.     

一个解带线性或非线性约束最优化问题的梯度投影方法

§1 引言 Rosen在[1,2]中利用梯度投影建立了带约束非线性规划问题的可行方向算法,称为梯度投影方法.由于此方法简单易行,计算的每一步都是显式迭代,而不必去解复杂的线性规划或二次规划问题,因此人们颇为注意.现在梯度投影方法已成为非线性规划算法     

Wolfe步长规则下约束优化问题的共轭梯度投影算法

本文研究了约束优化问题min x∈Ωf(x).利用共轭梯度算法与GLP梯度投影思想相结合的方法,构造了一个新的共轭梯度投影算法,并在Wolfe线搜索下获得了该算法的全局收敛性结果.     

A derivative-free three-term projection algorithm involving spectral quotient for solving nonlinear monotone equations

ABSTRACT

In this paper, we develop a three-term conjugate gradient method involving spectral quotient, which always satisfies the famous Dai-Liao conjugacy condition and quasi-Newton secant equation, independently of any line search. This new three-term conjugate gradient method can be regarded as a variant of the memoryless Broyden-Fletcher-Goldfarb-Shanno quasi-Newton method with regard to spectral quotient. By combining this method with the projection technique proposed by Solodov and Svaiter in 1998, we establish a derivative-free three-term projection algorithm for dealing with large-scale nonlinear monotone system of equations. We prove the global convergence of the algorithm and obtain the R-linear convergence rate under some mild conditions. Numerical results show that our projection algorithm is effective and robust, and is more competitive with the TTDFP algorithm proposed Liu and Li [A three-term derivative-free projection method for nonlinear monotone system of equations. Calcolo. 2016;53:427–450].     

Three adaptive hybrid derivative-free projection methods for constrained monotone nonlinear equations and their applications

In this work, by considering the hyperplane projection and hybrid techniques, three scaled three-term conjugate gradient methods are extended to solve the system of constrained monotone nonlinear equations, and the developed methods have the advantages of low storage and only using function values. The new methods satisfy the sufficient descent condition independent of any line search criterion. It has been proved that three new methods converge globally under some mild conditions. The numerical experiments for constrained monotone nonlinear equations and image de-blurring problems illustrate that the proposed methods are numerically effective and efficient.     

三项记忆梯度法及其投影算法的收敛性分析

对于无约束优化问题,提出了一类新的三项记忆梯度算法．这类算法是在参数满足某些假设的条件下,确定它的取值范围,从而保证三项记忆梯度方向是使目标函数充分下降的方向．在非单调步长搜索下讨论了算法的全局收敛性．为了得到具有更好收敛性质的算法,结合Solodov and Svaiter(2000)中的部分技巧,提出了一种新的记忆梯度投影算法,并证明了该算法在函数伪凸的情况下具有整体收敛性．     

Globally Convergent Three-Term Conjugate Gradient Methods that Use Secant Conditions and Generate Descent Search Directions for Unconstrained Optimization

In this paper, we propose a three-term conjugate gradient method based on secant conditions for unconstrained optimization problems. Specifically, we apply the idea of Dai and Liao (in Appl. Math. Optim. 43: 87–101, 2001) to the three-term conjugate gradient method proposed by Narushima et al. (in SIAM J. Optim. 21: 212–230, 2011). Moreover, we derive a special-purpose three-term conjugate gradient method for a problem, whose objective function has a special structure, and apply it to nonlinear least squares problems. We prove the global convergence properties of the proposed methods. Finally, some numerical results are given to show the performance of our methods.     

三项共轭梯度法收敛性分析

1．引言考虑求解无约束光滑优化问题的线搜索方法其中al事先给定,山为搜索方向,Ik是步长因子．在经典的共轭梯度法中,对k三2,搜索方向dk由负梯度方向一gb和已有搜索方向小．1两个方向组成：其中山—－91,作为参数．关于参数作的计算公式很多,其中两个有名的计算公式称为＊R公式和＊＊P公式（见门和河1叩,它们分别为此处及以下11·11均指欧氏范数．在文献山中,Beale提出了搜索方向形如的三项重开始共轭梯度法,其中dt为重开始方向．Powellll］对这一方法引入了适当的重开始准则,获得了很好的数值结果．本文里,我们将研究搜索方向…     

Three-term conjugate approach for structural reliability analysis

In this paper, a nonlinear conjugate structural first-order reliability method is proposed using three-term conjugate discrete map-based sensitivity analysis to enhance convergence properties as stable results and efficient computational burden of nonlinear reliability problems. The concept of finite-step length strategy is incorporated into this method to enhance the stability of the iterative formula for highly nonlinear limit state function, while three-term conjugate search direction combining with a finite-step size is utilized to enhance the efficiency of the sensitivity vector in the proposed iterative reliability formula. The proposed three-term discrete conjugate search direction is developed based on the sufficient descent condition to provide the stable results, theoretically. The efficiency and robustness of the proposed three-term conjugate formula are investigated through several nonlinear/ complex structural examples and are compared with several modified existing iterative formulas. Comparative results illustrate that the three-term conjugate-based finite step length formula provides superior efficiency and robustness than other studied methods.