凸约束伪单调方程组的无导数投影算法

基于HS共轭梯度法的结构,本文在弱假设条件下建立了一种求解凸约束伪单调方程组问题的迭代投影算法.该算法不需要利用方程组的任何梯度或Jacobian矩阵信息,因此它适合求解大规模问题.算法在每一次迭代中都能产生充分下降方向,且不依赖于任何线搜索条件.特别是,我们在不需要假设方程组满足Lipschitz条件下建立了算法的全局收敛性和R-线收敛速度.数值结果表明,该算法对于给定的大规模方程组问题是稳定和有效的.     

解凸约束非线性单调方程组的无导数谱PRP投影算法

本文在著名PRP共轭梯度算法的基础上研究了一种无导数谱PRP投影算法,并证明了算法在求解带有凸约束条件的非线性单调方程组问题的全局收敛性.由于无导数和储存量小的特性,它更适应于求解大规模非光滑的非线性单调方程组问题.数值试验表明,新算法对给定的测试问题是有效的和稳定的.     

求解非线性单调方程组的一种无导数投影算法

推广了一种修正的CG_DESCENT共轭梯度方法,并建立了一种有效求解非线性单调方程组问题的无导数投影算法.在适当的线搜索条件下,证明了算法的全局收敛性.由于新算法不需要借助任何导数信息,故它适应于求解大规模非光滑的非线性单调方程组问题.大量的数值试验表明,新算法对给定的测试问题是有效的.     

凸约束非线性方程组的一种无导数投影方法

推广LCG共轭梯度方法并建立一种求解凸约束非线性单调方程组问题的无导数投影方法.在适当的条件下,证明了方法的全局收敛性.方法不需要任何导数信息,而且继承了共轭梯度方法储存量小的特征,因此它特别适合求解大规模非光滑的非线性单调方程组问题.大量数值结果和比较表明方法是有效的和稳定的.     

凸约束非光滑方程组一个新的谱梯度投影算法

基于寻找分离超平面的三种经典线搜索技术,本文提出了一种自适应线搜索技术.结合谱梯度投影法,提出了凸约束非光滑单调方程组的一个谱梯度投影算法.该算法不需要计算和存储任何矩阵,因而适合求解大规模非光滑的非线性单调方程组.在较弱的条件下,证明了方法的全局收敛性,并分析了算法的收敛率.数值试验结果表明算法是有效的和鲁棒的.     

一类具有非单调线搜索的混合共轭梯度算法

提出一类求解无约束最优化问题的混合共轭梯度算法,新算法有机地结合了DY算法和HS算法的优点,并采用非单调线搜索技术在较弱条件下证明了算法的全局收敛性.数值实验表明新算法具有良好的计算效能.     

解带线性或非线性约束最优化问题的三项记忆梯度Rosen投影算法

利用Rosen投影矩阵，建立求解带线性或非线性不等式约束优化问题的三项记忆梯度Rosen投影下降算法，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Rosen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。     

一种多参数谱三项共轭梯度法

通过求解带有罚参数的优化问题设计共轭梯度法是一种新思路.基于Fatemi的优化问题求解,通过估计步长和选择合适的罚参数建立一个谱三项共轭梯度法,为证得算法的全局收敛性对谱参数进行修正.在标准Wolfe线搜索下证明了该谱三项共轭梯度算法的充分下降性以及全局收敛性.最后,在选取相同算例的多个算法测试结果中表明新方法数值试验性能表现良好.     

求解大规模问题的谱共轭梯度法（英文）

共轭梯度法是求解大规模无约束优化问题的一类重要方法.由于共轭梯度法产生的搜索方向不一定是下降方向,为保证每次迭代方向都是下降方向,本文提出一种求解无约束优化问题的谱共轭梯度算法,该方法的每次搜索方向都是下降方向.当假设目标函数一致凸,且其梯度满足Lipschitz条件,线性搜索满足Wolfe条件时,讨论所设计算法的全局收敛性.     

一种求解鞍点问题的预处理并行算法

研究了一种求解鞍点问题的并行预处理变形共轭梯度算法.通过应用迭代法进行预处理后,再采用变形共轭梯度求解的模式.首先构造系数矩阵近似逆的多项式表达式,以此作为预处理矩阵的逆矩阵,对方程组进行预处理;然后采用变形共轭梯度法并行求解预处理后的线性方程组.为减少运算量,采用迭代方式并行计算多项式与向量的乘法运算.通过调整迭代次数,即调整多项式次数,检验各种次数的多项式进行预处理后的求解方程的效果.数值试验结果表明,该算法明显优于未预处理的变形共轭梯度法,且当预处理迭代次数取4时效果最好.     

Spectral DY-Type Projection Method for Nonlinear Monotone Systems of Equations

In this paper, we propose a spectral DY-type projection method for nonlinear monotone systems of equations, which is a reasonable combination of DY conjugate gradient method, the spectral gradient method and the projection technique. Without the differentiability assumption on the system of equations, we establish the global convergence of the proposed method, which does not rely on any merit function. Furthermore, this method is derivative-free and so is very suitable to solve large-scale nonlinear monotone systems. The preliminary numerical results show the feasibility and effectiveness of the proposed method.     

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].     

Spectral gradient projection method for solving nonlinear monotone equations

An algorithm for solving nonlinear monotone equations is proposed, which combines a modified spectral gradient method and projection method. This method is shown to be globally convergent to a solution of the system if the nonlinear equations to be solved is monotone and Lipschitz continuous. An attractive property of the proposed method is that it can be applied to solving nonsmooth equations. We also give some preliminary numerical results to show the efficiency of the proposed method.     

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.     

An Liu-Storey-Type Method for Solving Large-Scale Nonlinear Monotone Equations

An algorithm for solving nonlinear monotone equations is proposed, which combines a modified Liu-Storey conjugate gradient method with hyperplane projection method. Under mild conditions, the global convergence of the proposed method is established with a suitable line search method. The method can be applied to solve large-scale problems for its lower storage requirement. Numerical results indicate that our method is efficient.     

A class of conjugate gradient methods for convex constrained monotone equations

The recent designed non-linear conjugate gradient method of Dai and Kou [SIAM J Optim. 2013;23:296–320] is very efficient currently in solving large-scale unconstrained minimization problems due to its simpler iterative form, lower storage requirement and its closeness to the scaled memoryless BFGS method. Just because of these attractive properties, this method was extended successfully to solve higher dimensional symmetric non-linear equations in recent years. Nevertheless, its numerical performance in solving convex constrained monotone equations has never been explored. In this paper, combining with the projection method of Solodov and Svaiter, we develop a family of non-linear conjugate gradient methods for convex constrained monotone equations. The proposed methods do not require the Jacobian information of equations, and even they do not store any matrix in each iteration. They are potential to solve non-smooth problems with higher dimensions. We prove the global convergence of the class of the proposed methods and establish its R-linear convergence rate under some reasonable conditions. Finally, we also do some numerical experiments to show that the proposed methods are efficient and promising.