A globally convergent BFGS method for nonlinear monotone equations without any merit functions

Since 1965, there has been significant progress in the theoretical study on quasi-Newton methods for solving nonlinear equations, especially in the local convergence analysis. However, the study on global convergence of quasi-Newton methods is relatively fewer, especially for the BFGS method. To ensure global convergence, some merit function such as the squared norm merit function is typically used. In this paper, we propose an algorithm for solving nonlinear monotone equations, which combines the BFGS method and the hyperplane projection method. We also prove that the proposed BFGS method converges globally if the equation is monotone and Lipschitz continuous without differentiability requirement on the equation, which makes it possible to solve some nonsmooth equations. An attractive property of the proposed method is that its global convergence is independent of any merit function.We also report some numerical results to show efficiency of the proposed method.

     

A nonmonotone filter method for nonlinear optimization

We propose a new nonmonotone filter method to promote global and fast local convergence for sequential quadratic programming algorithms. Our method uses two filters: a standard, global g-filter for global convergence, and a local nonmonotone l-filter that allows us to establish fast local convergence. We show how to switch between the two filters efficiently, and we prove global and superlinear local convergence. A special feature of the proposed method is that it does not require second-order correction steps. We present preliminary numerical results comparing our implementation with a classical filter SQP method.     

Convergence analysis of a modified BFGS method on convex minimizations

A modified BFGS method is proposed for unconstrained optimization. The global convergence and the superlinear convergence of the convex functions are established under suitable assumptions. Numerical results show that this method is interesting.     

A new,globally convergent Riemannian conjugate gradient method

This article deals with the conjugate gradient method on a Riemannian manifold with interest in global convergence analysis. The existing conjugate gradient algorithms on a manifold endowed with a vector transport need the assumption that the vector transport does not increase the norm of tangent vectors, in order to confirm that generated sequences have a global convergence property. In this article, the notion of a scaled vector transport is introduced to improve the algorithm so that the generated sequences may have a global convergence property under a relaxed assumption. In the proposed algorithm, the transported vector is rescaled in case its norm has increased during the transport. The global convergence is theoretically proved and numerically observed with examples. In fact, numerical experiments show that there exist minimization problems for which the existing algorithm generates divergent sequences, but the proposed algorithm generates convergent sequences.     

Global convergence of conjugate gradient method

In this article, we consider the global convergence of the Polak–Ribiére–Polyak conjugate gradient method (abbreviated PRP method) for minimizing functions that have Lipschitz continuous partial derivatives. A novel form of non-monotone line search is proposed to guarantee the global convergence of the PRP method. It is also shown that the PRP method has linear convergence rate under some mild conditions when the non-monotone line search reduces to a related monotone line search. The new non-monotone line search needs to estimate the Lipschitz constant of the gradients of objective functions, for which two practical estimations are proposed to help us to find a suitable initial step size for the PRP method. Numerical results show that the new line search approach is efficient in practical computation.     

Convergence of supermemory gradient method

In this paper we consider the global convergence of a new supermemory gradient method for unconstrained optimization problems. New trust region radius is proposed to make the new method converge stably and averagely, and it will be suitable to solve large scale minimization problems. Some global convergence results are obtained under some mild conditions. Numerical results show that this new method is effective and stable in practical computation.     

The Incremental Gauss-Newton Algorithm with Adaptive Stepsize Rule

In this paper, we consider the Extended Kalman Filter (EKF) for solving nonlinear least squares problems. EKF is an incremental iterative method based on Gauss-Newton method that has nice convergence properties. Although EKF has the global convergence property under some conditions, the convergence rate is only sublinear under the same conditions. One of the reasons why EKF shows slow convergence is the lack of explicit stepsize. In the paper, we propose a stepsize rule for EKF and establish global convergence of the algorithm under the boundedness of the generated sequence and appropriate assumptions on the objective function. A notable feature of the stepsize rule is that the stepsize is kept greater than or equal to 1 at each iteration, and increases at a linear rate of k under an additional condition. Therefore, we can expect that the proposed method converges faster than the original EKF. We report some numerical results, which demonstrate that the proposed method is promising.     

一个带有线搜索的新信赖域方法及其收敛性

本文利用一个修正的BFGS公式,提出了一个结合Armijo线搜索条件技术的BFGS信赖域方法,并在一定条件下证明了该方法的全局收敛性和超线性收敛性.初步的数值实验结果表明该方法是有效的.     

A Modified PRP Conjugate Gradient Algorithm with Trust Region for Optimization Problems

In this article, a modified PRP algorithm is presented for unconstrained optimization problems. This method possesses sufficiently descent property and the proposed direction is in a trust region. The global convergence and the linear convergence rate of the given method are established under weaker conditions. Numerical results show that the presented method is effective.     

隐式重启精化全局Lanczos方法

The numerical methods for solving large symmetric eigenvalue problems are considered in this paper.Based on the global Lanczos process,a global Lanczos method for solving large symmetric eigenvalue problems is presented.In order to accelerate the convergence of the F-Ritz vectors,the refined global Lanczos method is developed.Combining the implicitly restarted strategy with the deflation technique,an implicitly restarted and refined global Lanczos method for computing some eigenvalues of large symmetric matrices is proposed.Numerical results show that the proposed methods are efficient.     

补偿随机规划的一种新数值方法

本文给出解决两阶段求援随机规划的一种新的数值方法.由于引进了新的逼近技术,该方法具有全局收敛性和局部超线性收敛性.     

Wolfe线搜索下一个全局收敛的混合共轭梯度法

对无约束优化问题, 本文给出了一个新的混合共轭梯度法公式. 在标准Wolfe非精确线搜索下,证明了由新公式所产生的算法具有下降性和全局收敛性, 并对算法进行了数值试验, 其结果表明该算法是有效的.     

无记忆非拟 Newton 算法的导出和全局收敛性

In this paper, a new class of memoryless non-quasi-Newton method for solving unconstrained optimization problems is proposed, and the global convergence of this method with inexact line search is proved. Furthermore, we propose a hybrid method that mixes both the memoryless non-quasi-Newton method and the memoryless Perry-Shanno quasi-Newton method. The global convergence of this hybrid memoryless method is proved under mild assumptions. The initial results show that these new methods are efficient for the given test problems. Especially the memoryless non-quasi-Newton method requires little storage and computation, so it is able to efficiently solve large scale optimization problems.     

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.     

求连续minimax问题整体解的区间算法

1 引 言 Minimax问题是一类重要的数学规划问题,它来源于实际并有极广泛的应用([1],[2]).用区间数学方法求解 minimax问题已取得了一些成果.文[1]对由 C2类函数构成的无约束连续 minimax问题进行了研究,建立了相应的区间算法,文[6]～[11]分别讨论和建立了无约束和不等式约束的离散minimax问题的区间算法.文[12]、[13]则讨论了最坏     

Global convergence analysis of a class of epidemic models

This paper addresses the global convergence of the epidemic models whose infected subsystems are monotone in the sense of Hirsch (1984). By invoking results from monotone system theory and nonlinear control theory, a simple method is proposed for determining the global asymptotic stability of a disease free equilibrium (DFE) and the global convergence to an endemic equilibrium (EE). Typical epidemic models are studied to illustrate the applicability of the proposed methodology.     

A new adaptive trust region algorithm for optimization problems

It is well known that trust region methods are very effective for optimization problems. In this article, a new adaptive trust region method is presented for solving unconstrained optimization problems. The proposed method combines a modified secant equation with the BFGS updated formula and an adaptive trust region radius, where the new trust region radius makes use of not only the function information but also the gradient information. Under suitable conditions, global convergence is proved, and we demonstrate the local superlinear convergence of the proposed method. The numerical results indicate that the proposed method is very efficient.     

求解非线性互补问题的逐次逼近拟牛顿法的收敛性分析

提出了求解非线性互补问题的一个逐次逼近拟牛顿算法。在适当的假设下,证明了该算法的全局收敛性和局部超线性收敛性。     

修正PRP共轭梯度方法求解无约束最优化问题

基于著名的PRP共轭梯度方法,利用CG_DESCENT共轭梯度方法的结构,本文提出了一种求解大规模无约束最优化问题的修正PRP共轭梯度方法。该方法在每一步迭代中均能够产生一个充分下降的搜索方向,且独立于任何线搜索条件。在标准Wolfe线搜索条件下,证明了修正PRP共轭梯度方法的全局收敛性和线性收敛速度。数值结果展示了修正PRP方法对给定的测试问题是非常有效的。     

Minimax问题的一个滤子算法

本文提出一个求解不等式约束的Minimax问题的滤子算法,结合序列二次规划方法,并利用滤子以避免罚函数的使用.在适当的条件下,证明了此方法的全局收敛性及超线性收敛性.数值实验表明算法是有效的.