等式约束优化非单调信赖域算法

设计了一个新的求解等式约束优化问题的非单调信赖域算法.该算法不需要罚函数也无需滤子.在每次迭代过程中只需求解满足下降条件的拟法向步及切向步.新算法产生的迭代步比滤子方法更易接受,计算量比单调算法小.在一般条件下,算法具有全局收敛性.     

非线性等式约束优化问题的仿射信赖域方法

提出非线性等式和有界约束优化问题的结合非单调技术的仿射信赖域方法. 结合信赖域方法和内点回代线搜索技术, 每一步迭代转到由一般信赖域子问题产生的回代步中且满足严格内点可行条件. 在合理的假设条件下, 证明了算法的整体收敛性和局部超线性收敛速率. 最后, 数值结果表明了所提供的算法具有有效性.     

求解一类约束优化信赖域方法的子问题

１引言设Ｈ为一给定的ｎ×ｎ对称矩阵,ｃＲ＂,本文考虑如｝的约束优化问题这里ａ＞０为给定的参数,Ｃ＝｛ｘＲｎｘ＜ａ是Ｒ”中的一个球体,Ｋ是一个简单凸闭集．当Ｋ＝Ｒｎ时,问题（Ｐ）便是无约束优化的信赖域子问题．当Ｋ＝｛ｘＲｎμ≤ｘ≤υ５,（μ１,μ２,…,μｎ）Ｔ,υ＝（υ１,υ２…,υｎ）Ｔ,且—∞＜μｉ＜υｉ＜ｖ＜＋∞,ｉ＝１,２,…,ｎ时,问题（Ｐ）便是用信赖域方法求解带上下界约束的优化问题时遇到的子问题．对于无约束信赖域方法的子问题已经有了比较成熟的算法［８,１２－１３,１５－１６］．Ｋ＝Ｒ…     

等式与界约束非线性优化的信赖域增广Lagrangian算法

１．引 言本文讨论如下非线性约束优化问题：其中； 是Ｒｎ→Ｒ的可微函数，　　　　　　．记 问题（１．１）是非线性约束优化问题中的一类重要类型，事实上任一个非线性等式与不等式约束优化均可引入松驰变量转化为（１．１）的形式．因此（１．１）的求解是人们讨论的热点问     

一类约束优化问题的非单调信赖域算法

本文就一类等式约束优化问题,结合当前比较流行的非单调技术,提出了一类新的求解等式约束优化的非单调信赖域算法.其非单调程度由算法自适应控制,计算预测下降量和实际下降量的比值时,采用前m(k)个点的信息,这不同于以前在计算预测下降量和实际下降量的比值时,仅仅采用当前-个点的信息.在没有正则性条件的假设下我们证明了算法是有定义的.并且通过对不同情况的讨论证明了算法的全局收敛性.基本的数值试验表明算法是有效的,且说明提出的非单调信赖域算法比单调信赖域算法有效.     

求解线性不等式约束优化问题的一类信赖域算法

本文对线性不等式约束的非线性规划问题提出了一类信赖域算法,证明了算法所产生的序列的任一聚点为Ｋｕｈｎ－Ｔｕｃｋｅｒ点,并讨论了子问题求解的有效集方法．     

带非线性不等式约束优化问题的信赖域算法

借助于KKT条件和NCP函数,提出了求解带非线性不等式约束优化问题的信赖域算法.该算法在每一步迭代时,不必求解带信赖域界的二次规划子问题,仅需求一线性方程组系统.在适当的假设条件下,它还是整体收敛的和局部超线性收敛的.数值实验结果表明该方法是有效的.     

基于增广Lagrange函数的约束优化问题的一个信赖域方法

本文提出一个求解不等式约束优化问题的基于指数型增广Lagrange函数的信赖域方法.基于指数型增广Lagrange函数,将传统的增广Lagrange方法的精确求解子问题转化为一个信赖域子问题,从而减少了计算量,并建立相应的信赖域算法.在一定的假设条件下,证明了算法的全局收敛性,并给出相应经典算例的数值实验结果.     

等式约束优化的信赖域法

本文研究了约束优化信赖域法中的线性化约束条件在信赖域内无解的问题.利用一种基于增广Lagrange函数的方法.获得了一个改进的约束优化的信赖域法.该法的线性化约束条件在信赖内有解,并且具有全局收敛性和超线性收敛性.     

解非线性约束优化问题的依赖域算法

本文对一般非线性约束优化问题提出了一个信赖域算法,导出了等价的KKT条件.在试探步满足适当条件下,证明了算法的全局收敛性,并进行了数值试验.     

STRSCNE: A Scaled Trust-Region Solver for Constrained Nonlinear Equations

In this paper a Matlab solver for constrained nonlinear equations is presented. The code, called STRSCNE, is based on the affine scaling trust-region method STRN, recently proposed by the authors. The approach taken in implementing the key steps of the method is discussed. The structure and the usage of STRSCNE are described and its features and capabilities are illustrated by numerical experiments. The results of a comparison with high quality codes for nonlinear optimization are shown.     

A New Trust-Region Algorithm for Equality Constrained Optimization

We present a new trust-region algorithm for solving nonlinear equality constrained optimization problems. Quadratic penalty functions are employed to obtain global convergence. At each iteration a local change of variables is performed to improve the ability of the algorithm to follow the constraint level set. Under certain assumptions we prove that this algorithm globally converges to a point satisfying the second-order necessary optimality conditions. Results of preliminary numerical experiments are reported.     

A Conic Trust-Region Method for Nonlinearly Constrained Optimization

Trust-region methods are powerful optimization methods. The conic model method is a new type of method with more information available at each iteration than standard quadratic-based methods. Can we combine their advantages to form a more powerful method for constrained optimization? In this paper we give a positive answer and present a conic trust-region algorithm for non-linearly constrained optimization problems. The trust-region subproblem of our method is to minimize a conic function subject to the linearized constraints and the trust region bound. The use of conic functions allows the model to interpolate function values and gradient values of the Lagrange function at both the current point and previous iterate point. Since conic functions are the extension of quadratic functions, they approximate general nonlinear functions better than quadratic functions. At the same time, the new algorithm possesses robust global properties. In this paper we establish the global convergence of the new algorithm under standard conditions.     

On the Convergence of a Trust-Region Method for Solving Constrained Nonlinear Equations with Degenerate Solutions

This paper presents a trust-region method for solving the constrained nonlinear equation F(x) = 0, x , where R ⁿ is a nonempty and closed convex set, F is defined on the open set containing and is continuously differentiable. The iterates generated by the method are feasible. The method is globally and quadratically convergent under local error bounded assumption on F. The results obtained are extensions of the work of Yamashita Fukushima (Ref. 1) and Fan Yuan (Ref. 2) for unconstrained nonlinear equations. Numerical results show that the new algorithm works quite well.     

一种非单调滤子信赖域算法解线性不等式约束优化

本文给出了一种新的多维滤子算法结合非单调信赖域策略解线性约束优化.目标函数及其投影梯度的分量组成了新的多维滤子,并且与信赖域半径有关.当信赖域半径充分小时,新的滤子能接受试探点,避免算法无限循环.非单调信赖域策略保证了新算法的整体收敛性.目前为止,多维滤子算法局部收敛性分析仍然没有解决,在合理假设下,我们分析了新算法的局部超线性收敛性.数值结果验证了算法的有效性.     

On the Global Convergence of a Projective Trust Region Algorithm for Nonlinear Equality Constrained Optimization

A trust-region sequential quadratic programming (SQP) method is developed and analyzed for the solution of smooth equality constrained optimization problems. The trust-region SQP algorithm is based on filter line search technique and a composite-step approach, which decomposes the overall step as sum of a vertical step and a horizontal step. The algorithm includes critical modifications of horizontal step computation. One orthogonal projective matrix of the Jacobian of constraint functions is employed in trust-region subproblems. The orthogonal projection gives the null space of the transposition of the Jacobian of the constraint function. Theoretical analysis shows that the new algorithm retains the global convergence to the first-order critical points under rather general conditions. The preliminary numerical results are reported.     

解非线性约束方程的拉格朗日全局投影方法

基于最优化方法求解约束非线性方程组的一个突出困难是计算 得到的仅是该优化问题的稳定点或局部极小点,而非方程组的解点.由此引出的问题是如何从一个稳定点出发得到一个相对于方程组解更好的点. 该文采用投影型算法,推广了Nazareth-Qi$^{[8,9]}$ 求解无约束非线性方程组的拉格朗日全局算法(Lagrangian Global-LG)于约束方程上; 理论上证明了从优化问题的稳定点出发,投影LG方法可寻找到一个更好的点. 数值试验证明了LG方法的有效性.     

化不等式约束问题为无约束的广义可微限域精确罚函数方法

本文给出了广义可微精确罚函数的概念及一类所谓广义限域可微精确罚函数.本文预先选定罚因子,将不等式约束问题化为单一的无约束问题,并给出了具全局收敛性的算法.本文的罚函数构造简单,假设条件少而且算法的构造与收敛性结果是独特的.     

A SQP Method for Inequality Constrained Optimization

Abstract In this paper, a new SQP method for inequality constrained optimization is proposed and the globalconvergence is obtained under very mild conditions.