非线性规划的序列仿射尺度投影内点算法

本文提出了求解非线性规划的一种序列二次规划内点算法,与其他算法的不同之处在于引进了仿射尺度变换,且避免了一维搜索,这使得该算法的计算量获得了明显的减少,本文给出了算法的详细迭代步骤并讨论了算法的收敛性。     

一个求解线性规划的单纯形-内点算法

根据单纯形方法和大步长路径跟踪算法（Ｈｅｒｔｏｇ，Ｒｏｏｓ和Ｔｅｒｌａｋｙ１９９１），对于具有不等式约束的线性规划问题，引进了一个具有组合特性的内点算法．该方法保留了单纯形方法和内点算法的优点，克服了它们的不足，在任何情况下，这个方法都能快速收敛．数值结果也很好地验证了这个结论．     

一个修正的罚函数方法

本文通过给出的一个修正的罚函数,把约束非线性规划问题转化为无约束非线性规划问题.我们讨论了原问题与相应的罚问题局部最优解和全局最优解之间的关系,并给出了乘子参数和罚参数与迭代点之间的关系,最后给出了一个简单算法,数值试验表明算法是有效的.     

基于双参数罚函数求解约束优化问题的一个新算法

对于含约束不等式的最优化问题给出了一种双参数罚函数形式,在文[7]的拟牛顿算法的基础上提出了一个同时改变双参数罚函数的新算法,研究了它的收敛性,数值实验表明了该算法是有效的.     

求解非线性规划问题的一类对偶算法

本文提出了一类求解不等式约束非线性规划问题的构造性对偶算法,我们证明在适当的条件下,势函数的罚参数存在一个阀值,当罚参数小于这个阀值时,由这一方法所产生的序列局部收敛于问题的一个Kuhn-Tucker解,我们也建立了解的依赖于罚参数的误差上界,最后,我们给出了一个特残势函数的数值结果。     

基于指数型核函数的线性规划原始对偶内点算法

给出线性规划原始对偶内点算法的一个单变量指数型核函数.首先研究了这个指数型核函数的性质以及其对应的障碍函数.其次,基于这个指数型核函数,设计了求解线性规划问题的原始对偶内点算法,得到了目前小步算法最好的理论迭代界.最后,通过数值算例比较了基于指数型核函数的原始对偶内点算法和基于对数型核函数的原始对偶内点算法的计算效果.     

非线性-线性二层规划问题的罚函数方法

利用下层问题的K-T最优性条件将下层为线性规划的一类非线性二层规划转化成相应的单层规划,同时取下层问题的互补条件为罚项,构造了该类非线性二层规划的罚问题.通过对相应罚问题性质的分析,得到了该类非线性二层规划问题的最优性条件,同时设计了该类二层规划问题的求解方法.数值结果表明该方法是可行、有效的.     

一个解不等式约束非线性规划问题的有效方法

本文提出了一个解不等式约束非线性规划问题有效方法．在这个方法中,考虑解一个等价Ｋｕｈｎ－Ｔｕｃｋｅｒ条件的非线性方程组．这个方程组中ＮＣＰ函数的使用消去了对应于不等式约束的Ｌａｇｒａｎｇｅ乘子的非负性．截断牛顿方法被用来解这个非线性方程组．为了保证全局收敛性,一个强健的损失函数被选为寻查函数,同时方法中插入修正最速下降方向．本文证明了方法的分Ｑ－二阶收敛性,同时指出新方法可以有效地解稀疏大规模非线性规划问题。     

框式约束凸二次规划问题的内点算法

In this paper,a primal-dual interior point algorithm for convex quadratic progromming problem with box constrains is presented.It can be started at any primal-dual interior feasible point.If the initial point is close to the central path,it becomes a central path-following alogorithm and requires a total of O(√nL)number of iterations,where L is the input length.     

求解非线性规划的原始对偶内点法(英文)

In this paper, we propose a primal-dual interior point method for solving general constrained nonlinear programming problems. To avoid the situation that the algorithm we use may converge to a saddle point or a local maximum, we utilize a merit function to guide the iterates toward a local minimum. Especially, we add the parameter ε to the Newton system when calculating the decrease directions. The global convergence is achieved by the decrease of a merit function. Furthermore, the numerical results confirm that the algorithm can solve this kind of problems in an efficient way.     

A Combined Homotopy Infeasible Interior-Point Method for Convex Nonlinear Programming

In this paper, on the basis of the logarithmic barrier function and KKT conditions , we propose a combined homotopy infeasible interior-point method (CHIIP) for convex nonlinear programming problems. For any convex nonlinear programming, without strict convexity for the logarithmic barrier function, we get different solutions of the convex programming in different cases by CHIIP method.     

A Boolean Penalty Method for Zero-One Nonlinear Programming

We introduce a discrete penalty called Boolean Penalty to 0–1 constrained nonlinear programming (PNLC-01). The main importance of this Penalty function are its properties which allow us to develop algorithms for the PNLC-01 problem. Optimality conditions, and numerical results are presented.     

On the Newton Interior-Point Method for Nonlinear Programming Problems

Interior-point methods have been developed largely for nonlinear programming problems. In this paper, we generalize the global Newton interior-point method introduced in Ref. 1 and we establish a global convergence theory for it, under the same assumptions as those stated in Ref. 1. The generalized algorithm gives the possibility of choosing different descent directions for a merit function so that difficulties due to small steplength for the perturbed Newton direction can be avoided. The particular choice of the perturbation enables us to interpret the generalized method as an inexact Newton method. Also, we suggest a more general criterion for backtracking, which is useful when the perturbed Newton system is not solved exactly. We include numerical experimentation on discrete optimal control problems.     

A Simple Primal-Dual Feasible Interior-Point Method for Nonlinear Programming with Monotone Descent

We propose and analyze a primal-dual interior point method of the feasible type, with the additional property that the objective function decreases at each iteration. A distinctive feature of the method is the use of different barrier parameter values for each constraint, with the purpose of better steering the constructed sequence away from non-KKT stationary points. Assets of the proposed scheme include relative simplicity of the algorithm and of the convergence analysis, strong global and local convergence properties, and good performance in preliminary tests. In addition, the initial point is allowed to lie on the boundary of the feasible set.     

Global Convergence of the Newton Interior-Point Method for Nonlinear Programming

The aim of this paper is to show that the theorem on the global convergence of the Newton interior–point (IP) method presented in Ref. 1 can be proved under weaker assumptions. Indeed, we assume the boundedness of the sequences of multipliers related to nontrivial constraints, instead of the hypothesis that the gradients of the inequality constraints corresponding to slack variables not bounded away from zero are linearly independent. By numerical examples, we show that, in the implementation of the Newton IP method, loss of boundedness in the iteration sequence of the multipliers detects when the algorithm does not converge from the chosen starting point.     

Globally Convergent Interior-Point Algorithm for Nonlinear Programming

This paper presents a primal-dual interior-point algorithm for solving general constrained nonlinear programming problems. The inequality constraints are incorporated into the objective function by means of a logarithmic barrier function. Also, satisfaction of the equality constraints is enforced through the use of an adaptive quadratic penalty function. The penalty parameter is determined using a strategy that ensures a descent property for a merit function. Global convergence of the algorithm is achieved through the monotonic decrease of a merit function. Finally, extensive computational results show that the algorithm can solve large and difficult problems in an efficient and robust way.Communicated by L. C. W. DixonThe research reported in this paper was done while the first author was at Imperial College. The authors gratefully acknowledge constructive comments from Professor L. C. W. Dixon and an anonymous referee. They are also grateful to Dr. Stanislav Zakovic for helpful suggestions and comments. Financial support was provided by EPSRC Grants M16016 and GR/G51377/01.     

Local Convergence of the Interior-Point Newton Method for General Nonlinear Programming

In this paper, a formulation for an interior-point Newton method of general nonlinear programming problems is presented. The formulation uses the Coleman-Li scaling matrix. The local convergence and the q-quadratic rate of convergence for the method are established under the standard assumptions of the Newton method for general nonlinear programming.     

An Interior-Point Algorithm for Nonconvex Nonlinear Programming

The paper describes an interior-point algorithm for nonconvex nonlinear programming which is a direct extension of interior-point methods for linear and quadratic programming. Major modifications include a merit function and an altered search direction to ensure that a descent direction for the merit function is obtained. Preliminary numerical testing indicates that the method is robust. Further, numerical comparisons with MINOS and LANCELOT show that the method is efficient, and has the promise of greatly reducing solution times on at least some classes of models.     

Numerical Comparisons of Path-Following Strategies for a Primal-Dual Interior-Point Method for Nonlinear Programming

An important research activity in primal-dual interior-point methods for general nonlinear programming is to determine effective path-following strategies and their implementations. The objective of this work is to present numerical comparisons of several path-following strategies for the local interior-point Newton method given by El-Bakry, Tapia, Tsuchiya, and Zhang. We conduct numerical experimentation of nine strategies using two central regions, three notions of proximity measures, and three merit functions to obtain an optimal solution. Six of these strategies are implemented for the first time. The numerical results show that the best path-following strategy is that given by Argáez and Tapia.