一类非线性约束优化的修正正割方法的收敛性

本文修正了一类非线性约束优化的正割方法.通过引入不可微势函数作不精确的一维搜索,证明了修正后的正割算法不仅具有原算法不具备的整体收敛性;而且保持局部两步Q-超线性收敛速率.进一步数值结果表明此算法是非常有效的.     

对称锥互补问题的一类惩罚FB函数

利用欧几里德若当代数技术,在单调的条件下,用内积的方法证明了对称锥互补问题的一类FB互补函数相应的势函数的水平集有界性. 该方法在理论和应用上相较于以往用迹不等式证明势函数水平集有界性更具普适性和推广价值. 在设计算法求解势函数的无约束极小化问题时,水平集有界性是保证下降算法收敛的重要条件,因此,对算法的设计具有理论意义.     

环图上Sturm-Liouville算子的部分反谱问题

该文考虑量子环图上Sturm-Liouville算子.在一部分势函数已知情况下,通过谱数据重构未知势函数,并且给出其重构算法和唯一性定理.     

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。     

任意凸多面体上布点均匀性的度量

均匀性度量是构作均匀设计的基础,本文从距离概念出发,通过对称的方法,得到一种新的距离函数-势函数,并将势函数作为衡量任意凸多面体上布点均匀性好坏的准则.数值例子和多变量Kendall 协和系数检验表明,当试验区域限制在单位立方体上时,势函数与目前常用的两种偏差-中心化L_2-偏差和可卷L_2.偏差在度量布点均匀性方面结论一致.     

区域约束环境下柔性关节机械臂的一致性控制

文章研究了柔性关节机械臂在区域约束条件下的一致性控制问题,提出了一种基于区域势函数约束环境下的柔性关节机械臂一致性控制算法,该算法能够有效控制柔性机械臂同步态的范围,无需计算最终的同步态,因而适用于实际多柔性关节机械臂区域内的协同控制和应用.进一步地,基于网络拓扑结构特征,给出了实现柔性关节机械臂区域可达一致性控制的充分条件.最后,文章给出了数值模拟以验证所提算法的有效性和正确性.     

求解线性互补问题的一种新的势下降内点算法

本文针对具有半正定矩阵的线性互补问题提出了一个新的内点方法———势函数下降内点方法.采用部分校正技术和Sherman-Morrison-Woodbury准则获得问题的近似最优解.讨论了该算法的收敛性,并证明了该算法为多项式算法.     

一种求解合取范式可满足性问题的数学物理方法

给出合取范式与带电质点在某类静电场中势函数的一一对应关系,证明判断一个合取范式是否可满足等价于判断一个带电质点在相应的静电场中是否有使其势函数为零的位置,由于带电质点在静电场中总是沿使其势能下降最快的方向,也就是沿其势函数梯度指引的方向运动,并最终达到势能最低的位置。因此,对一个客观上可满足的合取范式所对应的势函数,使用梯度算法是求使该合取范式成真的赋值的高效算法。     

线性不等式约束的广义非线性互补问题的仿射内点信赖域方法

提供了一种新的非单调内点回代线搜索技术的仿射内点信赖域方法解线性不等式约束的广义非线性互补问题(GCP).基于广义互补问题构成的半光滑方程组的广义Jacobian矩阵,算法使用l_2范数作为半光滑方程组的势函数,形成的信赖域子问题为一个带椭球约束的线性化的二次模型.利用广义牛顿方程计算试探迭代步,通过内点映射回代技术确保迭代点是严格内点,保证了算法的整体收敛性.在合理的条件下,证明了信赖域算法在接近最优点时可转化为广义拟牛顿步,进而具有局部超线性收敛速率.非单调技术将克服高度非线性情况加速收敛进展.最后,数值结果表明了算法的有效性.     

线性不等式约束的广义非线性互补问题的仿射内点信赖域方法

提供了一种新的非单调内点回代线搜索技术的仿射内点信赖域方法解线性不等式约束的广义非线性互补问题(GCP).基于广义互补问题构成的半光滑方程组的广义Jacobian矩阵,算法使用l2范数作为半光滑方程组的势函数,形成的信赖域子问题为一个带椭球约束的线性化的二次模型.利用广义牛顿方程计算试探迭代步,通过内点映射回代技术确保迭代点是严格内点,保证了算法的整体收敛性.在合理的条件下,证明了信赖域算法在接近最优点时可转化为广义拟牛顿步,进而具有局部超线性收敛速率.非单调技术将克服高度非线性情况加速收敛进展.最后,数值结果表明了算法的有效性.     

On certain optimization methods with finite-step inner algorithms for convex finite-dimensional problems with inequality constraints

Numerical methods are proposed for solving finite-dimensional convex problems with inequality constraints satisfying the Slater condition. A method based on solving the dual to the original regularized problem is proposed and justified for problems having a strictly uniformly convex sum of the objective function and the constraint functions. Conditions for the convergence of this method are derived, and convergence rate estimates are obtained for convergence with respect to the functional, convergence with respect to the argument to the set of optimizers, and convergence to the g-normal solution. For more general convex finite-dimensional minimization problems with inequality constraints, two methods with finite-step inner algorithms are proposed. The methods are based on the projected gradient and conditional gradient algorithms. The paper is focused on finite-dimensional problems obtained by approximating infinite-dimensional problems, in particular, optimal control problems for systems with lumped or distributed parameters.     

Superlinearly convergent quasi-newton algorithms for nonlinearly constrained optimization problems

A class of algorithms for nonlinearly constrained optimization problems is proposed. The subproblems of the algorithms are linearly constrained quadratic minimization problems which contain an updated estimate of the Hessian of the Lagrangian. Under suitable conditions and updating schemes local convergence and a superlinear rate of convergence are established. The convergence proofs require among other things twice differentiable objective and constraint functions, while the calculations use only first derivative data. Rapid convergence has been obtained in a number of test problems by using a program based on the algorithms proposed here.Research supported by NSF Grant GJ-35292 at the University of Wisconsin.     

A Steffensen-like method and its higher-order variants

For solving nonlinear equations, we suggest a second-order parametric Steffensen-like method, which is derivative free and only uses two evaluations of the function in one step. We also suggest a variant of the Steffensen-like method which is still derivative free and uses four evaluations of the function to achieve cubic convergence. Moreover, a fast Steffensen-like method with super quadratic convergence and a fast variant of the Steffensen-like method with super cubic convergence are proposed by using a parameter estimation. The error equations and asymptotic convergence constants are obtained for the discussed methods. The numerical results and the basins of attraction support the proposed methods.     

On the order conditions of fuzzy convergence classes

By means of the order structure of the related lattice, the LIMINF condition of fuzzy convergence classes is proposed in this paper, which reflects the essential difference between fuzzy convergence classes and ordinary convergence classes. The relationship between the LIMINF condition and two related conditions proposed by Liu and Wang respectively are discussed. The theory of fuzzy convergence classes based on LIMINF condition is established for topological molecular lattices, L-topological spaces (in the sense of Chang or Lowen), weakly induced spaces, and induced spaces.     

加快迭代收敛速度的两个命题

定义了单调收敛函数和交错收敛函数,并根据其收敛特点,提出并证明了加快其收敛速度的两个命题.算例表明其效果较好.     

A truncated conjugate gradient method with an inexact Gauss-Newton technique for solving nonlinear systems

In this paper, a truncated conjugate gradient method with an inexact Gauss-Newton technique is proposed for solving nonlinear systems.?The iterative direction is obtained by the conjugate gradient method solving the inexact Gauss-Newton equation.?Global convergence and local superlinear convergence rate of the proposed algorithm are established under some reasonable conditions. Finally, some numerical results are presented to illustrate the effectiveness of the proposed algorithm.     

双参数并行Jacobi型方法及其收敛性

1983年Missirlis提出了一种解线性代数方程组的方法,称为并行Jacobi型方法(Parallel Jacobi-Type Method)并且讨论了它的收敛性.方法的优越性在于适合并行计算.本文将这个方法推广到两个参数的情形,讨论了方法的收敛性.双参数法一方面保持了适用于并行计算的特点,而且又扩大了方法的应用范围,提高了收敛速度.事实     

AN ADAPTIVE NONMONOTONIC TRUST REGION METHOD WITH CURVILINEAR SEARCHES

In this paper,an algorithm for unconstrained optimization that employs both trustregion techniques and curvilinear searches is proposed.At every iteration,we solve thetrust region subproblem whose radius is generated adaptively only once.Nonmonotonicbacktracking curvilinear searches are performed when the solution of the subproblem isunacceptable.The global convergence and fast local convergence rate of the proposedalgorithms are established under some reasonable conditions.The results of numericalexperiments are reported to show the effectiveness of the proposed algorithms.     

求解时间分布阶扩散方程的两个高阶有限差分格式

基于复化Simpson公式和复化两点Gauss-Legendre公式，构造了两个求解时间分布阶扩散方程的高阶有限差分格式.不同于以往文献中提出的时间一阶或二阶格式，这两种格式在时间方向都具有三阶精度，而在分布阶和空间方向可达到四阶精度.数值结果表明，两种算法都是稳定且收敛的，从而是有效的.两种格式的收敛速率也通过数值实验进行了验证，并且通过和文献中的算法对比可以得出其更为高效,     

混合变异粒子群优化算法及其应用

为改善粒子群优化算法在解决复杂优化问题时收敛质量不高的不足,提出了一种改进的粒子群优化算法,即混合变异粒子群优化算法(HMPSO).HMPSO算法采用了带有随机因子的惯性权重取值更新策略,降低了标准粒子群优化算法中由于粒子飞行速度过大而错过最优解的概率,从而加速了算法的收敛速度.此外,通过混合变异进化环节的引入,缓解了...