Newton type methods for solving nonsmooth equations

Numerical methods for the solution of nonsmooth equations are studied. A new subdifferential for a locally Lipschitzian function is proposed. Based on this subdifferential, Newton methods for solving nonsmooth equations are developed and their convergence is shown. Since this subdifferential is easy to be computed, the present Newton methods can be executed easily in some applications.

The Embedding Method for Nonsmooth Equations

In this paper. we present a class of' embedding methods for nonsmooth equations. Under suitable conditions, we Prove that there exists a homotopy solution curve, which is Unique and continuous. We also prove that the solution curve is singlcvalue-d with respect to the homotopy parameter. Then we construct all efficient algorithm for this class of equations and prove its convcrgcnce. Filially, we apply the algorithm to the nonlinear complementarity problem. The numerical results show that tile algorithm is satisfacotry.

An Inexact Parameterized Newton Method for B-Differentiable Equations

In this paper, we establish an inexact parameterized Newton method for solving the B differentiable equations. By introducing a new concept, we prove the local and large range convergence of the method under some weaker assumptions. We have conducted some numerical experiments. The numerical results show that the method is effective.

Solving constrained minimax problem via nonsmooth equations method

A new nonsmooth equations model of constrained minimax problem was de-rived. The generalized Newton method was applied for solving this system of nonsmooth equations system. A new algorithm for solving constrained minimax problem was established. The local superlinear and quadratic convergences of the algorithm were discussed.

Nonsmooth Equations of K-T Systems for a Constrained Minimax Problem

Using K-T optimality condition of nonsmooth optimization, we establish two equivalent systems of the nonsmooth equations for the constrained minimax problem directly. Then generalized Newton methods are applied to solve these systems of the nonsmooth equations. Thus a new approach to solving the constrained minimax problem is developed.

On the local convergence of a stochastic semismooth Newton method for nonsmooth nonconvex optimization

In this work,we present probabilistic local convergence results for a stochastic semismooth Newton method for a class of stochastic composite optimization problems involving the sum of smooth nonconvex and nonsmooth convex terms in the objective function.We assume that the gradient and Hessian information of the smooth part of the objective function can only be approximated and accessed via calling stochastic firstand second-order oracles.The approach combines stochastic semismooth Newton steps,stochastic proximal gradient steps and a globalization strategy based on growth conditions.We present tail bounds and matrix concentration inequalities for the stochastic oracles that can be utilized to control the approximation errors via appropriately adjusting or increasing the sampling rates.Under standard local assumptions,we prove that the proposed algorithm locally turns into a pure stochastic semismooth Newton method and converges r-linearly or r-superlinearly with high probability.

A Spectral Projected Gradient-Newton Two Phase Method for Constrained Nonlinear Equations

In this paper, we proposed a spectral gradient-Newton two phase method for constrained semismooth equations. In the first stage, we use the spectral projected gradient to obtain the global convergence of the algorithm, and then use the final point in the first stage as a new initial point to turn to a projected semismooth asymptotically newton method for fast convergence.

Proximal Methods for Elliptic Optimal Control Problems with Sparsity Cost Functional

First-order proximal methods that solve linear and bilinear elliptic optimal control problems with a sparsity cost functional are discussed. In particular, fast convergence of these methods is proved. For benchmarking purposes, inexact proximal schemes are compared to an inexact semismooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of proximal schemes applied to infinite-dimensional elliptic optimal control problems and to validate the theoretical estimates.

解非光滑方程组的Newton-GMRES算法

给出了求解非光滑方程组的Newton GMRES迭代法。该方法在求解半光滑方程组时,不需要计算广义Jacobi矩阵,同时使求解相应广义Newton方程组也变得容易。尤其对于大型问题,该方法特别适用。数值例子显示了这种方法的有效性。

A Cubic Spline Method for Solving a Unilateral Obstacle Problem

This paper, we develop a numerical method for solving a unilateral obstacle problem by using the cubic spline collocation method and the generalized Newton method. This method converges quadratically if a relation-ship between the penalty parameter and the discretization parameter h is satisfied. An error estimate between the penalty solution and the discret penalty solution is provided. To validate the theoretical results, some numerical tests on one dimensional obstacle problem are presented.

求解一类无限维非光滑算子方程的光滑化牛顿法

研究一类无限维非光滑算子方程的光滑化牛顿法,构造光滑函数逼近非光滑算子.在半光滑假设条件下,证明了光滑化牛顿法具有全局超线性收敛性.研究表明,此算法可用来求解一类特殊的来源于无限维非线性互补问题的非光滑算子方程.

电力系统的稳定平衡解模型及计算方法

针对电力系统中静态稳定和避免奇异诱导分岔、鞍结分岔、Hopf分岔几类典型的系统稳定问题,通过分析Jacobian矩阵特征值的性质,并结合数学上谱函数的半光滑特性,分别建立相应的几类系统稳定平衡解的数学模型。新模型为具有半光滑不等式约束的非线性方程组。模型的特点是不仅具有较好的数学性质,且可满足平衡解处的稳定性要求。针对数学模型的半光滑特性,利用光滑化函数将模型进行转换,进而建立模型求解的一类光滑化牛顿型算法。该算法理论上享有良好的与传统牛顿法相同的全局与局部收敛性能。通过电力系统的一个避免鞍结分岔的实例,测试模型及算法的可行性。

半光滑方程的牛顿型分解算法及其在最优潮流中的应用

对具有弱耦合特性的非线性半光滑方程组提出了牛顿型分解算法,理论上证明了新算法的收敛性.新算法享有分解法节省计算量的优点,且推广了光滑方程于半光滑方程系统.根据电力系统有功与电压、无功和相角固有的弱耦合性质,运用新算法于电力系统的最优潮流(Optimal Power Flow-OPF)的求解,计算结果显示了算法的有效性.

一类非光滑优化及其在控制系统稳定化中的应用

研究一类来自控制系统稳定化中的非光滑优化问题.考虑Lyapunov函数是非光滑的,特别是有限个光滑函数的极大值函数.建立了相应的非光滑优化模型,进一步导出了这类非光滑优化的KKT系统,然后基于非线性互补函数将此KKT系统转化成一个非光滑方程组,最后分别用广义牛顿法和光滑化牛顿法求解此非光滑方程组,使得此类稳定化设计可以具体实现.

求解LC^1约束优化问题的非精确广义牛顿法

通过将非线性LC1约束优化问题的KKT条件转化成半光滑方程组,提出一个求解LC1约束优化问题的非精确广义牛顿法,在一定的条件下证明了算法的全局收敛性和超线性收敛性.

求解垂直互补问题的参数牛顿法(英文)

给出了求解垂直互补问题的一种参数牛顿法,在较为温和的条件下证明了该方法的局部超线性收敛结果,并且给出了具体数值计算.

两个半光滑函数之和的非光滑方程组解法

对两个半光滑函数之和F（x）=F1（x）＋F2（x），其中F1，F2均为半光滑函数，给出了求解F（x）=0的一种广义牛顿法．算法在每一迭代点处分别计算中一个元素，而不需计算中元素．

广义约束极大极小问题的非光滑方程组模型及其应用

广义约束极大极小问题在理论和实践中有着广泛的应用.为了能够借助已有的优化方法解决这类问题,利用KKT最优性条件和Fischer-Burmeister非线性互补函数,给出了广义约束极大极小问题的两个等价的非光滑方程组模型,介绍了1个相应的解法—Newton法,并给出了该模型在车间调度方面的应用.

非光滑方程组牛顿法的全局收敛性分析(英文)

通过定义一种新的 * -微分 ,本文给出了局部 L ipschitz非光滑方程组的牛顿法 ,并对其全局收敛性进行了研究 .该牛顿法结合了非光滑方程组的局部收敛性和全局收敛性 .最后 ,我们把这种牛顿法应用到非光滑函数的光滑复合方程组问题上 。

求解非光滑方程的阻尼PSB方法与阻尼DFP方法的收敛性分析

提出了几种求解非光滑方程的阻厄ＰＳＢ方法及阻尼ＤＦＰ方法（即采用Ａｒｍｉｊｏ原则确定步长），并讨论了这些算法的全局收敛性及超线性收敛性。