一个新的求解加权线性互补问题的非单调光滑牛顿法

本文研究了求解加权线性互补问题的光滑牛顿法.利用一类光滑函数将加权线性互补问题等价转化成一个光滑方程组,然后提出一个新的光滑牛顿法去求解它.在适当条件下,证明了算法具有全局和局部二次收敛性质.与现有的光滑牛顿法不同,我们的算法采用一个非单调无导数线搜索技术去产生步长,从而具有更好的收敛性质和实际计算效果.     

一个求解特殊加权线性互补问题的预估校正光滑牛顿法

本文研究特殊加权线性互补问题的求解方法.我们利用一个带有权重的光滑函数将问题转化成一个光滑方程组,然后提出一个预估校正光滑牛顿法去求解它.在适当条件下,我们证明提出的算法具有全局和局部二次收敛性质.特别地,在解集非空的条件下,我们证明价值函数点列收敛到零.数值试验表明算法是有效的.     

一个求解广义圆锥互补问题的无导数光滑算法

本文研究了一个求解广义圆锥互补问题的无导数光滑算法.利用光滑函数将广义圆锥互补问题等价转化成一个光滑方程组,然后再利用牛顿法求解此方程组.该算法采用了一种新的非单调无导数线搜索技术,并且在适当条件下具有全局和局部二次收敛性质.数值实验结果表明算法是非常有效的.     

二阶锥权互补问题的非精确非内点连续化算法

基于二阶锥权互补函数,将二阶锥权互补问题转化为一个方程组,运用非精确非内点连续化算法求解该方程组.该算法能以任意点作为初始点,且每次迭代时至多求解一个方程组.为节省算法求解方程组时的计算时间和内存,将非精确牛顿法引入到算法中.在适当假设下,证明了该算法是全局与局部二阶收敛的.最后数值实验表明了算法的良好性能.     

基于新光滑函数的P0映射非线性互补问题的光滑牛顿法(英文)

非线性互补问题(NCP)可以重新表述为一个非光滑方程组的解.通过引入一个新的光滑函数,将问题近似为参数化光滑方程组.基于这个光滑函数,我们提出了一个求解P₀映射和R₀映射非线性互补问题的光滑牛顿法.该算法每次迭代只求解一个线性方程和一次线搜索.在适当的条件下,证明了该方法是全局和局部二次收敛的.数值结果表明,该算法是有效的.     

求解广义非线性互补问题的光滑化拟牛顿法

利用光滑对称扰动Fischer-Burmeister函数将广义非线性互补问题转化为非线性方程组,提出新的光滑化拟牛顿法求解该方程组.然后证明该算法是全局收敛的,且在一定条件下证明该算法具有局部超线性(二次)收敛性.最后用数值实验验证了该算法的有效性.     

圆锥规划问题的光滑牛顿方法

给出求解圆锥规划问题的一种新光滑牛顿方法.基于圆锥互补函数的一个新光滑函数,将圆锥规划问题转化成一个非线性方程组,然后用光滑牛顿方法求解该方程组.该算法可从任意初始点开始,且不要求中间迭代点是内点.运用欧几里得代数理论,证明算法具有全局收敛性和局部超线性收敛速度.数值算例表明算法的有效性.     

非光滑非线性互补问题的牛顿法

研究了非光滑的非线性互补问题. 首先将非光滑的非线性互补问题转化为一个非光滑方程组,然后用牛顿法求解这个非光滑方程组. 在该牛顿法中,每次迭代只需一个原始函数B-微分中的一个元素. 最后证明了该牛顿法的超线性收敛性.     

求解圆锥规划的光滑牛顿法

圆锥规划是一类重要的非对称锥优化问题.基于一个光滑函数,将圆锥规划的最优性条件转化成一个非线性方程组,然后给出求解圆锥规划的光滑牛顿法.该算法只需求解一个线性方程组和进行一次线搜索.运用欧几里得约当代数理论,证明该算法具有全局和局部二阶收敛性.最后数值结果表明算法的有效性.     

解凸约束非线性单调方程组的无导数低存储Broyden族投影法

拟牛顿法是求解非线性方程组的一类有效方法.相较于经典的牛顿法,拟牛顿法不需要计算Jacobian矩阵且仍具有超线性收敛性.本文基于BFGS和DFP的迭代公式,构造了新的充分下降方向.将该搜索方向和投影技术相结合,本文提出了无导数低存储的投影算法求解带凸约束的非线性单调方程组并证明了该算法是全局且R-线性收敛的.最后,将该算法用于求解压缩感知问题.实验结果表明,本文所提出的算法具有良好的计算效率和稳定性.     

A non-interior continuation method for second-order cone programming

We extend the smoothing function proposed by Huang, Han and Chen [Journal of Optimization Theory and Applications, 117 (2003), pp. 39–68] for the non-linear complementarity problems to the second-order cone programming (SOCP). Based on this smoothing function, a non-interior continuation method is presented for solving the SOCP. The proposed algorithm solves only one linear system of equations and performs only one line search at each iteration. It is shown that our algorithm is globally and locally superlinearly convergent in absence of strict complementarity at the optimal solution. Numerical results indicate the effectiveness of the algorithm.     

Sub-quadratic convergence of a smoothing Newton method for second-order cone programming

In this paper, we present a smoothing Newton method for solving the second-order cone programming (SOCP) based on the Chen–Harker–Kanzow–Smale (CHKS) smoothing function. Our smoothing method reformulates SOCP as a nonlinear system of equations and then applies Newton’s method to the system. The proposed method solves only one linear system of equations and performs only one line search at each iteration. It is shown that the method is globally and locally sub-quadratically convergent under a nonsingularity assumption. Numerical results suggest that the method is promising.     

Analysis of a non-interior continuation method for second-order cone programming

Based on the Chen-Harker-Kanzow-Smale (CHKS) smoothing function, a non-interior continuation method is presented for solving the second-order cone programming (SOCP). Our algorithm reformulates the SOCP as a nonlinear system of equations and then applies Newton’s method to the perturbation of this system. The proposed algorithm does not have restrictions regarding its starting point and solves at most one linear system of equations at each iteration. Under suitable assumptions, the algorithm is shown to be globally and locally quadratically convergent. Some numerical results are also included which indicate that our algorithm is promising and comparable to interior-point methods.     

A nonmonotone smoothing Newton method for circular cone programming

The circular cone programming (CCP) problem is to minimize or maximize a linear function over the intersection of an affine space with the Cartesian product of circular cones. In this paper, we study nondegeneracy and strict complementarity for the CCP, and present a nonmonotone smoothing Newton method for solving the CCP. We reformulate the CCP as a second-order cone programming (SOCP) problem using the algebraic relation between the circular cone and the second-order cone. Then based on a one parametric class of smoothing functions for the SOCP, a smoothing Newton method is developed for the CCP by adopting a new nonmonotone line search scheme. Without restrictions regarding its starting point, our algorithm solves one linear system of equations approximately and performs one line search at each iteration. Under mild assumptions, our algorithm is shown to possess global and local quadratic convergence properties. Some preliminary numerical results illustrate that our nonmonotone smoothing Newton method is promising for solving the CCP.     

AN INFEASIBLE-INTERIOR-POINT PREDICTOR-CORRECTOR ALGORITHM FOR THE SECOND-ORDER CONE PROGRAM

A globally convergent infeasible-interior-point predictor-corrector algorithm is presented for the second-order cone programming （SOCP） by using the Alizadeh- Haeberly-Overton （AHO） search direction. This algorithm does not require the feasibility of the initial points and iteration points. Under suitable assumptions, it is shown that the algorithm can find an -approximate solution of an SOCP in at most O（√n ln（ε0/ε）） iterations. The iteration-complexity bound of our algorithm is almost the same as the best known bound of feasible interior point algorithms for the SOCP.     

An SQP-type algorithm for nonlinear second-order cone programs

We propose an SQP-type algorithm for solving nonlinear second-order cone programming (NSOCP) problems. At every iteration, the algorithm solves a convex SOCP subproblem in which the constraints involve linear approximations of the constraint functions in the original problem and the objective function is a convex quadratic function. Those subproblems can be transformed into linear SOCP problems, for which efficient interior point solvers are available. We establish global convergence and local quadratic convergence of the algorithm under appropriate assumptions. We report numerical results to examine the effectiveness of the algorithm. This work was supported in part by the Scientific Research Grant-in-Aid from Japan Society for the Promotion of Science.     

A new smoothing Newton method for solving constrained nonlinear equations

In this paper, a new smoothing Newton method is proposed for solving constrained nonlinear equations. We first transform the constrained nonlinear equations to a system of semismooth equations by using the so-called absolute value function of the slack variables, and then present a new smoothing Newton method for solving the semismooth equations by constructing a new smoothing approximation function. This new method is globally and quadratically convergent. It needs to solve only one system of unconstrained equations and to perform one line search at each iteration. Numerical results show that the new algorithm works quite well.     

Nonsmooth Equation Based BFGS Method for Solving KKT Systems in Mathematical Programming

In this paper, we present a BFGS method for solving a KKT system in mathematical programming, based on a nonsmooth equation reformulation of the KKT system. We split successively the nonsmooth equation into equivalent equations with a particular structure. Based on the splitting, we develop a BFGS method in which the subproblems are systems of linear equations with symmetric and positive-definite coefficient matrices. A suitable line search is introduced under which the generated iterates exhibit an approximate norm descent property. The method is well defined and, under suitable conditions, converges to a KKT point globally and superlinearly without any convexity assumption on the problem.     

一个解凸二次规划的预测-校正光滑化方法

本文为凸二次规划问题提出一个光滑型方法,它是Engelke和Kanzow提出的解线性规划的光滑化算法的推广。其主要思想是将二次规划的最优性K-T条件写成一个非线性非光滑方程组,并利用Newton型方法来解其光滑近似。本文的方法是预测-校正方法。在较弱的条件下,证明了算法的全局收敛性和超线性收敛性。     

Local reduction based SQP-type method for semi-infinite programs with an infinite number of second-order cone constraints

The second-order cone program (SOCP) is an optimization problem with second-order cone (SOC) constraints and has achieved notable developments in the last decade. The classical semi-infinite program (SIP) is represented with infinitely many inequality constraints, and has been studied extensively so far. In this paper, we consider the SIP with infinitely many SOC constraints, called the SISOCP for short. Compared with the standard SIP and SOCP, the studies on the SISOCP are scarce, even though it has important applications such as Chebychev approximation for vector-valued functions. For solving the SISOCP, we develop an algorithm that combines a local reduction method with an SQP-type method. In this method, we reduce the SISOCP to an SOCP with finitely many SOC constraints by means of implicit functions and apply an SQP-type method to the latter problem. We study the global and local convergence properties of the proposed algorithm. Finally, we observe the effectiveness of the algorithm through some numerical experiments.