线性二阶锥规划的一个光滑化方法及其收敛性

首先讨论了用Chen-Harker-Kanzow-Smale光滑函数刻画线性二阶锥规划的中心路径条件;基于此,提出了求解线性二阶锥规划的一个光滑化算法,然后分析了该算法的全局及其局部二次收敛性质.     

二次锥规划的预估-校正光滑方法

基于光滑Fischer-Burmeister函数,给出一个求解二次锥规划的预估-校正光滑牛顿法.该算法构造一个等价于最优性条件的非线性方程组,再用牛顿法求解此方程组的扰动.在适当的假设下,证明算法是全局收敛且是局部二阶收敛的.数值试验表明算法的有效性.     

一个新的求解二阶锥规划的非内部连续化算法

基于光滑Fischer-Burmeister函数,本文给出一个新的求解二阶锥规划的非内部连续化算法.算法对初始点的选取没有任何限制,并且在每一步迭代只需求解一个线性方程组并进行一次线性搜索.在不需要满足严格互补条件下,证明了算法是全局收敛且是局部超线性收敛的.数值试验表明算法是有效的.     

二阶锥规划一个超线性收敛的非内部连续化算法

基于非光滑向量值最小函数的一个新光滑函数, 建立了二阶锥规划一个超线性收敛的非内部连续化算法. 该算法的特点如下: 首先, 初始点任意; 其次, 每次迭代只需求解一个线性方程组即可得到搜索方向; 最后, 在无严格互补假设下, 获得算法的全局收敛性、强收敛性和超线性收敛性. 数值结果表明算法是有效的.     

一个求解二阶锥互补问题的非单调光滑算法

光滑算法是求解二阶锥互补问题非常有效的方法,而这类算法通常采用单调线性搜索.给出了一个求解二阶锥互补问题的非单调光滑算法,在不需要满足严格互补条件下证明了算法是全局和局部二阶收敛的.数值试验表明算法是有效的.     

一个求解二阶锥互补问题的非单调光滑算法北大核心

光滑算法是求解二阶锥互补问题非常有效的方法,而这类算法通常采用单调线性搜索.给出了一个求解二阶锥互补问题的非单调光滑算法,在不需要满足严格互补条件下证明了算法是全局和局部二阶收敛的.数值试验表明算法是有效的.     

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.     

一种具有全局收敛性的求解二阶锥规划的非精确光滑算法

在方程组方法框架下,给出了一种求解二阶锥规划的非精确光滑算法.在适当的条件下,证明了该算法具有全局收敛性.数值试验表明该算法对求解中大规模二阶锥规划是有效的.     

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.     

非线性二阶锥互补问题的低阶罚函数算法（英文）

本文研究非线性二阶锥互补问题的一般低阶罚函数算法.并将非线性二阶锥互补问题转化为序列非线性方程组.在一定条件下,当罚因子趋向于无穷时,获得序列非线性方程组的解序列以指数速度收敛于原始非线性二阶锥互补问题的解,推广了幂罚函数算法求解非线性二阶锥互补问题的结果.数值实验结果说明了算法的有效性.     

A one-step smoothing Newton method for second-order cone programming

A new smoothing function for the second-order cone programming is given by smoothing the symmetric perturbed Fischer–Burmeister function. Based on this new function, a one-step smoothing Newton method is presented for solving the second-order cone programming. The proposed algorithm solves only one linear system of equations and performs only one line search at each iteration. This algorithm does not have restrictions regarding its starting point and is Q-quadratically convergent. Numerical results suggest the effectiveness of our algorithm.     

A new smoothing Newton-type method for second-order cone programming problems

A new smoothing function of the well-known Fischer–Burmeister function is given. Based on this new function, a smoothing Newton-type method is proposed for solving second-order cone programming. At each iteration, the proposed algorithm solves only one system of linear equations and performs only one line search. This algorithm can start from an arbitrary point and it is Q-quadratically convergent under a mild assumption. Numerical results demonstrate the effectiveness of the algorithm.     

A smoothing-type Newton method for second-order cone programming problems based on a new smooth function

A new smoothing function similar with the well known Fischer-Burmeister function is given. Based on this new function, a smoothing-type Newton method is proposed for solving second-order cone programming. At each iteration, the proposed algorithm solves only one system of linear equations and performs only one line search. This algorithm can start from an arbitrary point and it is Q-quadratically convergent under a mild assumption. Preliminary numerical results demonstrate the effectiveness of the method.     

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.     

SMOOTHING NEWTON ALGORITHM FOR THE CIRCULAR CONE PROGRAMMING WITH A NONMONOTONE LINE SEARCH

In this paper, we present a nonmonotone smoothing Newton algorithm for solving the circular cone programming(CCP) problem in which a linear function is minimized or maximized over the intersection of an affine space with the circular cone. Based on the relationship between the circular cone and the second-order cone(SOC), we reformulate the CCP problem as the second-order cone problem(SOCP). By extending the nonmonotone line search for unconstrained optimization to the CCP, a nonmonotone smoothing Newton method is proposed for solving the CCP. Under suitable assumptions, the proposed algorithm is shown to be globally and locally quadratically convergent. Some preliminary numerical results indicate the effectiveness of the proposed algorithm for solving the CCP.     

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.     

A smoothing Newton method for second-order cone optimization based on a new smoothing function

A new smoothing function is given in this paper by smoothing the symmetric perturbed Fischer-Burmeister function. Based on this new smoothing function, we present a smoothing Newton method for solving the second-order cone optimization (SOCO). The method solves only one linear system of equations and performs only one line search at each iteration. Without requiring strict complementarity assumption at the SOCO solution, the proposed algorithm is shown to be globally and locally quadratically convergent. Numerical results demonstrate that our algorithm is promising and comparable to interior-point methods.