非线性约束优化一个强收敛的广义投影强次可行方向法

讨论带非线性不等式和等式约束的最优化问题,借助强次可行方向法和半罚函数的思想,给出了问题的一个新的广义投影强次可行方向法.该算法的一个重要特性是有限次迭代后,迭代点落入半罚问题的可行域.在适当的条件下证明了算法的全局收敛性和强收敛性.数值实验表明算法是有效的.     

一种求解非线性约束优化问题的无罚函数无滤子的方法

借助于强次可行方向法的思想和滤子法的思想,给出了一种求解非线性约束优化问题的无罚函数无滤子的方法.方法借助于广义投影技术产生搜索方向,直接通过原目标函数和约束违反度函数作为搜索函数来产生步长,有效地避免了消耗计算成本的恢复阶段.最后在适当的假设条件下,给出了算法的全局收敛性和有效性.     

非线性规划的拟罚函数—强次可行方向法

本文首先提出非线性规划的拟Ｋｕｈｎ—Ｔｕｃｋｅｒ点和拟罚函数法的概念和思想，然后结合强次可行方向法思想给出问题的两个新型算法，称之为拟罚函数—强次可行方向法．证明了该算法收敛到原问题的拟Ｋｕｈｎ—Ｔｕｃｋｅｒ点．     

线性均衡约束最优化的一个广义投影强次可行方向法

本文讨论带线性均衡约束最优化问题,首先利用摄动技术和一个互补函数将问题等价转化为一般约束最优化问题,然后结合广义投影技术和强次可行方向法思想,建立了问题的一个新算法.算法在迭代过程中保证搜索方向不为零,从而使得每次迭代只需计算一次广义投影.在适当的条件下,证明了算法的全局收敛性,并对算法进行了初步的数值试验.     

一般约束最优化强收敛的拟乘子-强次可行方向法

本文讨论一般等式和不等式约束优化问题 ,利用广义投影技术和强次可行方向法思想 ,结合拟 K-T点和拟乘子法 [1] 两个新概念 ,建立问题一个初始点任意的有显式搜索方向的新算法 .证明算法不仅收敛到原问题的拟 K- T点 ,且具有更好的强收敛性 .对算法进行了一定的数值试验 .     

一般约束最优化拓广的强次可行方向法

本文讨论非线性等式与不等式最优化问题,引进一个拟罚函数及其相应的只带不等式约束的辅助问题,然后采用广义投影技术和强次可行方向法思想建立原问题的一个全局收敛新算法,该算法具有初点始任意,结构简单,计算量较小等特点。     

不等式约束最优化超线性与二次收敛的强次可行SQP算法

利用ＳＱＰ方法、广义投影技术和强次可行方（向）法思想,建立不等式约束优化一个新的初始点任意的快速收敛算法． 算法每次迭代仅需解一个总存在可行解的二次子规划,或用广义投影计算“一阶”强次可行下降辅助搜索方向;采用曲线搜索与直线搜索相结合的方法产生步长． 在较温和的条件下,算法具有全局收敛性、强收敛性、超线性与二次收敛性． 给出了算法有效的数值试验．     

一般约束最优化的拟乘子—强次可行方向法

本文讨论一般等式和不等式约束的优化问题，首先提出了问题的拟Ｋｕｈｎ－Ｔｕｃｋｅｒ点和拟乘子法两个新概念，然后借助于不等式约束优化问题强次可行方向法的思想和技巧建立问题的两个新算法。     

不等式约束优化一个基于滤子思想的广义梯度投影算法

讨论非线性不等式约束优化问题, 借鉴于滤子算法思想,提出了一个新型广义梯度投影算法.该方法既不使用罚函数又无真正意义下的滤子.每次迭代通过一个简单的显式广义投影法产生搜索方向,步长由目标函数值或者约束违反度函数值充分下降的Armijo型线搜索产生.算法的主要特点是: 不需要迭代序列的有界性假设;不需要传统滤子算法所必需的可行恢复阶段;使用了ε积极约束集减小计算量.在合适的假设条件下算法具有全局收敛性, 最后对算法进行了初步的数值实验.     

强组合PhaseⅠ－PhaseⅡ次可行方向法

本文对Ｐｏｌａｋ等人的组合ＮａｓｅⅠ－Ⅱ可行方向法进行改进，使之不仅能自动地将初始化阶段（Ｐｈａｓｅｌ）和最优化阶段（ＰｈａｓｅⅡ）统一起来，而且保证了满足不等式约束的函数个数不断叠累递增，故称改进后的算法为强组合ＰｈａｓｅⅠ－ⅡＰｈａｓｅⅡ次可行方向法．本文算法包含了一种新的目标局数非单词的非精确线搜索，它保证了算法产生的点列的任何聚点都是问题的Ｋ－Ｔ的点．     

Strongly sub-feasible direction method for constrained optimization problems with nonsmooth objective functions

In this paper, we propose a strongly sub-feasible direction method for the solution of inequality constrained optimization problems whose objective functions are not necessarily differentiable. The algorithm combines the subgradient aggregation technique with the ideas of generalized cutting plane method and of strongly sub-feasible direction method, and as results a new search direction finding subproblem and a new line search strategy are presented. The algorithm can not only accept infeasible starting points but also preserve the “strong sub-feasibility” of the current iteration without unduly increasing the objective value. Moreover, once a feasible iterate occurs, it becomes automatically a feasible descent algorithm. Global convergence is proved, and some preliminary numerical results show that the proposed algorithm is efficient.     

A new finitely convergent algorithm for systems of nonlinear inequalities

In this work, combining the generalized projection techniques with the idea of a strongly sub-feasible direction method, a new algorithm for solving systems of nonlinear inequalities is presented. At each iteration of the proposed algorithm, the search direction is yielded by just one new explicit formula. The proposed algorithm is proved not only to possess global and strong convergence but also to be able to produce a solution in a finite number of iterations. Finally, some interesting numerical results are reported.     

Sequential quadratically constrained quadratic programming norm-relaxed algorithm of strongly sub-feasible directions

In this paper, we present a sequential quadratically constrained quadratic programming (SQCQP) norm-relaxed algorithm of strongly sub-feasible directions for the solution of inequality constrained optimization problems. By introducing a new unified line search and making use of the idea of strongly sub-feasible direction method, the proposed algorithm can well combine the phase of finding a feasible point (by finite iterations) and the phase of a feasible descent norm-relaxed SQCQP algorithm. Moreover, the former phase can preserve the “sub-feasibility” of the current iteration, and control the increase of the objective function. At each iteration, only a consistent convex quadratically constrained quadratic programming problem needs to be solved to obtain a search direction. Without any other correctional directions, the global, superlinear and a certain quadratic convergence (which is between 1-step and 2-step quadratic convergence) properties are proved under reasonable assumptions. Finally, some preliminary numerical results show that the proposed algorithm is also encouraging.     

A superlinearly convergent strongly sub-feasible SSLE-type algorithm with working set for nonlinearly constrained optimization

In this paper, by means of a new efficient identification technique of active constraints and the method of strongly sub-feasible direction, we propose a new sequential system of linear equations (SSLE) algorithm for solving inequality constrained optimization problems, in which the initial point is arbitrary. At each iteration, we first yield the working set by a pivoting operation and a generalized projection; then, three or four reduced linear equations with a same coefficient are solved to obtain the search direction. After a finite number of iterations, the algorithm can produced a feasible iteration point, and it becomes the method of feasible directions. Moreover, after finitely many iterations, the working set becomes independent of the iterates and is essentially the same as the active set of the KKT point. Under some mild conditions, the proposed algorithm is proved to be globally, strongly and superlinearly convergent. Finally, some preliminary numerical experiments are reported to show that the algorithm is practicable and effective.     

A superlinearly convergent norm-relaxed SQP method of strongly sub-feasible directions for constrained optimization without strict complementarity

In this paper, a kind of optimization problems with nonlinear inequality constraints is discussed. Combined the ideas of norm-relaxed SQP method and strongly sub-feasible direction method as well as a pivoting operation, a new fast algorithm with arbitrary initial point for the discussed problem is presented. At each iteration of the algorithm, an improved direction is obtained by solving only one direction finding subproblem which possesses small scale and always has an optimal solution, and to avoid the Maratos effect, another correction direction is yielded by a simple explicit formula. Since the line search technique can automatically combine the initialization and optimization processes, after finite iterations, the iteration points always get into the feasible set. The proposed algorithm is proved to be globally convergent and superlinearly convergent under mild conditions without the strict complementarity. Finally, some numerical tests are reported.     

Superlinearly Convergent Norm-Relaxed SQP Method Based on Active Set Identification and New Line Search for Constrained Minimax Problems

In this paper, the minimax problems with inequality constraints are discussed, and an alternative fast convergent method for the discussed problems is proposed. Compared with the previous work, the proposed method has the following main characteristics. First, the active set identification which can reduce the scale and the computational cost is adopted to construct the direction finding subproblems. Second, the master direction and high-order correction direction are computed by solving a new type of norm-relaxed quadratic programming subproblem and a system of linear equations, respectively. Third, the step size is yielded by a new line search which combines the method of strongly sub-feasible direction with the penalty method. Fourth, under mild assumptions without any strict complementarity, both the global convergence and rate of superlinear convergence can be obtained. Finally, some numerical results are reported.     

A method combining norm-relaxed QP subproblems with systems of linear equations for constrained optimization

Based on the ideas of norm-relaxed sequential quadratic programming (SQP) method and the strongly sub-feasible direction method, we propose a new SQP algorithm for the solution of nonlinear inequality constrained optimization. Unlike the previous work, at each iteration, the norm-relaxed quadratic programming subproblem (NRQPS) in our algorithm only consists of the constraints corresponding to an estimate of the active set, and the high-order correction direction (used to avoid the Maratos effect) is obtained by solving a system of linear equations (SLE) which also only consists of such a subset of constraints and gradients. Moreover, the line search technique can effectively combine the initialization process with the optimization process, and therefore (if the starting point is not feasible) the iteration points always get into the feasible set after a finite number of iterations. The global convergence is proved under the Mangasarian–Fromovitz constraint qualification (MFCQ), and the superlinear convergence is obtained without assuming the strict complementarity. Finally, the numerical experiments show that the proposed algorithm is effective and promising for the test problems.     

基于并行计算的多方向强次可行模松弛SQP算法

本文研究求解非线性约束优化问题.利用多方向并行方法,提出了一个新的强次可行模松弛序列二次规划(SQP)算法.数值试验表明,迭代次数和计算时间少于只取单一参数的传统算法.     

线性约束凸规划的鞍梯度法

求线性约束凸规划问题的最优解。方法：在鞍梯度法的基础上提出了一个具有全局收敛性的原一对偶外点算法。结果：每步迭代利用Lagrange函数的鞍梯度构造搜索方向，生成次可行解序列，由此得到的序列的极限就是原-对偶问题的最优解。结论：即使从原一对偶问题的不可行点开始迭代算法也收敛。