不等式约束优化基于新型积极识别集的SQCQP算法

本文提出一个新的求解非线性不等式约束优化问题的罚函数型序列二次约束二次规划(SQCQP)算法.算法每次迭代只需求解一个凸二次约束二次规划(QCQP)子问题,且通过引入新型积极识别集技术,QCQP子问题的规模显著减小,从而降低计算成本.在不需要函数凸性等较弱假设下,算法具有全局收敛性.初步的数值试验表明算法是稳定有效的.     

一般约束优化基于识别函数的模松弛算法

借助于半罚函数和产生工作集的识别函数以及模松弛SQP算法思想, 本文建立了求解带等式及不等式约束优化的一个新算法. 每次迭代中, 算法的搜索方向由一个简化的二次规划子问题及一个简化的线性方程组产生. 算法在不包含严格互补性的温和条件下具有全局收敛性和超线性收敛性. 最后给出了算法初步的数值试验报告.     

混合约束Minimax问题的基于序列线性方程组的模松弛SQP算法

本文针对带等式与不等式的混合约束Minimax问题,提出了基于序列线性方程组的模松弛SQP算法.在新算法中,我们首先引入了ε-积极约束集,在此基础上构造了—个模松弛QP子问题和序列线性方程组,以获得可行下降方向.另外,新算法采取了一种既无罚函数又无滤子的弧搜索步长策略,以避免罚参数的选取·新算法既克服了Maratos效应,又大大地减少了算法的计算工作量和储存量.在适当的假设条件下,证明了算法的全局收敛性.初步数值实验验证了该算法的有效性与优越性.     

一般约束极大极小优化问题一个强收敛的广义梯度投影算法

该文考虑求解带非线性不等式和等式约束的极大极小优化问题,借助半罚函数思想,提出了一个新的广义投影算法.该算法具有以下特点:由一个广义梯度投影显式公式产生的搜索方向是可行下降的;构造了一个新型的最优识别控制函数;在适当的假设条件下具有全局收敛性和强收敛性.最后,通过初步的数值试验验证了算法的有效性.     

一种新的二次约束二次规划问题的分支定界算法

本文为了获得二次约束二次规划(QCQP)问题的全局最优解,提出一种新的参数化线性松弛分支定界算法.该算法利用参数化线性松弛技术,得到(QCQP)的全局最小值的下界,并利用区域缩减技术以最大限度地删除不可行区域,加快该算法的收敛速度.数值实验表明,本文提出的算法是有效并且可行的.     

一种求解混合约束优化问题的半可行序列线性方程组滤子算法的局部收敛性

作者在[10]中提出了一种半可行序列线性规划滤子方法.它将QP-free方法推广至混合约束优化问题上,并且保持对不等式约束的可行性,对等式约束部分用滤子方法处理,从而避免了罚参数的选取.该算法只需求解四个具有相同系数矩阵的线性方程组以得到搜索方向.在一定程度上克服了序列二次规划方法的缺点.[10]中仅给出了全局收敛性.本文主要给出了该算法的局部超线性收敛性证明以及数值结果.     

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.     

不等式约束极大极小问题的可行下降束方法

本文提出一个求解不等式约束极大极小问题的可行下降束方法.该方法的主要特点有(1)借助于函数的次梯度及束方法思想,不需要假设原问题的分量函数具备光滑性;(2)利用部分割平面模型技术,每次无效步迭代仅需利用一个分量函数的函数值和次梯度产生新的割平面,从而有效减少了计算量;(3)能够保证有效迭代点的可行性及目标函数的下降性;(4)引入次梯度聚集技术,对束集中的次梯度进行聚集,克服了数值计算和存储的困难;(5)算法具备全局收敛性,且初步的数值试验表明算法是有效的.     

求解带线性约束的凸优化的一类自适应不定线性化增广拉格朗日方法

增广拉格朗日方法是求解带线性约束的凸优化问题的有效算法.线性化增广拉格朗日方法通过线性化增广拉格朗日函数的二次罚项并加上一个临近正则项,使得子问题容易求解,其中正则项系数的恰当选取对算法的收敛性和收敛速度至关重要.较大的系数可保证算法收敛性,但容易导致小步长.较小的系数允许迭代步长增大,但容易导致算法不收敛.本文考虑求解带线性等式或不等式约束的凸优化问题.我们利用自适应技术设计了一类不定线性化增广拉格朗日方法,即利用当前迭代点的信息自适应选取合适的正则项系数,在保证收敛性的前提下尽量使得子问题步长选择范围更大,从而提高算法收敛速度.我们从理论上证明了算法的全局收敛性,并利用数值实验说明了算法的有效性.     

Minimax问题的一个滤子算法

本文提出一个求解不等式约束的Minimax问题的滤子算法,结合序列二次规划方法,并利用滤子以避免罚函数的使用.在适当的条件下,证明了此方法的全局收敛性及超线性收敛性.数值实验表明算法是有效的.     

Simple Sequential Quadratically Constrained Quadratic Programming Feasible Algorithm with Active Identification Sets for Constrained Minimax Problems

In this paper, the nonlinear minimax problems with inequality constraints are discussed. Based on the idea of simple sequential quadratically constrained quadratic programming algorithm for smooth constrained optimization, an alternative algorithm for solving the discussed problems is proposed. Unlike the previous work, at each iteration, a feasible direction of descent called main search direction is obtained by solving only one subprogram which is composed of a convex quadratic objective function and simple quadratic inequality constraints without the second derivatives of the constrained functions. Then a high-order correction direction used to avoid the Maratos effect is computed by updating the main search direction with a system of linear equations. The proposed algorithm possesses global convergence under weak Mangasarian–Fromovitz constraint qualification and superlinear convergence under suitable conditions with the upper-level strict complementarity. At last, some preliminary numerical results are reported.     

A Nonlinear Lagrange Algorithm for Minimax Problems with General Constraints

This article presents a novel nonlinear Lagrange algorithm for solving minimax optimization problems with both inequality and equality constraints, which eliminates the nonsmoothness of the considered problems and the numerical difficulty of the penalty method. The convergence of the proposed algorithm is analyzed under some mild assumptions, in which the sequence of the generated solutions converges locally to a Karush-Kuhn-Tucker solution at a linear rate when the penalty parameter is less than a threshold and the error bound of the solutions is also obtained. Finally, the detailed numerical results for several typical testproblems are given in order to show the performance of the proposed algorithm.     

求解约束优化问题的一个对偶算法

１．引言 考虑下述形式的不等式约束优化问题：其中 ＝０,１,…,ｍ,是连续可微函数．求解（１．１）的数值方法有很多,传统方法有乘子法,序列一次规划方法,等等（见 Ｂｅｒｔｓｅｋａｓ（１９８２）, Ｈａｎ（１９７６, １９７７））．近年来对求解（１．１）的原始－对偶算法的研究已成为非线性规划领域的新的热点,如ＥＩ－Ｂａｋｒｙ,Ｔａｐｉａ,Ｔｓｕｃｈｉｙａ　＆　Ｚｈａｎｇ（１９９６）,Ｙａｍａｓｈｉｔａ（１９９２,１９９６,１９９７）等;尽管这些原始－对偶算法具有好的收敛性质和计算效果,但其算法结构相对…     

Feasible generalized monotone line search SQP algorithm for nonlinear minimax problems with inequality constraints

In this paper, the nonlinear minimax problems with inequality constraints are discussed, and a sequential quadratic programming (SQP) algorithm with a generalized monotone line search is presented. At each iteration, a feasible direction of descent is obtained by solving a quadratic programming (QP). To avoid the Maratos effect, a high order correction direction is achieved by solving another QP. As a result, the proposed algorithm has global and superlinear convergence. Especially, the global convergence is obtained under a weak Mangasarian–Fromovitz constraint qualification (MFCQ) instead of the linearly independent constraint qualification (LICQ). At last, its numerical effectiveness is demonstrated with test examples.     

A sequential quadratically constrained quadratic programming method for unconstrained minimax problems

In this paper, a sequential quadratically constrained quadratic programming (SQCQP) method for unconstrained minimax problems is presented. At each iteration the SQCQP method solves a subproblem that involves convex quadratic inequality constraints and a convex quadratic objective function. The global convergence of the method is obtained under much weaker conditions without any constraint qualification. Under reasonable assumptions, we prove the strong convergence, superlinearly and quadratic convergence rate.     

On the accurate identification of active set for constrained minimax problems

In this paper, the problem of identifying the active constraints for constrained nonlinear programming and minimax problems at an isolated local solution is discussed. The correct identification of active constraints can improve the local convergence behavior of algorithms and considerably simplify algorithms for inequality constrained problems, so it is a useful adjunct to nonlinear optimization algorithms. Facchinei et al. [F. Facchinei, A. Fischer, C. Kanzow, On the accurate identification of active constraints, SIAM J. Optim. 9 (1998) 14-32] introduced an effective technique which can identify the active set in a neighborhood of a solution for nonlinear programming. In this paper, we first improve this conclusion to be more suitable for infeasible algorithms such as the strongly sub-feasible direction method and the penalty function method. Then, we present the identification technique of active constraints for constrained minimax problems without strict complementarity and linear independence. Some numerical results illustrating the identification technique are reported.     

Relaxation-based algorithms for minimax optimization problems with resource allocation applications

We consider minimax optimization problems where each term in the objective function is a continuous, strictly decreasing function of a single variable and the constraints are linear. We develop relaxation-based algorithms to solve such problems. At each iteration, a relaxed minimax problem is solved, providing either an optimal solution or a better lower bound. We develop a general methodology for such relaxation schemes for the minimax optimization problem. The feasibility tests and formulation of subsequent relaxed problems can be done by using Phase I of the Simplex method and the Farkas multipliers provided by the final Simplex tableau when the corresponding problem is infeasible. Such relaxation-based algorithms are particularly attractive when the minimax optimization problem exhibits additional structure. We explore special structures for which the relaxed problem is formulated as a minimax problem with knapsack type constraints; efficient algorithms exist to solve such problems. The relaxation schemes are also adapted to solve certain resource allocation problems with substitutable resources. There, instead of Phase I of the Simplex method, a max-flow algorithm is used to test feasibility and formulate new relaxed problems.Corresponding author.Work was partially done while visiting AT&T Bell Laboratories.     

A SSLE-Type Algorithm of Quasi-Strongly Sub-Feasible Directions for Inequality Constrained Minimax Problems

In this paper, we discuss the nonlinear minimax problems with inequality constraints. Based on the stationary conditions of the discussed problems, we propose a sequential systems of linear equations (SSLE)-type algorithm of quasi-strongly sub-feasible directions with an arbitrary initial iteration point. By means of the new working set, we develop a new technique for constructing the sub-matrix in the lower right corner of the coefficient matrix of the system of linear equations (SLE). At each iteration, two systems of linear equations (SLEs) with the same uniformly nonsingular coefficient matrix are solved. Under mild conditions, the proposed algorithm possesses global and strong convergence. Finally, some preliminary numerical experiments are reported.     

Convergence of an augmented Lagrange algorithm for nonlinear optimizations with second-order cone constraints

An augmented Lagrange algorithm for nonlinear optimizations with second-order cone constraints is proposed based on a Löwner operator associated with a potential function for the optimization problems with inequality constraints. The favorable properties of both the Löwner operator and the corresponding augmented Lagrangian are discussed. And under some mild assumptions, the rate of convergence of the augmented Lagrange algorithm is studied in detail.