线性约束最优化的一个共轭投影梯度法

本结合共轭梯度法及梯度投影法的思想,建立线性等式约束最优化的一个新算法,称之为共轭投影梯度法。分别对二次凸目标函数和一般目标函数分析和论证了算法的重要性质和收敛性。     

一个线性约束优化问题的广义梯度投影法

本文对线性约束优化问题提出了一个新的广义梯度投影法，该算法采用了非精确线性搜索，并在每次迭代运算中结合了广义投影矩阵和变尺度方法的思想确定其搜索方向．在通常的假设条件下，证明了该算法的整体收敛性和超线性收敛速度．     

非线性最优化的广义梯度投影法

梯度投影法已有许多有效算法,但这些算法还存在三个问题:1)为了保证算法的收敛性,在算法的每一迭代步,需要选取δ-主动约束集,计算量较大.2)在迭代过程中,需要跟踪主动约束集.3)只能处理非线性不等式约束问题.本文讨论非线性等式与不等式约束的优化问题,给出了一个广义梯度投影法,证明了算法的收敛性并且完满地解决了上述三个问题.本文算法结构简单且其处理技巧有普遍意义.     

互补约束规划问题的一个广义梯度投影算法

本文研究了一类均衡约束最优化问题.利用广义梯度投影法,结合罚函数思想,得到了一个初始点可以任意的广义梯度投影算法.在较弱的条件下,证明了算法的全局收敛性.     

一个新的共轭投影梯度算法及其超线性收敛性

利用共轭投影梯度技巧，结合SQP算法的思想，建立了一个具有显示搜索方向的新算法，在适当的条件下，证明算法是全局收敛和强收敛的，且具有超线性收敛性，最后数值实验表明算法是有效的。     

使用信赖域策略的投影梯度方法解约束优化问题

本文使用信赖域策略结合投影梯度算法来解约束优化问题,并给出算法及其收敛性。进一步,给出了收敛点具有满足约束问题一阶和二阶必要性的性质。     

在一个新步长规则下梯度投影算法的全局收敛性

考虑约束最优化问题：minx∈Ωf(x)其中:f:R^n→R是连续可微函数，Ω是一闭凸集。本文研究了解决此问题的梯度投影方法，在步长的选取时采用了一种新的策略，在较弱的条件下，证明了梯度投影响方法的全局收敛性。     

一般约束优化问题的一个新广义梯度投影法

本文以l1-l∞混合罚函数作为效益函数,提出一个一般约束优化问题的新的广义梯度投影法.该算法具有以下特点:初始点可任意选取;搜索方向是效益函数的下降方向;在传统的假设条件下具有全局收敛性.论文最后通过数值试验验证了算法的有效性.     

约束优化问题的修正共轭梯度投影算法

对闭凸集约束的非线性规划问题构造了一个修正共轭梯度投影下降算法,在去掉迭代点列有界的条件下,分析了算法的全局收敛性.新算法与共轭梯度参数结合,给出了三类结合共轭梯度参数的修正共轭梯度投影算法.数值例子表明算法是有效的.     

求解界约束优化问题的有效集算法综述

主要介绍了求解界约束优化问题的有效集方法,包括投影共轭梯度法和有效集识别函数法,讨论了各自的优点和不足.最后,指出了有效集法的研究趋势及应用前景.     

New sequential quadratic programming algorithm with consistent subproblems

One of the most interesting topics related to sequential quadratic programming algorithms is how to guarantee the consistence of all quadratic programming subproblems. In this decade, much work trying to change the form of constraints to obtain the consistence of the subproblems has been done. The method proposed by De O. Pantoja J.F. A. and coworkers solves the consistent problem of SQP method, and is the best to the authors’ knowledge. However, the scale and complexity of the subproblems in De O. Pantoja’s work will be increased greatly since all equality constraints have to be changed into absolute form. A new sequential quadratic programming type algorithm is presented by means of a special ε-active set scheme and a special penalty function. Subproblems of the new algorithm are all consistent, and the form of constraints of the subproblems is as simple as one of the general SQP type algorithms. It can be proved that the new method keeps global convergence and Local superlinear convergence. Project partly supported by the National Natural Science Foundation of China.     

Two Error Bounds for Constrained Optimization Problems and Their Applications

This paper presents a global error bound for the projected gradient and a local error bound for the distance from a feasible solution to the optimal solution set of a nonlinear programming problem by using some characteristic quantities such as value function, trust region radius etc., which are appeared in the trust region method. As applications of these error bounds, we obtain sufficient conditions under which a sequence of feasible solutions converges to a stationary point or to an optimal solution, respectively, and a necessary and sufficient condition under which a sequence of feasible solutions converges to a Kuhn–Tucker point. Other applications involve finite termination of a sequence of feasible solutions. For general optimization problems, when the optimal solution set is generalized non-degenerate or gives generalized weak sharp minima, we give a necessary and sufficient condition for a sequence of feasible solutions to terminate finitely at a Kuhn–Tucker point, and a  sufficient condition which guarantees that a sequence of feasible solutions terminates finitely at a stationary point. This research was supported by the National Natural Science Foundation of China (10571106) and CityU Strategic Research Grant.     

Projected gradient trust-region method for solving nonlinear systems with convex constraints

In this paper, a projected gradient trust region algorithm for solving nonlinear equality systems with convex constraints is considered. The global convergence results are developed in a very general setting of computing trial directions by this method combining with the line search technique. Close to the solution set this method is locally Q-superlinearly convergent under an error bound assumption which is much weaker than the standard nonsingularity condition.     

求解界约束优化的一种新的非单调谱投影梯度法

本文给出求解界约束优化问题的一种新的非单调谱投影梯度算法. 该算法是将谱投影梯度算法与Zhang and Hager [SIAM Journal on Optimization,2004,4（4）：1043-1056]提出的非单调线搜索结合得到的方法. 在合理的假设条件下,证明了算法的全局收敛性.数值实验结果表明,与已有的界约束优化问题的谱投影梯度法比较,利用本文给出的算法求解界约束优化问题是有竞争力的.     

约束优化的一个改进的强次可行SQP算法及数值试验

本文对约束优化一个强次可行SQP算法进行改进,使之产生的迭代点在有限次迭代后全落入可行域;并对算法数值效果进行了大量的比较试验.     

Two-Phase Model Algorithm with Global Convergence for Nonlinear Programming

The family of feasible methods for minimization with nonlinear constraints includes the nonlinear projected gradient method, the generalized reduced gradient method (GRG), and many variants of the sequential gradient restoration algorithm (SGRA). Generally speaking, a particular iteration of any of these methods proceeds in two phases. In the restoration phase, feasibility is restored by means of the resolution of an auxiliary nonlinear problem, generally a nonlinear system of equations. In the minimization phase, optimality is improved by means of the consideration of the objective function, or its Lagrangian, on the tangent subspace to the constraints. In this paper, minimal assumptions are stated on the restoration phase and the minimization phase that ensure that the resulting algorithm is globally convergent. The key point is the possibility of comparing two successive nonfeasible iterates by means of a suitable merit function that combines feasibility and optimality. The merit function allows one to work with a high degree of infeasibility at the first iterations of the algorithm. Global convergence is proved and a particular implementation of the model algorithm is described.     

局部误差的有界约束非线性方程组的非单调信赖域算法

本文提供了在没有非奇异假设的条件下,求解有界约束半光滑方程组的投影信赖域算法.基于一个正则化子问题,求得类牛顿步,进而求得投影牛顿步.在合理的假设条件下,证明了算法不仅具有整体收敛性而且保持超线性收敛速率.     

带扰动项的FR共轭梯度法

本文提出了两种搜索方向带有扰动项的Fletcher-Reeves (abbr. FR)共轭梯度法.其迭代公式为xk 1=xk αk(sk ωk),其中sk由共轭梯度迭代公式确定,ωk为扰动项,αk采用线搜索确定而不是必须趋于零.我们在很一般的假设条件下证明了两种算法的全局收敛性,而不需要目标函数有下界或水平集有界等有界性条件.     

求解一般约束优化问题的一个全局收敛的混合不精确SQP算法

对于一般约束优化问题,本文通过一种特殊的耦合策略,把一个局邵超线性收敛的不精确SQP算法与广义梯度投影法相结合,从而给出了一个混合算法.该算法无需计算拉格朗日函数的海色矩阵,并且在适当的假设下,算法具有全局和局部超线性收敛性.     

一类Lagrangian对偶问题的零对偶间隙性质及其最优路径的收敛性

针对一般的非线性规划问题,利用某些Lagrange型函数给出了一类Lagrangian对偶问题的一般模型,并证明它与原问题之间存在零对偶间隙．针对具体的一类增广La- grangian对偶问题以及几类由非线性卷积函数构成的Lagrangian对偶问题,详细讨论了零对偶间隙的存在性．进一步,讨论了在最优路径存在的前提下,最优路径的收敛性质．