可行点

To make our approach to be practical, we also discuss the asymptotic expansion of the operator in chapter 6 which is based on the non-abelian Stokes theorem.

但是由于格点上的算符是path-order变量的乘积，并且其构成非常复杂，按照正常的方式展开实际上是不可能的，为了使我们的算法实际可行，本文接着讨论了格点上算符的渐近展开问题，并且给出了基于non-abelStokes定理的方法，这种方法克服了以前各种办法的缺点，并且简单可行，适用于任意的Wilson算符。

This algorithm is proposed on the basis of the thought as follovvs: as for three dimensional Euclid space, the feasible region of any linear programming problem is a extended convex polyhedron, of which surface is consisted of some planes, and its objective function can be regarded as a parallel plane pencil with objective function value acting as parameter.

如果线性规划问题有最优解，那么过可行域的一已知顶点必至少存在这样一条棱——它以该己知顶点为一端点，可行域的另一顶点为另一端点，并使目标函数在另一端点的函数值优于己知端点的函数值，否则，该己知点就是线性规划问题的最优解。

Both low memory size and small-scale computation are showed in the new algorithm, on the other hand, a new pivot rule, which is an exterior point method, also makes the new algorithm more efficient.

该算法以核心矩阵为运算单元，一方面呈现了存储空间小，计算量小的特点；另一方面，该算法采用了一种新的转轴规则的外点算法，在保持原始可行的基础上，不断改善对偶解使其可行。

The initial pointmay not a feasible point.

另外，用该法不要求初始设计点是可行点给工程设计带来极大的方便。

In order to overcome the deficiency that initial points should be strictly feasible in interior point methods, a primal-dual infeasible-interior-point algorithm for the second-order cone programming is presented.

为了克服内点算法中初始点是严格可行的这一缺点，给出二次锥规划的一种原-对偶不可行内点算法。

In Chapter 4,adopting a new skill of combiningSQP idea with the strongly convergent algorithm obtainned in Chapter 3,we present a feasible SQPalgorithm whose initial point and iterative point all are fasible.

第四章采用SQP思想与第三章的强收敛算法相结合的新技巧，建立了初始点及迭代点均可行的可行SQP算法。

Through calculating the fundus of the null space of matrix A in the constraint,there exists at least one feasible descent direction at every nonstationary point and every iterate generated by the methods is feasible so long as the initial iterate is feasible.

通过计算约束条件中矩阵A的零空间的基底，使得在每一个非稳定点处至少存在一个可行的下降方向，并且在初始点是可行点的条件下，该算法产生的每一个迭代点都是可行点。

However, various algorithms of MFD have a common shortcoming, that is, the initial iteration point must be feasible, so an auxiliary procedure must be considered for finding an initial feasible point, i.e., a system of nonlinear inequalities must be solved before using the method of feasible directions, which is not easy to be finished in generally, especially for large scale problems.

然而，各类可行方向算法有一共同的缺点，那就是初始迭代点必须可行，从而需要一个辅助程序来寻找一个初始可行点，即在运用可行方向法之前先要求解一非线性不等式组，而这个问题通常不易解决，特别是对于大规模问题而言。

Embedding the gradient direction search and new kinds of evaluation functions into the Genetic Algorithm, a special hybrid Genetic Algorithm with mutation along the weighted gradient direction to solve non-linear programming problems is developed. Then in Chapt 4, a type of nonlinear programming problems with fuzzy objective and fuzzy resources constraints is discussed, in which the objective is ill-defined and the resources possess two different types of fuzzy resources, i. e., tolerance and imprecision. By way of description and formulation of fuzzy objective and fuzzy resources, the problem with fuzzy objective and two different types of fuzzy resources is modelled into a fuzzy nonlinear programming model FO/RNLP and interpreted as three equivalent alternative crisp model to reflect the variety of the environment and the subjective and preference of the decision maker .

本文首先针对遗传算法在求解非线性规划问题中存在的问题，首次提出非可行点／染色体的主导准可行方向，准可行方向，及非可行点属于可行域的可行度等新概念，借助于模糊思想，提出了描述和度量非可行染色体的新方法；通过嵌入非可行染色体的信息于评价函数中，提出了三种新的评价非可行染色体的评价函数；把传统的启发式方法-梯度方向搜索和新的评价函数嵌入到遗传算法中，提出了一种沿加权梯度方向变异的新型混合式遗传算法；第四章针对生产实际中非清晰定义的模糊目标和两种类型的模糊资源，建立了一类连续型模糊目标／资源约束非线性规划问题的模型FO／RNLP。

There exits several MDO algorithms. But they are in our opinion either nonefficient or complicated. So we devised an algorithm called Subspace Approximation Optimization . In the SAO algorithm, the whole system is decomposed into one system-level optimization and several disciplinary optimizations so that a large and complicated problem can be divided into several easy-solving sub-problems. The coupling relationships and the coordination among disciplines are presented by equality constraints and these equality constraints are assigned to relevant disciplines. The optimums of design variables in system level optimization are transferred to discipline level optimization. The optimums of design variables in discipline level correspond to the point that is the nearest to the optimums of design variables in system level. If the optimums of design variables in system level are out of feasible region of discipline 1eve1, linear constraints can be built in the system level optimization using the design variable optimums obtained by the discipline level optimization. The system level optimization would improve the design of the whole system with these linear constraints.

目前，国内外已经发展出了多种飞机多学科设计优化算法，本文的重点是针对协同优化算法的不足，提出了子空间近似优化算法(Subspace ApproximatingOptimization，SAO)，SAO算法中，整个系统的优化问题被分解成一个系统级优化和若干学科级优化，而各个学科之间的耦合与权衡关系则被当作等式约束，这些等式约束将被分配到各个学科级优化中去，系统级优化的任务是寻找整个系统的最优解，而学科级优化的目标函数是以系统级优化分配下来的设计点为圆心的超球半径的平方，因此，如果系统级优化分配下来的设计点在学科级优化可行域内，则学科级优化目标函数为0，反之，则学科级优化的最优点是系统级优化当前设计点距离可行域最近的点。

If you are unfortunate enough to the lovelorn, please tell me, I will help you out, really, please contact me!

如果你不幸失恋了，请告诉我，我会帮助你摆脱困境，真的，请联系我啦！

China's plan to cut energy intensity by 20 percent and pollutant discharges by 10 percent between 2006 and 2010 is a case in point.

中国计划在2006年到2010间降低20%的能源强度和减少10%的主要污染物排放，就是一个这样的例子。