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严格凸函数

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Properties of efficient portfolios and the efficient frontier to the model are systematically analyzed. Our main results concerning the properties are: every efficient portfolio can be solved by minimizing portfolio risk under a given level of portfolio return or by maximizing portfolio return under a given level of portfolio risk; on the efficient frontier, the risk is a convex and strictly increasing function of the return and the return is a concave and strictly increasing function of the risk; the utility function on the efficient frontier can be expressed as a quasi-concave function of the risk or the return if the investor's utility function is quasi-concave.

从理论上系统地对该模型下的有效投资组合和有效前沿的性质进行了分析,结果表明:每一个有效投资组合可通过在给定期望收益水平的条件下最小化投资组合风险来获得,或者在给定风险水平的条件下最大化期望投资组合收益来获得;在有效前沿上,风险是收益的严格单调递增凸函数,收益是风险的严格单调递增凹函数;当投资者的效用函数是拟凹函数时,则有效前沿上的效用可表达成风险或收益的拟凹函数。

Some properties of semistrictly strongly preinvex function are derived.

给出了半严格强预不变凸函数的一些性质。

Furthermor, we consider their nondiferentiable situation, we define nonsmooth univex functions for Lipschitz functions by using Clarke generalized directional derivative and study nonsmooth multiobjective fractional programming with the new convexity. We establish generalized Karush-Kuhn-Tucker necessary and sufficient optimality condition and prove weak, strong and strict converse duality theorems for nonsmooth multiobjective fractional programming problems containing univex functions.

而且,本文利用Clarke广义方向导数针对Lipschitz函数在原来一致凸函数概念的基础上定义了不可微的一致凸函数,并利用这类新凸性,我们研究了非光滑多目标分式规划,获得了广义Karush-Kuhn-Tucher最优性条件;弱对偶定理、强对偶定理和严格逆对偶定理。

We define I-quasi-invex vector function ., I-strictly quasi-invex vector function and KT-I-strictly quasi invex vector function, and derive the above equivalent condition for unconstrained or constrained multiobjective programming.

于是,在本文的第三部分,我们定义了Ⅰ类不变拟凸、Ⅰ类严格不变拟凸、KT-Ⅰ类严格不变拟凸的向量值函数,并且在无约束或约束多目标规划中,获得了每个驻点是有效解的等价条件。

In chapter 2, we give the criteria of extreme point in Musielak-Orlicz function space. Meanwhile we get necessity and sufficient condition for that Musielak-Orlicz function space are rotundity as corollaries.

第2章得到赋Orlicz范数Musielak-Orlicz函数空间的点作为端点的充要条件,并借助此条件得出赋Orlicz范数Musielak-Orlicz函数空间严格凸的等价条件。

On the other hand, uniqueness of integral solution and its regularity are obtained by means of Lipschitz conditions, properties of absolute continuous mappings, reflexivity and strict convexity of Banach spaces.

借助Lipschitz条件、绝对连续函数的性质及Banach空间的自反严格凸性,获其积分解的唯一性且是强解。

Consequently, when applied to minimize a strictly convex quadratic function, the proposed methods terminate at the solution of the problem finitely.

因此,当目标函数是严格凸的二次函数,且采用精确线性搜索时,这些修正的共轭梯度法具有共轭性和二次终止性。

Moreover, we get the sufficient and necessary condition of in Orlicz spaces.Chapter 3 Extreme points and strongly extreme points in Orlicz spaces equipped with the generalized Orlicz norm: In this paper, the conceptions of the generalized Orlicz norm and the generalized Luxemburg norm are introduced, and the criteria of extreme points and strongly extreme points of Orlicz function spaces equipped with the generalized Orlicz norm are obtained. Moreover, criteria of space strictly convex and mid-point locally uniform convex are given.

第三章 赋广义Orlicz范数的Orlicz函数空间的端点和强端点:本章在Orlicz空间推广了Orlicz范数和Luxemburg范数,引入了广义Orlicz范数和广义Luxemburg范数的定义,并给出了赋广义Orlicz范数的Orlicz函数空间的端点和强端点的判据,进而得到了赋广义Orlicz范数的Orlicz函数空间严格凸和中点局部一致凸的充要条件。

The method attempts to transform the original problem into a series of unconstraint optimization problems.

在这些研究中,均假定在对数障碍函数是严格凸的及解集是非空有界的情况下,证明了方法的全局收敛性。

The new algorithm uses the particle aggregation quality to judge that if the particles in the swarm are congregative so much,then apply the differential evolution to mutate the self prevenient best position of each particle,in order to realize the aim of preserving the varieties of the swarm.

该算法对于单调函数、严格凸或单峰函数,能在初始时很快向最优值行进,但在最优值附近收敛较慢。对于多峰函数,则更容易出现所谓&早熟&现象,即局部收敛[3]。粒子群算法出现&早熟&现象的主要原因是缺乏种群多样性。

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My dream is to be a crazy growing tree and extend at the edge between the city and the forest.

此刻,也许正是在通往天国的路上,我体验着这白色的晕旋。

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单击"保存"会将文件保存到主持人的硬盘或服务器上,而不是您自己的计算机上。