极小化极大

基本解释 (translations)

minimax

与极小化极大相关的网络例句 [注：此内容来源于网络，仅供参考]

When using restricted maximum likelihood for parameter estimation, we need to get the minimum of a function. But if the expression of the function is complicated, it is difficult to obtain the derivatives of the function. We introduced conjugate direction method which avoided the derivatives.

在用限制极大似然法估计参数时，需解决函数的极小化问题，而当函数的表达式较为复杂时，将难以获得其偏导数，本文给出的共轭方向法，避开了求导问题。

Third,argument-polar radius curve of polar coordinate system whose pole is geometric gravity is formed,and maximum and minimum value points are searched.

然后以重心为极点，对边缘点极坐标化，形成幅角-极径曲线，在该曲线上寻找局部极大极小值点。

In Chapter 6, based on discretization technique an implementable algorithm for nonconvex generalized semi-infinite minimax problems is presented and, utilizing properties of generalized quasi-directional derivative, its global convergence is proven under weak conditions.

对于广义极大极小问题，本文第六章在较弱的条件下，利用广义伪方向导数的性质，用离散化的技巧给出了非凸广义半无限极大极小问题的一种可实现的全局收敛算法。

This book reviews the many areas of numerical analysis, including the configuration polynomial, finite difference, factorial polynomials, summation, Newton formula, operator and configuration polynomial, Cheung section, close polynomials, TaylM more item type, interpolation, numerical differentiation, numerical integration, and with the series, differential equations, differential equations, least squares polynomial approximation, minimax polynomial approximation, rational function approximation, triangular approximation, non-linear algebra, linear equations, linear programming, boundary value problems, MonteCarIo methods and so on.

本书综述了数值分析领域的诸多内容，包括配置多项式、有限差分、阶乘多项式、求和法、Newton公式、算子与配置多项式、祥条、密切多项式、TaylM多项式、插值、数值微分、数值积分、和与级数、差分方程、微分方程、最小二乘多项式逼近、极小化极大多项式逼近、有理函数逼近、三角逼近、非线性代数、线性方程组、线性规划、边值问题、MonteCarIo方法等内容。本书的特色主要表现在利用例题及大量详细的题解来透彻地阐明所述内容的内涵，同时附有大量的补充题以便读者进一步巩固和深化从书中获得的数值分析知识。

The abstract result contains several concrete results in the literature and can also be used to deal with some new cases for resonant differential equations.In the introduction, we briefly introduce the development process of the variational methods. In Chapter 2, we list some basic knowledges refering to the variational methods, including the Sobobev space,—△ operator, the weak solution and the minimizing sequence methods and some minimax theorems. In Chapter 3, we introduce the research process of Hamiltonian system of second order and the semilinear elliptic problems, using the methods introduced previously. In Chapter 4, we prove the main theorem of the thesis, and apply it to the problems in the previous Chapter, and can also be applied to some new resonant cases.

在前言中，简要介绍了变分法的产生、发展过程，在第二章中我们介绍了有关变分法的一些基本知识，包括Sobolev空间，—△算子，弱解，极小化序列方法和一些极小极大定理，在第三章中我们介绍了非线性项有界或满足次线性条件，以及它满足推广的Ahmad-Lazer-Paul条件时，二阶Hamiltonian系统和半线性椭圆问题的研究历程，最后在第四章中我们证明了本论文的主要定理，并把它应用到第三章的问题中，使得前面的几种共振的情形都可以统一到这个抽象的结果中。

To obtain quadratical convergence, however, strict complementarity condition at the Danskin point was used. The condition is too strict to be satisfied in many practical problems such as discrete semi-infinite minimax problem. Another kind of Newton method for finite minimax problems was presented by E. Polak and, without strict complementarity at the Danskin point, superlinear convergence (of order 3/2) was proven.

Polak等人提出了一种直接求解极大极小问题的二阶收敛的牛顿法，但是为获得二阶收敛速度要求在Danskin点处满足严格互补条件，这个条件太强，很多实际问题尤其是半无限极大极小问题的离散化不满足该条件；他们又给出另外一种牛顿法，在不假设严格互补条件成立的情况下，证明了它的超线性（3/2阶）收敛性。

Quasi Hamiltonian system; nonlinear stochastic optimal control; robustness; robust control; parametric uncertainty; uncertain disturbance; Bouc-Wen hysteretic system; Preisach hysteretic system; minimax optimal control; stochastic stabilization; stochastic averaging method; stochastic dynamical programming principle; stochastic differential game; maximal Lyapunov exponent

国家自然科学基金；拟Hamilton系统；非线性随机最优控制；鲁棒性；鲁棒控制；参数不确定性；不确定扰动； Bouc-Wen滞迟系统； Preisach滞迟系统；极小极大最优控制；随机稳定化；随机平均法；随机动态规划原理；随机微分对策；最大Lyapunov指数

After giving the definition of copy method, we present two impor-tant lemmata and discuss the NP-completeness of the above two minsum and minmax problems.

文中在给出了复制法的定义后，又给出两个重要的引理，接下来主要讨论了上述两极小化求和及极小化极大分批排序问题的NP-完备性。

与极小化极大相关的网络解释 [注：此内容来源于网络，仅供参考]

minimax decision function：极小化极大决策函数

极小化极大解|minimax solution | 极小化极大决策函数|minimax decision function | 极小化极大序贯决策函数|minimax sequential decision function

minimax sequential decision function：极小化极大序贯决策函数

极小化极大决策函数|minimax decision function | 极小化极大序贯决策函数|minimax sequential decision function | 极小化极大原理|minimax principle

minimax principle：极小化极大原理

极小化极大序贯决策函数|minimax sequential decision function | 极小化极大原理|minimax principle | 极小化模型|minimization model

principle of minimax：极小化极大原

principle of maximum 最大值原 ,极大值原 | principle of minimax 极小化极大原 | principle of minimum 极小值原

minimax solution：极小化极大解

极小化极大估计|minimax estimate | 极小化极大解|minimax solution | 极小化极大决策函数|minimax decision function

minimax strategy：极小化极大策略

minimax solution 极小极大解 | minimax strategy 极小化极大策略 | minimax test 极小极大检验

minimax theorem：极小极大定理

minimax strategy 极小极大策略 | minimax theorem 极小极大定理 | minimization 极小化

minimax theorem：极小化极大定理

minimax test 极小极大检验 | minimax theorem 极小化极大定理 | minimax 极大极小

minimax：极小化极大

总风险与P(w1)成线性关系,如果固定边界,改变P(w1),则风险R会在P(w1)=0或者1的地方达到最大值. 要使这个最大值最小,则我们可以找到一个边界使比列常量为0,那么风险将与先验概率P(w1)相独立. 以上就是极小化极大(minimax)求解.

minimax search：极小化极大搜索

quadratically integrable function 二次判别函数 | minimax search 极小化极大搜索 | main throttle valve 主汽门 主汽阀