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convex programming相关的网络例句

查询词典 convex programming

与 convex programming 相关的网络例句 [注:此内容来源于网络,仅供参考]

With the Ben Tal generalized algebraic operation of 2 , we obtained some necessary and sufficient conditions for the class of generalized convex programming, and the results generalize the corresponding ones for the convex Programming.

利用文〔2〕中定义的Ben-Tal广义代数运算,本文得到了这类广义凸规划的Kuhn-Tucker充分条件和必要条件,所得结果推广了通常凸规划相应的结

Conic optimization is a particular case of convex programming, and it is also an extension of linear programming.

锥规划(conic optimization,简称CO)是一种特殊的凸规划,是线性规划的推广。

In this paper, based on the conjugate function and the dual programming of convex programming, with the dual cone, to conic programming, the dual programming is fully discussed, the expression of the dual programming is educed, the main dualities are proved, and the dualities of the familiar conic programming are studied.

以共轭函数和凸规划的对偶规划为基础,利用对偶锥的概念,全面讨论了一般锥规划的对偶问题,严格推导出锥规划对偶规划的表示形式,给出了锥规划的主要对偶性质,并用这些结果研究了常见锥规划的对偶性。

Consider the convex programming problems min, s. t. g≤0(i=1, 2,…, m), where x∈R and g: R→R are twice continuously differentiable convex function.

对于凸规划问题min,s.t.g≤0(i=1, 2,…, m),其中,x∈R;f,g:R→R为二次连续可微凸函数。

There are a lot of conclusions about properties of convex set and convex function, which have full application in convex programming.

相应于凸规划的凸集和凸函数的性质已有很多结论,并且在凸规划的研究中得到了充分应用。

This paper is a discussion of new algorithm for convex objective function and convex programming with constraints.The algorithm is an extension and improvement for a new linear programming.

对一般凸目标函数和一般凸集约束的凸规划问题新解法进行探讨,它是线性规划一种新算法的扩展和改进。

In fact, the former only consider the strict convex programming, while the latter considers the convex programming.

支持向量机中有一类重要的变形方法,虽然很有效,但缺乏相应的统计学习理论基础。

Convex programming studies the case when the objective function is convex and the constraints, if any, form a convex set.

二次规划的目标函数可以有二次条件,而设置必须指定与线性等式和不等式。

To solve the optimization problems, apply the DCA (difference of convex functions algori-thms) which is a general framework for nonconvex continuous optimization. This leads to a successive linear programming algorithm with finite convergence.

为解决优化问题,应用了突函数差异算法(difference of convex functions algorithms,DCA)这一进行非突连续优化的通用框架,致使连续线性规划算法有限收敛。

Optimization; parametric quadratic convex programming; set-valued map; directional derivative; linear stability; solution-set map; parametric linear programming; error bound; subdifferential map; lower locally directionally Lipschitzian; upper locally di-rectionally Lipschitzian; locally directionally Lipschitzian; convex function; quasidiferential; kernelled quasidiferential; quasi-kernel; star-kernel; star-diferential; Penot diferential; subderivative; superderivative; epiderivative; set-valued optimization; set-valued analysis; subdifferential; optimization condition;ε-dual; scalization; generalized subconvexlike-cone;ε-Lagrange multiplier

基础科学,数学,运筹学最优化;集值映射;方向导数;线性稳定;最优解集映射;参数线性规划;参数凸二次规划;误差界;次微分映射;下局部方向Lipschitzian;上局部方向Lipschitzian;局部方向Lipschitzian;凸函数;拟微分;核拟微分;拟核;星核;星微分; Penot-微分;上导数;下导数; Epi-导数;集值优化;集值分析;集值映射的次微分;最优性条件;广义锥次类凸;ε-对偶;数乘;ε-Lagrange乘子

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