以优化后的预处理正交极小化算法为基础 ，对多层二维二相全隐式模拟模型进行了以层为单位的粗粒度并行任务划分 。
At the base of optimized ORTHOMIN method, the two dimension two phase full-implicit model with muti-layers has been paralleled as coarse granularity.
本文讨论了跨期套利交易的价差风险极小化模型、方法和算法。
In this paper the models, methods and algorithm of minimum risk of price difference in the derivative security market are discussed.
传统的模糊c -均值(FCM)聚类是一种基于梯度下降的优化算法，该方法对初始化较敏感，且易陷入局部极小。
The traditional fuzzy C-means (FCM) algorithm is an optimization algorithm based on gradient descending. it is sensitive to the initial condition and liable to be trapped in a local minimum.
该文首先给出了求在线性约束下极小化有限个阶梯函数和的一种分枝定界算法。
This paper first presents a branch-and-bound algorithm to minimize a finite sum of stair-case functions under linear constraints.
在一般情况下，对目标函数为极小化完工时间平方和与极小化总误工数问题分别给出了最优算法。
For the general case, optimal algorithms are presented respectively for minimizing the sum of quadratic completion times and the number of tardy jobs.
不恰当的初始化会造成算法收敛到局域极小值。
Improper initialization will lead the algorithms converge to local minimum.
在新的控制条件下，证明了二次型极小化问题的迭代算法的有效性，所得结果改进了徐洪坤关于二次型优化的最新结果。
Under new control conditions, we prove convergence of the quadratic minimization problem, which improves the recent results by Xu about quadratic optimization.
进一步，在问题有解时，通过极小化增益矩阵元素绝对值的和，给出了求解期望低成本输出反馈控制的算法。
Then following the existing path-following method for solving BMI problem, an iterative LMI algorithm is proposed to locally search the desired output-feedback gain.
本文基于多值逻辑函数极小化提出一种正例学习问题，并对这一正例学习问题给出一个启发式学习算法。
This paper presents a learning problem from positive examples based on multiple valued minimization paradigm. A new heuristic algorithm for the problem is given.
同现存的极小化方法相比，SWT算法简单，极小化程度高，并可适用于十五个以上输入变元的较复杂问题。
Compared with the existing minimization methods, SWT is the simplest, optimal method and applicable to complicated problems with more than 15 input variables.
同现存的极小化方法相比，SWT算法简单，极小化程度高，并可适用于十五个以上输入变元的较复杂问题。
Compared with the existing minimization methods, SWT is the simplest, optimal method and applicable to complicated problems with more than 15 input variables.
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