The filled function method is an approach for solving unconstrained global minimization problem.
填充函数法是一种解无约束全局极小化问题的方法。
In this paper, a bundle method for the minimization of the convex nonsmooth function in point by point maximum is suggested.
研究了用束方法求解非光滑逐点最大凸函数的极小化问题。
The energy equation is solved by the zonal method, and the inverse radiation problem is solved through the minimization of performance function with the conjugated gradient method.
采用段法(区域法)求解正问题，反演中采用求目标函数极小值的共轭梯度法。
A method of knowledge expression on based rough set theory for logic function is proposed. A minimization algorithm of simplifying logic function by using rough set is given.
提出了基于粗糙集理论对逻辑函数进行知识表达的方法，给出了运用粗糙集进行组合逻辑化简的优化算法。
Moreover, this method also possesses an automatically restarting function, which makes it particularly effective for the minimization of a non-quadratic function.
尤其重要的是：本算法具有自动进行再启动的功能，使得这个算法对于非二次函数的最小值问题特别有效。
The properties of this filled function and a global minimization method based on the filled function are analyzed.
给出一类求解总体极值问题的填充函数，分析了该填充函数的特性与基于填充函数的总体极小化方法。
The objective function was defined as minimization between strains obtained from finite element method and ones observed from three-axial compression test in laboratory.
以三维有限元模拟的应变值与三轴压缩实验观测应变数据残差平方和最小为参数反演的目标函数。
The objective function was defined as minimization between strains obtained from finite element method and ones observed from three-axial compression test in laboratory.
以三维有限元模拟的应变值与三轴压缩实验观测应变数据残差平方和最小为参数反演的目标函数。
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