conjugate direction method
英 [ˈkɒndʒəɡeɪt dəˈrekʃn ˈmeθəd]
美 [ˈkɑːndʒəɡeɪt dəˈrekʃn ˈmeθəd]
网络 共轭方向法
双语例句
- The conjugate direction method is one between the gradient method and Newton methods.
共轭梯度法是最典型的共轭方向法。 - The path is defined as linear combination of a sequence of conjugate directions which are obtained by applying conjugate direction method to the approximate quadratic function in the null space.
将共轭方向法应用于零空间中的近似二次模型，得到一组共轭方向序列，共轭方向序列生成了共轭梯度路径。 - An efficient conjugate direction method for unconstrained function minimization
一种有效的求解无约束优化问题的共轭方向法 - The complete physical model and mathematical expressions were developed for axial or radial turbine optimum design. By using the SUMT method, the extreme value problem with inequality constraint conditions was turned into optimum design problem without constraints, and was calculated by DFP conjugate direction method.
对径、轴流涡轮的最优化设计命题建立了完整的物理模型和数学表达式，采用SUMT外点法将含有不等式约束条件极值问题处理成无约束最优化设计问题，并用DFP共轭方向法求解。 - Three optimization methods, namely, Hooke-Jeeves modular vector search method, Powell conjugate direction search method, and random direction search method, have been chosen and improved. And three programs are given.
本文选用及改进了Hooke-Jeeves的模矢搜索法、Powell的共轭方向法及随机方向搜索法，相应编制了3个优化设计程序。 - A novel conjugate direction method for optimization is proposed Using it in the quadratic programming with equality constraints, an algorithm different from Wolfe's is developed.
本文给出求解极小化问题的一类共轭方向算法，将此算法用于等式约束二次规划（QEP），得到不同于Wolfe算法的另一种求解QEP方法。 - In this paper, a method for solving unconstrained optimization problems, which is called "proper conjugate direction method", is developed.
本文提出了一种求解无约束最优化问题的方法，称之为“恰当共轭方向法”。 - An Unconstrained Optimization Technique& Proper Conjugate Direction Method
一种无约束最优化方法&恰当共轭方向法 - The conjugate direction method with pattern search in subspace
带有子空间模式搜索的共轭方向法 - Based on the restart conjugate gradient method by Powell, the current point's Newton direction has been con-structed according to the information of second derivative in the course of conjugate-gradient calculation, which produces Conjugate-Gradient-and-Newton Hybrid ( CGNH) method.
在Powell重启动共轭梯度法基础上，利用共轭迭代过程产生的二阶导数信息，构造出当前点的牛顿方向，从而得出一类快速共轭梯度法。 - Variable metric conjugate direction method for linear constrained optimization problem
线性约束下的变尺度共轭方向法 - The traditional gradient based optimization methods as steepest descent method, Newton method and conjugate direction method build on rigorous mathematical foundation, high computational efficiency, reliable procedures and have been widely used in various fields for many years.
最速下降法、牛顿法和共轭方向法等基于梯度的优化算法具有完善的数学基础，具有计算效率高、可靠性强和比较成熟等特点，是一类具有代表性且广泛应用的优化算法。 - Conjugate direction method only needs to use the information of the first derivative, but it overcomes the shortcoming of the steepest descent method in the slow convergence and avoids the defects of Newton method in storaging and computing the second derivative.
它仅需要利用一阶导数信息，既克服了最速下降法收敛慢的缺点，又避免了存储和计算Newton法所需要的二阶导数信息。 - Conjugate direction method comes from the study of the minimization problem of the quadratic function, but it can be extended to deal with the minimization problem of non-quadratic function.
共轭方向法是从研究二次函数的极小化产生的，但是它可以推广到处理非二次函数的极小化问题。