共轭方向法

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。

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 .

线性约束下的变尺度共轭方向法

Variable metric conjugate direction method for linear constrained optimization problem

通过运用Runge-Kutta法对模型进行参数求解，并运用非线性最优化Powell共轭方向法对模型参数进行优化，确立了模型的最优参数组合。

By methods of Runge-Kutta and no-linear Powell to gain and optimize parameters , the best combination was obtained .

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。

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 .

一个新的共轭方向法与拟牛顿法的结合

A Combined New Conjugate-Gradient with Quasi-Newt on Algorithm for Nonlinear Optimization

一种共轭方向法的内含及扼要论证

The Intension and a Briey Proof of a Conjugation Direction Method

带有子空间模式搜索的共轭方向法

The conjugate direction method with pattern search in subspace

一种有效的求解无约束优化问题的共轭方向法

An efficient conjugate direction method for unconstrained function minimization

一种无约束最优化方法&恰当共轭方向法

An Unconstrained Optimization Technique & Proper Conjugate Direction Method

无限制最适化方法，包括梯度法、共轭方向法、牛顿法和拟牛顿法。

Unconstrained optimization methods include gradient , conjugate direction , Newton , and quasi-Newton methods .

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”。

In this paper , a method for solving unconstrained optimization problems , which is called " proper conjugate direction method ", is developed .

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。

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 .

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。

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 .

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。

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 .

基于最优化理论的共轭方向加速法，研究了地铁隧道施工引起地面沉降的Peck法与随机介质法各自的多参数反分析方法；

An optimization theory of Powell 's conjugation directional method was employed in subsidence back-analysis prediction for stochastic medium model and Peck volume loss model .

共轭梯度法是最典型的共轭方向法。

The conjugate direction method is one between the gradient method and Newton methods .