线性最优化问题

词组短语

linear optimization problem

与线性最优化问题相关的网络例句 [注：此内容来源于网络，仅供参考]

The paper considers applying generalized pattern search methods to linear equality constrained minimization and analyzes the global convergence.

本文的主要内容是对广义模式搜索算法关于线性等式约束最优化问题的全局收敛性做了一些研究。

We also construct an algorithm for minimizing its Moreau-Yosida regularization, because this is a smooth convex optimization problem.

极小化一个凸函数可以采用在UV-空间分解理论基础上提出的概念型超线性收敛算法，也可以采用极小化这一凸函数的Moreau-Yosida正则化函数的算法，因为这是一个凸规划的光滑最优化问题。

In chapter 2 we propose a linear equality constraint optimization question , the new algorithm is combined with the new conjugate gradient method(HS-DY conjugate gradient method)and Rosen"s gradient projection method , and has proven it"s convergence under the Wolfe line search.In chapter 3 we have combined a descent algorithm of constraint question with Rosen"s gradient projection, and proposed a linear equality constraint optimization question"s new algorithm, and proposed a combining algorithm about this algorithm, then we have proven their convergence under the Wolfe line search, and has performed the numerical experimentation.

在第三章中我们将无约束问题的一类下降算法与Rosen投影梯度法相结合，将其推广到线性等式约束最优化问题，提出了线性等式约束最优化问题的一类投影下降算法，并提出了基于这类算法的混合算法，在Wolfe线搜索下证明了这两类算法的收敛性，并通过数值试验验证了算法的有效性。

There are a lot of algorithms of feasible direction for the optimization problem with linear Constraints.

对线性约束最优化问题，已有许多可行方向算法，它们都建立在各自的理论基础之上，产生搜索方向的方法各不相同。

The objective function of the QP problem is a quadratic function which is an approximation of the Lagrangian function of the constrained problem and the constraints of the QP problem are linear approximation of the constraints of the constrained problem.

这些二次规划子问题的目标函数是原约束最优化问题的Lagrange函数的某种二次近似，其约束条件是原约束条件的线性逼近。

These results are also specialized to abstract linear optimization problems.

这些结果的特例是抽象的线性最优化问题。

With this method, the operator can be of stability and accuracy.

偏移延拓算子通过求解线性最优化和非线性最优化问题来获得，因此保证了算子的稳定性和精度。

We introduce convex,and in particular semidefinite,optimization methods,duality and complexity theory to shed new light to this relation for the single stock problem, given moments of the prices of the underlying assets,we show that we can find best possible bounds on option prices with general payoff funcations efficiently,either algorithmically(solving a semidefinite optimization problem)or in closed form, conversely,given observable option prices,we provide best possible bounds on moments of the prices of the underlying assets,as well as on the prices of the other options on the same asset by sovling linear optimization problems for options that are affected by multiple stocks either directly(the payoff of the option depends on multiple stocks)or indirectly(we have information on correlations between stock prices),we find on-optimal bounds using convex optimization methods,however,we show that it is NP-hard to find best possible bounds in multiple dimensions,we extend our results to incorporate transactions costs,this paper,in theory and practice can provide a reference to researchers and designers about Chinese financial derivative products,the full text is divided into six chapters as follows: ChapterⅠ:Papers on the background and significance of the subjects on a number of option pricing models as well as their advantages and shortcomings of the model and describes the status of research and writing papers and the main contents of the basic idea.

相应地，给定期权价格，也能够出标的资产瞬时价格的最有可能的最好的界。还有通过解决一个线性最优化问题，根据同一标的资产的其他期权的价格来找到这个期权的界值，对于期权受到多种股票价格直接影响(期权的收益依赖于多种股票)或者间接影响(我们有股票之间联系的有用信息)，如果使用凸规划方法我们就会发现没有最优的界，也能够证明对于多维情形确实是很难找到最优的最有可能的界值，最后将这一结论推广到考虑交易成本的情况。本文在理论和实践上给我国金融衍生产品研究者和设计者提供一定的参考。全文共分为六章，具体安排如下：第一章：阐述论文的选题背景和意义，介绍期权定价的一些模型以及这些模型的优点与缺点，并介绍国内外研究的现状以及论文的写作基本思路与主要内容。

The projection gradient method will be a possible way to solve the problem that we just get. It has been shown that the projections of the every directions, of which is the boundary point in linear restraint problems, are the possible decent directions, and the projection of negative grads direction is a decent direction. In 1960, Rosen proposed the basic idea of projection gradient methods, and then lots of researchers have been tried to find the convergence of this method. But most of them get the convergence with the condition to amend the convergence itself.

在约束最优化问题的算法中怎样寻找有效的下降方向是构造算法的重要内容，在寻找下降方向方面可行方向法中的投影梯度法有效的解决了下降方向的寻找问题，利用线性约束问题边界点的任意方向在边界上的投影都是可行方向，而负梯度方向的投影就是一个下降方向。60年代初Rosen提出投影梯度法的基本思想，自从Rosen提出该方法以后，对它的收敛性问题不少人进行了研究，但一般都是对算法作出某些修正后才能证明其收敛的，直到最近对Rosen算法本身的收敛性的证明才予以解决。

The problem of solving linear equations changed equivalently into the problem of searching the minimum of unconstrained optimizations of function by variation principle in the paper.

首先通过变分原理将求解线性方程组的问题转化为等价的求解无约束函数最优化问题的极小值。

与线性最优化问题相关的网络解释 [注：此内容来源于网络，仅供参考]

conjugate gradient method：共轭梯度法

共轭梯度法(Conjugate Gradient Method)是以共轭方向(Conjugate Direction)作为搜索方向的一类算法. 最初的共轭梯度法由Hesteness和Stiefel于1952年为求解线性方程组而提出,后来用于求解无约束最优化问题.

linear optimization problem：线性最优化问题

linear optimization 线性最优化 | linear optimization problem 线性最优化问题 | linear order 线性有序类

linear optimization：线性最优化

本章着重介绍线性最优化(linear optimization)模型的基础知识. 该模型又被称为线性规划(linear programming)模型,是进行管理类问题分析的一个十分有用的数学工具. 在此,我们并不深入研究线性最优化模型中复杂的数学理论,

linear order：线性有序类

linear optimization problem 线性最优化问题 | linear order 线性有序类 | linear orderedness 线性次序

opt：优化

决策变量、约束条件、目标函数是线性规划的三要素.线性规划问题的数学模型的一般形式 (1)列出约束条件及目标函数2、目标函数是决策变量的线性函数,根据具体问题可以是最大化(max)或最小化(min),二者统称为最优化(opt).