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factorization相关的网络例句

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与 factorization 相关的网络例句 [注:此内容来源于网络,仅供参考]

This paper introduced a dollar polynomial divisible in nature, the nature of the greatest common divisor, factorization properties, factorization and its applications in nature.

本论文介绍一元多项式的整除性质,最大公因式的性质,因式分解性质、因式分解性质及其应用。

This paper presents a new scheme based on Nonnegative Matrix Factorization and simplex-based method to solve this problem.

本文提出一种新的基于非负矩阵因式分解(Nonnegative Matrix Factorization,NMF)算法和单形方法相结合的混合像元分解方法去解决上述问题。

At last, we briefly compare the QCD factorization to the"generalized factorization"and PQCD method.

最后,我们简要比较了QCD因子化和"推广的因子化"及PQCD方法之间的差别。

Kanade [1] in 1992 proposed a factorization method which can effectively avoid noise interference, while not restricted to a particular motion model, such as pure translation or rotation .

Kanade[1]于1992年提出了一种Factorization的方法,可以有效地避免噪声的干扰,同时不会受限于特定的运动模型,例如单纯的平移或是旋转。

A efficient LU decomposition method is spectral factorization. That is, after the minimum phase correlative function of each array of matrix had be given by a spectral factorization method, all of the functions are arranged in a position according to its former location to get a lower triangular matrix.

谱法LU分解的优点是在对大量具有相同非零结构的矩阵分解时,通过建立包含所有具有不同非零元素的列对应的自相关函数的分解表,使得随后的所有矩阵分解只需通过查表实现。

Non-negative matrix factorization is a newly popular method for non-negative dimensionality reduction, feature extraction, data mining, etc.

摘要非负矩阵分解(Non-negative matrix factorization, NMF)是一个近年来非常流行的非负数据处理方法,它常用于维数约减、特征提取和数据挖掘等。

First, based on the factorization theorem for lepton-induced hard diffractive scat-tering and the factorization framework of the nonrelativistic Quantum Chromodynamics for quarkonia production, We present a study of J/〓+jet diffractive productionin the direct photon processes at DESY HERA.

首先,基于轻子引起的inclusive硬衍射散射的因子化定理和重夸克偶素产生的非相对论QCD因子化框架,我们研究了DESY HERA对撞机上J/〓+jet的衍射直接光产生。

In Chapter 5, a new method with direct partitioned column updates of cholesky factorization for solving large scale sparse unconstrained optimization problems is proposed, which depends on a consistent partition of the columns and cholesky factorization of the Hessian matrix.

第5章基于稀疏Hesse阵的列的相容分划,并在其Cholesky分解的基础上建立了求解大型稀疏无约束优化问题的直接校正Cholesky因子算法。

This paper analyses the LR factorization of matrix,and improves the algorithm based on recursive algorithm and iteration,and brings forth the developed method of matrix factorization,which accelerates the convergence speed of the factorization.

将方程组系数矩阵的LR分解算法过程加以改进,加入递归算法,使矩阵产生分块。

It turns out that minor prime factorization and determinantal factorization are two interesting special cases of the proposal general factorization.

其中体现出来子式和行列式分解是一般分解方案中两种有趣特殊的。

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