线性方程

xiàn xìnɡ fānɡ chénɡ
  • linear equation
线性方程线性方程
  1. 系数矩阵为特殊M-矩阵的线性方程组的PEk解法

    PE_k solution for linear equation system with special M-matrix as its coefficient matrix

  2. 关于剩余类环Zm上的线性方程组

    The linear equation system on the ring-Z_m of remanent class

  3. 织构材料X射线应力分析的线性方程组

    Simultaneous Linear Equations of X ray Stress Analysis for Textured Materials

  4. 用E方法与高精度计算解线性方程组

    Solving linear systems with e-method and high accuracy arithmetic

  5. 用Excel来演示解线性方程组的过程

    Demonstrating the processes of solving the set of linear equations by Microsoft Excel

  6. 应用ABS算法求解一类不定线性方程组

    ABS algorithms for solving certain system of indefinite equations

  7. 求解这个k元线性方程组可得到X在此子空间内的一个最优解。

    Solving the linearly equations with k unknown numbers , the optimal solution of X is obtained in the subspace .

  8. 求解大型对称线性方程组的循环收缩Lanczos算法

    Restarted and Deflated Lanczos Algorithm for Solving Large Symmetric Systems

  9. 线性方程组的并行算法及在Transputer系统上的实现

    A Parallel Algorithm of Solving Linear Equations and Implementation on Multitransputer Systems

  10. 结构方程模型(SEM)是应用线性方程系统表示观测变量与潜在变量之间及潜在变量之间关系的一种统计方法。

    Structural Equation Modeling ( SEM ) is a statistical method that states the relation between observation variables and latent variables or among the latent variables by linear equation system .

  11. 本文提出了新的电磁层析成象方法,其线性方程组与k无关,能用于传统方法无法应用的情况。

    In this paper , a new EMT is presented , the linear system of equations of which is independent of K and can therefore work well where even the traditional technique failed .

  12. 作为特例,此定理导出了四元数线性方程的Cramer法则。

    In particular , the Cramer formula of quaternionic linear equations is derived .

  13. 介绍应用线性方程组因子表解法辨识自回归动平均(ARMA)模型的方法。

    The application of linear equation-set factor-table solution method to identificate the auto regressive moving average ( ARMA ) model is introduced .

  14. 为研究线性方程组的数值解,文章用直接解法、雅可比迭代法、高斯-赛德尔迭代法进行了近似计算,并给出在MATLAB中计算的程序。

    To study numerical solution of the linear equations , the article presents direct method , Jacobi iterate method and Gauss-seidel iterate method to approximately calculate , and has given its process in MATLAB .

  15. Krylov子空间方法解线性方程组的并行性能分析及应用

    Parallel Efficiency Analysis of Krylov subspace Methods for Large Sparse Linear Algebraic Systems and Application

  16. 高阶DLMS型方法求解线性方程组

    Higher Order DLMS-Type Method for Solving Liner Algebraic Equations

  17. 应用这些结论还给出了rayS~-阵和rayS~-阵的图论特征刻画,及其他若干类特殊的复线性方程组的ray可解性条件。

    As applications of these results , we also gave the graph theoretical characterizations of the ray S-matrices and ray S-matrices , and the ray solvable conditions on some other special classes of complex linear systems .

  18. 由回归分析产生多元线性方程和相应的拟合度(GOF)及估计LAI的标准误(SE)。

    This procedure produces multivariate linear equations and their associated goodness-of-fit ( GOF ) values and standard errors ( SE ) for LAI estimation .

  19. 在多视角测量数据融合中采用Screw理论,将坐标系关系求取中带约束的多变量非线性优化问题转化为线性方程组的最小二乘问题,简化了计算的复杂性,提高了求解过程的稳定性。

    In coordinate computing , multiple variables non-linear optimized problems with constraint are changed into least squares problems of linear equation group utilizing screw theory , which simplifies the computing complexity and advanced stability in solving course .

  20. 当区间线性方程组的系数矩阵A为区间H阵时,证明了BIMV算法的可行性与收敛性。

    The feasibility and convergence of the BIMV method are proved when the coefficient matrix A of interval linear equations is an interval H-matrix .

  21. 由于存储芯片版图P/G网规模的巨大,对于计算电阻网络中节点间等效电阻问题,直接利用常规线性方程组求解算法无法同时满足内存空间与运行时间上的限制。

    Because of the large scale of the P / G routing network in the memory chip layout , general linear equation group resolving algorithms for calculating the equivalent resistance between the nodes cannot satisfy the restrict of both memory space and running time simultaneously .

  22. 讨论了二阶线性方程化成一阶线性方程的条件,并指出这些条件联系着一类Riccati方程的求解问题。

    In this paper , the conditions of changing second order linear differential equations into first linear differential equations are discussed , which are connected with the solution of Riccati equation .

  23. 通过收集、分析实验数据,运用多元线性方程和统计学原理提出了几个基于UML类图的备选质量预测模型,并详细定义了其中的度量指标。

    Through collecting and analyzing the data from experiment , applying multivariate linear equation and the theory of statistics a few alternative quality prediction models which were based UML class diagram were proposed , and concerning measurement metrics were defined fully .

  24. 利用线性方程组理论给出了Lagrange插值公式的一个构造性证明,得到了Vandermonde矩阵的逆矩阵的一种显式算法。

    A constructive proofs for Lagrange interpolation formula is given by means of linear equations system , and an explicit algorithm for the inverse matrix of the Vandermonde matrix is obtained .

  25. 通过将问题的KKT系统转化成一个约束方程,算法在每步迭代只需解一个线性方程组即可得到搜索方向。

    By reformulating the KKT system as a constrained equation , the algorithm generates the search direction by solving a linear equation at each iteration .

  26. 对Jacobi迭代法与Gauss-Seidel迭代法在解线性方程组中的应用进行了介绍,并比较了两者的优缺点。

    In this paper , the application of Jacobi Iteration and Gauss-Seidel Iteration in solution of linear ( equations ) was introduced , and their advantage and disadvantage were also compared .

  27. Excel中“规划求解”解决日常工作中遇到的减少成本与增加利润及非齐次线性方程组解的问题。

    Through the " solver " of Excel , we can solve two kinds of question which are encountered in our routine . One is the issue of costs reduction & profits increase , the other is the issue of solving the non-homogeneous linear equation .

  28. 给出一般约束最优化的序列二次规划(SQP)和序列线性方程组(SSLE)算法两个拓广的模型。

    In this paper , two extension models of successive quadratic programming ( SQP ) algorithms and sequential system of linear equations ( SSLE ) algorithms for solving general constrained optimization are given .

  29. 主要包括下面三部分内容:第二章,我们首先研究了线性方程的Cauchy问题,然后利用压缩映射原理得到了非线性方程局部解的存在唯一性。

    The main results include the following three aspects : In Chapter two , we firstly study the Cauchy problem to the linear equation , then we obtain the existence and uniqueness of local solutions for the nonlinear Cauchy problem by means of the contraction mapping theorem .

  30. 由于使用基本解方法后得到的插值矩阵是高度病态的,再加上问题本身(不论是IHCP还是BHCP)的高度不适定性,所以最终得到的线性方程组是极为病态的。

    The interpolation matrixes arising from the MFS are highly ill-conditioned due to the highly ill-posed problems ( both IHCP and BHCP ) . Thus , a regularization method should be employed .