The task of determining the greatest common divisors (GCD) for several polynomials which arises in image compression, computer algebra and speech encoding can be formulated as a low rank approximation problem with Syl...The task of determining the greatest common divisors (GCD) for several polynomials which arises in image compression, computer algebra and speech encoding can be formulated as a low rank approximation problem with Sylvester matrix. This paper demonstrates a method based on structured total least norm (STLN) algorithm for matrices with Sylvester structure. We demonstrate the algorithm to compute an approximate GCD. Both the theoretical analysis and the computational results show that the method is feasible.展开更多
提出了一种采用结构总体最小二乘(Structured total least squares,STLS)进行卫星惯量矩阵在轨估计的方法,与当前估计方法相比,该方法在考虑敏感器测量噪声时能获得一致估计。首先由动量守恒定律得到估计方程,针对该方程的特点定义了惯...提出了一种采用结构总体最小二乘(Structured total least squares,STLS)进行卫星惯量矩阵在轨估计的方法,与当前估计方法相比,该方法在考虑敏感器测量噪声时能获得一致估计。首先由动量守恒定律得到估计方程,针对该方程的特点定义了惯量矩阵的STLS估计,并使用结构总体最小范数(Structured total least norm,STLN)算法进行求解。证明了当噪声为高斯分布时该STLS估计为极大似然估计,给出了该STLS估计具有一致性的充分条件,仿真结果验证了文章所提估计方法的有效性。展开更多
文摘The task of determining the greatest common divisors (GCD) for several polynomials which arises in image compression, computer algebra and speech encoding can be formulated as a low rank approximation problem with Sylvester matrix. This paper demonstrates a method based on structured total least norm (STLN) algorithm for matrices with Sylvester structure. We demonstrate the algorithm to compute an approximate GCD. Both the theoretical analysis and the computational results show that the method is feasible.
文摘提出了一种采用结构总体最小二乘(Structured total least squares,STLS)进行卫星惯量矩阵在轨估计的方法,与当前估计方法相比,该方法在考虑敏感器测量噪声时能获得一致估计。首先由动量守恒定律得到估计方程,针对该方程的特点定义了惯量矩阵的STLS估计,并使用结构总体最小范数(Structured total least norm,STLN)算法进行求解。证明了当噪声为高斯分布时该STLS估计为极大似然估计,给出了该STLS估计具有一致性的充分条件,仿真结果验证了文章所提估计方法的有效性。
