Often, polynomials or rational functions, orthogonal for a particular inner product are desired. In practical numerical algorithms these polynomials are not constructed, but instead the associated recurrence relations are computed. Moreover, also typically the inner product is changed to a discrete inner product, which is the finite sum of weighted functions evaluated in specific nodes. For particular applications it is beneficial to have an efficient procedure to update the recurrence relations when adding or removing nodes from the inner product. The construction of the recurrence relations is equivalent to computing a structured matrix (polynomial) or pencil (rational) having prescribed spectral properties. Hence the solution of this problem is often referred to as solving an Inverse Eigenvalue Problem. In Van Buggenhout et al. (2022) we proposed updating techniques to add nodes to the inner product while efficiently updating the recurrences. To complete this study we present in this article manners to efficiently downdate the recurrences when removing nodes from the inner product. The link between removing nodes and the QR algorithm to deflate eigenvalues is exploited to develop efficient algorithms. We will base ourselves on the perfect shift strategy and develop algorithms, both for the polynomial case and the rational function setting. Numerical experiments validate our approach.
翻译:通常情况下, 多元或理性函数, 或正方形功能是适合特定内产物的 。 在实际数字算法中, 这些多元值不是构建的, 而是相关的重现关系是计算出来的。 此外, 内产物通常被转换为独立的内产物, 也就是在特定节点中评估的加权函数的有限总和。 对于特定应用来说, 在添加或从内产物中删除节点时更新重现关系的高效程序是有好处的。 重现关系的构建相当于计算结构化矩阵( Polynomial) 或铅笔( logalal) 具有指定的光谱特性。 因此, 这个问题的解决方案常常被称作解决反向的 Eigenvalue 问题。 在 Van Buggenhout 等人( 2022 ) 中, 我们提议更新技术, 以便在有效更新复现时将节点添加内产物。 为了完成本文章中显示的重现程序, 有效地降低重现。 在从内产品中删除节点中去除节点时, 删除节点时, 和QR 解到解解的双向光值的计算法之间的联系将会被利用。 我们的矩阵将建立在高效的演算法。