Core Concepts

Efficient algorithms for approximating connection coefficients between orthogonal polynomials with modified measures.

Abstract

The content discusses fast algorithms for approximating connection coefficients between orthogonal polynomials with modified measures. It introduces infinite-dimensional banded matrix factorizations to compute the modified Jacobi matrices efficiently. The article presents applications and numerical experiments using open-source implementations based on these results. Key topics include orthogonal polynomials, matrix factorizations, and infinite-dimensional matrices.

Stats

The connection coefficients are computed via infinite-dimensional banded matrix factorizations.
The algorithms enable sparse spectral methods with modified classical orthogonal polynomials.
The Jacobi matrix implements multiplication by x for orthonormal polynomials.

Quotes

"We describe fast algorithms for approximating the connection coefficients between a family of orthogonal polynomials and another family with a polynomially or rationally modified measure."

Key Insights Distilled From

by Timon S. Gut... at **arxiv.org** 03-27-2024

Deeper Inquiries

The concept of banded derivatives of modified classical orthogonal polynomials plays a crucial role in enhancing computational efficiency in various numerical algorithms. By representing the derivatives of these orthogonal polynomials as banded matrices, we can exploit the sparsity of these matrices to significantly reduce the computational complexity of operations involving differentiation.
Banded matrices have a well-defined structure where most of the elements are zero, except for a few diagonals near the main diagonal. This sparsity property allows for more efficient storage and computation compared to general dense matrices. When the derivatives of orthogonal polynomials are banded, operations such as differentiation can be performed with fewer arithmetic operations, leading to faster computations and reduced memory requirements.
In practical terms, the use of banded derivatives enables the application of efficient algorithms that take advantage of the structured nature of these matrices. For example, in solving differential equations or performing spectral methods involving orthogonal polynomials, the banded structure of the derivative matrices allows for faster and more accurate computations, ultimately improving the overall computational efficiency of the algorithms.

Completely monotonic functions play a significant role in the context of M-matrix connection coefficients, particularly when considering properties related to positive-definite matrices and their inverses. In the context of M-matrices, which are characterized by having positive diagonal elements and non-positive off-diagonal elements, completely monotonic functions exhibit specific properties that have implications for the structure and properties of these matrices.
One key implication is that completely monotonic functions can preserve the M-matrix property when applied to a positive-definite matrix. Specifically, if a function is completely monotonic and positive on the interval (0, ∞), and its derivative is also completely monotonic, then the resulting matrix after applying this function to a positive-definite matrix will also be an M-matrix. This property is valuable in understanding how certain transformations of matrices can retain important structural characteristics, such as positive definiteness and the M-matrix property.
By leveraging completely monotonic functions in the context of M-matrix connection coefficients, we can analyze and manipulate matrices with specific structural properties, leading to insights into the behavior of these matrices under certain transformations and operations.

The efficient computation of banded derivatives of orthogonal polynomials using connection coefficients involves leveraging the relationships between the polynomials and their derivatives to streamline the differentiation process. By expressing the derivatives in terms of connection coefficients, we can efficiently compute the banded derivative matrices through a systematic approach that takes advantage of the structured nature of these matrices.
One approach to efficiently computing banded derivatives is to utilize the connection coefficients to establish relationships between the polynomials at different degrees. By applying these relationships recursively, we can derive expressions for the derivatives in terms of the connection coefficients, allowing for a more direct and efficient computation of the banded matrices.
Additionally, techniques such as matrix factorizations and spectral methods can be employed to compute the banded derivatives effectively. By utilizing the banded structure of the derivative matrices and exploiting the sparsity of these matrices, efficient algorithms can be developed to compute the derivatives with reduced computational complexity and improved efficiency.
Overall, by incorporating connection coefficients and leveraging the structured nature of banded matrices, the computation of banded derivatives of orthogonal polynomials can be optimized for efficiency, enabling faster and more accurate calculations in various numerical algorithms and applications.

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