Search arXiv⌕ Search

arXiv · 2610.39388

Contour-Based Fast Fractional Integration in a Jacobi Fractional Polynomial Basis

Abstract

We develop a contour-based method for applying Riemann--Liouville fractional integrals in a Jacobi fractional polynomial basis with an endpoint weight. A Gamma-ratio representation reduces the action to contour quadrature and banded ultraspherical solves. For a fixed number of contour nodes, the cost and storage are linear in the polynomial dimension. For fractional equations, we construct selected diagonals of a short rational approximation directly and use their banded factorizations as reusable right preconditioners for Krylov iterations. Numerical experiments show accurate integral actions and substantially reduced solution costs across Abel, multi-order, BBO, deferred-correction, eigenvalue, and boundary-value problems.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiaolin Liu. 2026-10-29. Contour-Based Fast Fractional Integration in a Jacobi Fractional Polynomial Basis. https://arxiv.org/abs/2610.39388

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A continuous scale space of diffeomorphisms

In this paper, we define and study a nested family of reproducing kernel Hilbert spaces of vector fields that is indexed by a range of scales, from which we construct a reproducing kernel Hilbert space of scale-dependent vector fields. We provide a characterization of the reproducing kernel of that space, with numerical approximations ensuring quick evaluations when this kernel does not have a closed form. We then introduce a multiscale version of the large deformation diffeomorphic metric mapping (LDDMM) problem and prove the existence of solutions. Finally, we provide numerical experiments performing landmark matching using multiscale LDDMM.

math.NA↗

Hierarchical Matrices with Tucker Blocks: Construction and Forward Evaluation

In this paper, we introduce hierarchical matrices with Tucker blocks (HMTB) for data-sparse approximation of matrices arising from nonoscillatory and mildly oscillatory kernels. Building on the hierarchical matrix framework, HMTB matrices replace conventional low-rank blocks with Tucker representations. Using high-dimensional interpolation and tensor compression and contractions, we develop algorithms for constructing HMTB matrices and performing matrix-vector multiplication. For an $N \times N$ matrix, these algorithms have linear and quasi-linear complexities in $N$, respectively. Numerical comparisons with an interpolation-based $\mathcal{H}$-matrix implementation demonstrate storage savings and, in several cases, reduced construction times, while maintaining competitive forward-evaluation costs. The framework is also applicable to quasi-uniform grids.

math.NA↗

Data-driven model order reduction for wave propagation in materials with damage and nonlinearities

In this work, we consider wave propagation in materials characterized by nonlinear properties or damage. To accelerate the simulations of the resulting high-dimensional problems, we apply model order reduction methods. Depending on the knowledge of the underlying equations and the availability of their discrete operators, intrusive methods (here projection-based approaches based on proper orthogonal decomposition (POD)) or non-instrusive methods (here data-driven approaches including dynamic mode decomposition (DMD) and operator inference (OpInf)) can be used. We recall the theoretical foundations of the methods and apply them to the problem of wave propagation. In three different numerical examples, we evaluate the performance of the reduction techniques.

math.NA↗