Search arXiv⌕ Search

arXiv · 2610.39631

A Positivity-Preserving Numerical Method for the Free Cox-Ingersoll-Ross Equation

Abstract

We introduce a positivity-preserving drift-implicit square-root Euler method for the free Cox--Ingersoll--Ross (CIR) equation driven by free Brownian motion. The method is obtained by discretizing the square-root equation implicitly in the drift and preserves strict positivity of the numerical solution without any step-size restriction. Under the Feller condition, we establish the uniqueness of the global strictly positive solution and derive an upper bound for its operator norm. A key ingredient for the error analysis is a free stochastic differential representation of the inverse square-root process. Another key ingredient is an a priori grid-point error estimate for sufficiently small step sizes that yields uniform upper and strictly positive lower spectral bounds for the numerical solution on any fixed finite time interval. These results support the strong convergence of order $1$ and improve the classical results. Moreover, both the exact and numerical solutions are exponentially stable with respect to perturbations of the initial value. The numerical stability holds for arbitrary step sizes, and the associated discrete decay rate converges to its continuous counterpart as the step size tends to zero. Numerical experiments confirm the theoretical results.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Y. Niu, Z. Peng, J. Wei, Z. Yin, D. Zeng. 2026-10-29. A Positivity-Preserving Numerical Method for the Free Cox-Ingersoll-Ross Equation. https://arxiv.org/abs/2610.39631

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↗