SearcharxivSearch

arXiv · 2602.00162

End Cover for Initial Value Problem: Complete Validated Algorithms with Complexity Analysis

Abstract

We consider the first-order autonomous ordinary differential equation \[ \mathbf{x}' = \mathbf{f}(\mathbf{x}), \] where $\mathbf{f} : \mathbb{R}^n \to \mathbb{R}^n$ is locally Lipschitz. For a box $B_0 \subseteq \mathbb{R}^n$ and $h > 0$, we denote by $\mathrm{IVP}_{\mathbf{f}}(B_0,h)$ the set of solutions $\mathbf{x} : [0,h] \to \mathbb{R}^n$ satisfying \[ \mathbf{x}'(t) = \mathbf{f}(\mathbf{x}(t)), \qquad \mathbf{x}(0) \in B_0 . \] We present a complete validated algorithm for the following \emph{End Cover Problem}: given $(\mathbf{f}, B_0, \varepsilon, h)$, compute a finite set $\mathcal{C}$ of boxes such that \[ \mathrm{End}_{\mathbf{f}}(B_0,h) \;\subseteq\; \bigcup_{B \in \mathcal{C}} B \;\subseteq\; \mathrm{End}_{\mathbf{f}}(B_0,h) \oplus [-\varepsilon,\varepsilon]^n , \] where \[ \mathrm{End}_{\mathbf{f}}(B_0,h) = \left\{ \mathbf{x}(h) : \mathbf{x} \in \mathrm{IVP}_{\mathbf{f}}(B_0,h) \right\}. \] Moreover, we provide a complexity analysis of our algorithm and introduce a novel technique for computing the end cover $\mathcal{C}$ based on covering the boundary of $\mathrm{End}_{\mathbf{f}}(B_0,h)$. Finally, we present experimental results demonstrating the practicality of our approach.

Explore related subjects

Keep this discovery

BibTeXRIS

Bingwei Zhang, Chee Yap. 2026-01-29. End Cover for Initial Value Problem: Complete Validated Algorithms with Complexity Analysis. https://arxiv.org/abs/2602.00162

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

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS