SearcharxivSearch

arXiv · 2607.23460

A Linear-Time Residue Bound for a One-Dimensional (L,V,W) Block-Cover Problem, and a Sharp Heavy-Base Threshold for its Exactness

Abstract

We study a one-dimensional exact-cover problem parameterized by three integers $(L,V,W)$: given an integer profile $a_0,\dots,a_{n-1}$, write it as a nonnegative integer combination of a length-$L$ ``horizontal'' block $[1,\dots,1]$, a value-$V$ ``vertical'' block, and a value-$W$ block, while minimizing the number of value-$W$ blocks. We give an $O(n)$ algorithm that eliminates the horizontal-block coupling by a class-wise difference recurrence and then matches residues modulo $V$ on the last $L$ columns. We prove that its output is always a valid \emph{lower bound} on the optimum, via a mod-$L$ class invariant. We then prove the main result: once the profile is dense enough --- a \emph{heavy base} $\min_c a_c \ge B(L,V,W)$ with \[ B(L,V,W)=\Big\lceil \tfrac{(L-1)\lcm(V,W)}{LW}-1\Big\rceil\,W+(L-1)(V-1), \] the bound is \emph{exact}. The exactness proof is a branch-cut argument on the exact dynamic program: two structural equivalences (a horizontal-to-vertical exchange modulo $V$, and a vertical reduction modulo $\lcm(V,W)/W$) collapse the DP to the residue computation, and the two summands of $B$ are exactly the reserves that keep both equivalences from producing a negative residual. We further show the threshold is sharp: for $(L,V,W)=(3,6,4)$, $B=14$, and the profile $(17,16,13,16,17)$ with $\min_c a_c=13$ makes the algorithm strictly undercount, so $B-1$ does not suffice. An independent exact dynamic program agrees with the algorithm on every tested profile with $\min_c a_c\ge B$ across many parameter triples, and the test harness \texttt{test\_general.c} is released for reproduction. The contribution is the algorithm, the branch-cut exactness proof, and the sharp threshold $B(L,V,W)$.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fuwei Xie. 2026-07-26. A Linear-Time Residue Bound for a One-Dimensional (L,V,W) Block-Cover Problem, and a Sharp Heavy-Base Threshold for its Exactness. https://arxiv.org/abs/2607.23460

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