arXiv subjects

Rudrayan Kundu

Publications and source records attributed to Rudrayan Kundu.

3 recordsLinked to original sources

arXiv2026

Unlocking Fractional Moments in Delphic Set Streams

We consider estimation of non-integer frequency moments $F_k$ and related Bernstein-type statistics in the Delphic set stream model under a bounded-frequency assumption: every universe element appears at most $τ$ times. The main challenge of this model is to keep space low while also keeping update time low, which is not trivial because the sets can be exponential in size compared to their representations. Our core insight is that by sampling the stream at different rates and observing the resulting distinct-counts, we can 'probe' the frequency distribution and numerically integrate these probes to reconstruct a broad class of statistics. Building on that, we crucially observe that the distinct-count of a randomly sampled substream, viewed as a function of the sampling rate, is a single analytic object whose evaluations determine a broad class of statistics via a complementary Laplace-type integral. Algorithmically we exploit this by: 1. estimating those evaluations using only standard $F_0$ (distinct-count) algorithms on sampled substreams and 2. recovering target statistics by controlled numerical integration on a judiciously chosen grid. For $F_k$ with $k\in (0,1)$ we obtain the first one-pass streaming algorithms for Delphic set streams whose space and per-set update time are $\mathrm{poly}(\log|Ω|,\log m,\varepsilon^{-1},\log(1/δ))$ in the practically relevant regime $τ=\mathrm{polylog}(|Ω|,m)$; in general the bounds are polynomial in $τ$ and $\varepsilon^{-1}$ and logarithmic in $δ^{-1}$. We also give a complexity-theoretic barrier explaining why lower bounds for removing the bounded-frequency assumption appear difficult: ruling out polylogarithmic algorithms for unrestricted Delphic $F_k$ would imply a linear-space threshold-counting separation.

cs.DS↗

arXiv2026

On Linear-Size Guillotine-Separable Subsets of Fat Convex Objects, Disks, and Squares

Let $\mathcal{K}$ be a family of pairwise disjoint objects in the plane. We say that a subset $\mathcal{K}^*\subseteq \mathcal{K}$ is \emph{separable} if it admits a sequence of guillotine cuts that separate all objects in $\mathcal{K}^*$ from each other while not cutting any of them. Urrutia (1996) asked whether any family of $n$ convex objects has a separable subset of size $Ω(n)$. Pach and Tardos (2000) answered this question negatively for line segments, but established positive results for fat objects of similar size. More recently, it was shown that sets of arbitrarily-sized axis-aligned squares also admit a separable subset of linear size. However, the question whether any set of arbitrarily-sized fat convex objects has a separable subset of linear size has remained open, even for disks. A major obstacle is that the existing technique for arbitrarily-sized squares uses only axis-aligned cuts, while even for disks, axis-aligned cuts alone are insufficient to obtain a separable subset of linear size. We resolve this longstanding open problem by proving that every family of pairwise disjoint fat convex objects has a separable subset of linear size. Our result extends to higher dimensions: any family of pairwise disjoint arbitrarily-sized fat convex objects in $\mathbb{R}^d$, where $d$ is a fixed constant, has a subset of linear size that is recursively separable by a sequence of hyperplane cuts. Our framework also yields improved guarantees for important special cases. For axis-aligned squares with axis-aligned guillotine cuts, we leverage additional structural properties of squares to show that at least $13.46\%$ of the squares are separable, improving the previous best bound of $9/256 \approx 3.51\%$ due to Chalermsook, Kugelmann, Orgo, Uniyal, and Zarsav (2025). For disks, by exploiting Oler's packing inequality, we prove that at least $n/93$ disks can always be separated.

cs.CG↗

arXiv2026

Maximizing Diversity in (near-)Median String Selection

Given a set of strings over a specified alphabet, identifying a median or consensus string that minimizes the total distance to all input strings is a fundamental data aggregation problem. When the Hamming distance is considered as the underlying metric, this problem has extensive applications, ranging from bioinformatics to pattern recognition. However, modern applications often require the generation of multiple (near-)optimal yet diverse median strings to enhance flexibility and robustness in decision-making. In this study, we address this need by focusing on two prominent diversity measures: sum dispersion and min dispersion. We first introduce an exact algorithm for the diameter variant of the problem, which identifies pairs of near-optimal medians that are maximally diverse. Subsequently, we propose a $(1-ε)$-approximation algorithm (for any $ε>0$) for sum dispersion, as well as a bi-criteria approximation algorithm for the more challenging min dispersion case, allowing the generation of multiple (more than two) diverse near-optimal Hamming medians. Our approach primarily leverages structural insights into the Hamming median space and also draws on techniques from error-correcting code construction to establish these results.

cs.DS↗