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For input $x$, let $F(x)$ denote the set of outputs that are the "legal" answers for a computational problem $F$. Suppose $x$ and members of $F(x)$ are so large that there is not time to read them in their entirety. We propose a model of…

Data Structures and Algorithms · Computer Science 2011-04-08 Ronitt Rubinfeld , Gil Tamir , Shai Vardi , Ning Xie

If $L^x$ is the total occupation local time of $d$-dimensional super-Brownian motion, $X$, for $d=2$ and $d=3$, we construct a random measure $\mathcal{L}$, called the boundary local time measure, as a rescaling of $L^x e^{-\lambda L^x} dx$…

Probability · Mathematics 2020-01-27 Jieliang Hong

Many proofs in discrete mathematics and theoretical computer science are based on the probabilistic method. To prove the existence of a good object, we pick a random object and show that it is bad with low probability. This method is…

Information Theory · Computer Science 2017-08-01 Pat Morin , Wolfgang Mulzer , Tommy Reddad

We propose a new algorithmic framework, called "partial rejection sampling", to draw samples exactly from a product distribution, conditioned on none of a number of bad events occurring. Our framework builds (perhaps surprising) new…

Data Structures and Algorithms · Computer Science 2019-01-16 Heng Guo , Mark Jerrum , Jingcheng Liu

The investigation of partitions of integers plays an important role in combinatorics and number theory. Among the many variations, partitions into powers $0<\alpha<1$ were of recent interest. In the present paper we want to extend our…

Combinatorics · Mathematics 2023-11-16 Gabriel F. Lipnik , Manfred G. Madritsch , Robert F. Tichy

Partial Rejection Sampling is an algorithmic approach to obtaining a perfect sample from a specified distribution. The objects to be sampled are assumed to be represented by a number of random variables. In contrast to classical rejection…

Data Structures and Algorithms · Computer Science 2024-09-18 Mark Jerrum

We continue the investigation of locally testable codes, i.e., error-correcting codes for whom membership of a given word in the code can be tested probabilistically by examining it in very few locations. We give two general results on…

Information Theory · Computer Science 2007-07-16 Eli Ben-Sasson , Madhu Sudan

The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of black-box LLMs. Existing methodologies usually prioritize a…

Artificial Intelligence · Computer Science 2024-03-06 Wenyang Hu , Yao Shu , Zongmin Yu , Zhaoxuan Wu , Xiangqiang Lin , Zhongxiang Dai , See-Kiong Ng , Bryan Kian Hsiang Low

We show the existence of rigid combinatorial objects which previously were not known to exist. Specifically, for a wide range of the underlying parameters, we show the existence of non-trivial orthogonal arrays, $t$-designs, and $t$-wise…

Combinatorics · Mathematics 2017-03-14 Greg Kuperberg , Shachar Lovett , Ron Peled

In this work, we study the Lov\'asz local lemma (LLL) problem in the area of distributed quantum computing, which has been the focus of attention of recent advances in quantum computing [STOC'24, STOC'25, STOC'25]. We prove a lower bound of…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-10-20 Sebastian Brandt , Tim Göttlicher

Introduced by Korman, Kutten, and Peleg (PODC 2005), a proof labeling scheme (PLS) is a distributed verification system dedicated to evaluating if a given configured graph satisfies a certain property. It involves a centralized prover,…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-08-25 Yuval Emek , Yuval Gil , Shay Kutten

A frustration-free local Hamiltonian has the property that its ground state minimises the energy of all local terms simultaneously. In general, even deciding whether a Hamiltonian is frustration-free is a hard task, as it is closely related…

Quantum Physics · Physics 2017-12-15 András Gilyén , Or Sattath

Data is the cornerstone of large language models (LLMs), but not all data is useful for model learning. Carefully selected data can better elicit the capabilities of LLMs with much less computational overhead. Most methods concentrate on…

Machine Learning · Computer Science 2024-07-12 Mingjia Yin , Chuhan Wu , Yufei Wang , Hao Wang , Wei Guo , Yasheng Wang , Yong Liu , Ruiming Tang , Defu Lian , Enhong Chen

Since the invention of the famous LLL algorithm, lattice reduction has been an extremely useful tool in computational number theory. By construction, the LLL algorithm deals with lattices living in a vector space endowed with a positive…

Computational Complexity · Computer Science 2025-11-21 Antoine Joux

In pattern matching on strings, a locate query asks for an enumeration of all the occurrences of a given pattern in a given text. The r-index [Gagie et al., 2018] is a recently presented compressed self index that stores the text and…

Data Structures and Algorithms · Computer Science 2025-07-29 Patrick Dinklage , Johannes Fischer , Lukas Nalbach , Jan Zumbrink

Optimization methods that make use of derivatives of the objective function up to order $p > 2$ are called tensor methods. Among them, ones that minimize a regularized $p$th-order Taylor expansion at each step have been shown to possess…

Optimization and Control · Mathematics 2025-10-30 Karl Welzel , Yang Liu , Raphael A. Hauser , Coralia Cartis

We introduce probabilistic language tries (PLTs), a unified representation that makes explicit the prefix structure implicitly defined by any generative model over sequences. By assigning to each outgoing edge the conditional probability of…

Machine Learning · Computer Science 2026-04-09 Gregory Magarshak

Kim defined a very general combinatorial abstraction of the diameter of polytopes called subset partition graphs to study how certain combinatorial properties of such graphs may be achieved in lower bound constructions. Using Lov\'asz'…

Combinatorics · Mathematics 2012-03-08 Nicolai Hähnle

Recent advances in Large Language Models (LLMs) have opened new perspectives for automation in optimization. While several studies have explored how LLMs can generate or solve optimization models, far less is understood about what these…

Artificial Intelligence · Computer Science 2025-12-16 Francesca Da Ros , Luca Di Gaspero , Kevin Roitero

The class PLS (Polynomial Local Search) captures the complexity of finding a solution that is locally optimal and has proven to be an important concept in the theory of local search. It has been shown that local search versions of various…

Data Structures and Algorithms · Computer Science 2025-12-16 Yasuaki Kobayashi , Kazuhiro Kurita , Yutaro Yamaguchi
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