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We devise a framework for proving tight lower bounds under the counting exponential-time hypothesis #ETH introduced by Dell et al. (ACM Transactions on Algorithms, 2014). Our framework allows us to convert classical #P-hardness results for…

Computational Complexity · Computer Science 2017-05-09 Radu Curticapean

A constraint satisfaction problem (CSP) is said to be \emph{approximation resistant} if it is hard to approximate better than the trivial algorithm which picks a uniformly random assignment. Assuming the Unique Games Conjecture, we give a…

Computational Complexity · Computer Science 2013-01-15 Per Austrin , Subhash Khot

For a $k$-ary predicate $P$, a random instance of CSP$(P)$ with $n$ variables and $m$ constraints is unsatisfiable with high probability when $m \gg n$. The natural algorithmic task in this regime is \emph{refutation}: finding a proof that…

Computational Complexity · Computer Science 2016-10-11 Ryuhei Mori , David Witmer

We study the computational complexity of the problem $\#\text{IndSub}(\Phi)$ of counting $k$-vertex induced subgraphs of a graph $G$ that satisfy a graph property $\Phi$. Our main result establishes an exhaustive and explicit classification…

Computational Complexity · Computer Science 2022-03-30 Jacob Focke , Marc Roth

The Promise Constraint Satisfaction Problem (PCSP) is a generalization of the Constraint Satisfaction Problem (CSP) that includes approximation variants of satisfiability and graph coloring problems. Barto [LICS '19] has shown that a…

Computational Complexity · Computer Science 2025-06-09 Kristina Asimi , Libor Barto

We survey results on the formalization and independence of mathematical statements related to major open problems in computational complexity theory. Our primary focus is on recent findings concerning the (un)provability of complexity…

Computational Complexity · Computer Science 2025-04-08 Igor C. Oliveira

We prove several tight results on the fine-grained complexity of approximating the diameter of a graph. First, we prove that, for any $\varepsilon>0$, assuming the Strong Exponential Time Hypothesis (SETH), there are no near-linear time…

Data Structures and Algorithms · Computer Science 2021-04-05 Ray Li

We argue that parameterized complexity is a useful tool with which to study global constraints. In particular, we show that many global constraints which are intractable to propagate completely have natural parameters which make them…

Artificial Intelligence · Computer Science 2009-03-04 Christian Bessiere , Emmanuel Hebrard , Brahim Hnich , Zeynep Kiziltan , Toby Walsh

The k-Clique problem is a canonical hard problem in parameterized complexity. In this paper, we study the parameterized complexity of approximating the k-Clique problem where an integer k and a graph G on n vertices are given as input, and…

Computational Complexity · Computer Science 2025-01-28 Karthik C. S. , Subhash Khot

A matrix is said to possess the Restricted Isometry Property (RIP) if it acts as an approximate isometry when restricted to sparse vectors. Previous work has shown it to be NP-hard to determine whether a matrix possess this property, but…

Computational Complexity · Computer Science 2018-07-04 Jonathan Weed

We extend Choiceless Polynomial Time (CPT), the currently only remaining promising candidate in the quest for a logic capturing PTime, so that this extended logic has the following property: for every class of structures for which…

Logic in Computer Science · Computer Science 2023-02-13 Moritz Lichter , Pascal Schweitzer

We study the Steiner Tree problem on the intersection graph of most natural families of geometric objects, e.g., disks, squares, polygons, etc. Given a set of $n$ objects in the plane and a subset $T$ of $t$ terminal objects, the task is to…

Computational Geometry · Computer Science 2025-11-11 Sujoy Bhore , Baris Can Esmer , Daniel Marx , Karol Wegrzycki

The proportional hazards (PH) model is arguably one of the most popular models used to analyze time to event data arising from clinical trials and longitudinal studies, among many others. In many such studies, the event time of interest is…

We study the parameterized complexity of evaluating Ontology Mediated Queries (OMQs) based on Guarded TGDs (GTGDs) and Unions of Conjunctive Queries (UCQs), in the case where relational symbols have unrestricted arity and where the…

Logic in Computer Science · Computer Science 2021-06-24 Cristina Feier

Conditional lower bounds based on $P\neq NP$, the Exponential-Time Hypothesis (ETH), or similar complexity assumptions can provide very useful information about what type of algorithms are likely to be possible. Ideally, such lower bounds…

Computational Complexity · Computer Science 2022-03-16 Dániel Marx

This paper addresses the properties of Continuous Interior Penalty (CIP) finite element solutions for the Helmholtz equation. The $h$-version of the CIP finite element method with piecewise linear approximation is applied to a…

Numerical Analysis · Mathematics 2012-11-08 Lingxue Zhu , Erik Burman , Haijun Wu

$ \newcommand{\eps}{\epsilon} \newcommand{\NP}{\mathsf{NP}} \newcommand{\YES}{\mathsf{YES}} \newcommand{\NO}{\mathsf{NO}} \newcommand{\myminus}{\text{-}}\newcommand{\Bsat}{{\mathsf{B}}} \newcommand{\threesat}{\rm{3}\myminus\mathsf{SAT}}…

Computational Complexity · Computer Science 2018-06-12 Ishay Haviv , Oded Regev , Amnon Ta-Shma

Recent work by Rauhut and Ward developed a notion of weighted sparsity and a corresponding notion of Restricted Isometry Property for the space of weighted sparse signals. Using these notions, we pose a best weighted sparse approximation…

Information Theory · Computer Science 2015-01-08 Jason Jo

Constraint Satisfaction Problems are ubiquitous in fields ranging from the physics of solids to artificial intelligence. In many cases, such systems undergo a transition when the ratio of constraints to variables reaches some value…

Statistical Mechanics · Physics 2025-03-25 Michael Winer , Aidan Herderschee

We propose a method for inferring the conditional indepen- dence graph (CIG) of a high-dimensional discrete-time Gaus- sian vector random process from finite-length observations. Our approach does not rely on a parametric model (such as,…

Machine Learning · Statistics 2014-03-11 Alexander Jung , Reinhard Heckel , Helmut Bölcskei , Franz Hlawatsch