English
Related papers

Related papers: Hard CNF Instances for Ideal Proof Systems

200 papers

In this paper we consider the Ideal Membership Problem (IMP for short), in which we are given real polynomials $f_0,f_1,\dots, f_k$ and the question is to decide whether $f_0$ belongs to the ideal generated by $f_1,\dots,f_k$. In the more…

Computational Complexity · Computer Science 2021-06-09 Andrei A. Bulatov , Akbar Rafiey

In recent years, numerous vision and learning tasks have been (re)formulated as nonconvex and nonsmooth programmings(NNPs). Although some algorithms have been proposed for particular problems, designing fast and flexible optimization…

Computer Vision and Pattern Recognition · Computer Science 2017-07-03 Yiyang Wang , Risheng Liu , Xiaoliang Song , Zhixun Su

The CNF formula satisfiability problem (CNF-SAT) has been reduced to many fundamental problems in P to prove tight lower bounds under the Strong Exponential Time Hypothesis (SETH). Recently, the works of Abboud, Hansen, Vassilevska W. and…

Computational Complexity · Computer Science 2020-08-31 Daniel Gibney , Gary Hoppenworth , Sharma V. Thankachan

This is a survey on propositional proof complexity aimed at introducing the basics of the field with a particular focus on a method known as feasible interpolation. This method is used to construct "hard theorems" for several proof systems…

Logic · Mathematics 2025-05-07 Amirhossein Akbar Tabatabai

We study the complexity of inverse cellular automata on configurations of bounded size. Deciding injectivity in this setting is co-NP-complete by a theorem of Durand. We give a simpler proof of this theorem by a direct reduction from UNSAT…

Logic · Mathematics 2026-04-02 Maryia Kapytka

In various scenarios motivated by real life, such as medical data analysis, autonomous driving, and adversarial training, we are interested in robust deep networks. A network is robust when a relatively small perturbation of the input…

Machine Learning · Computer Science 2024-10-07 Patryk Krukowski , Daniel Wilczak , Jacek Tabor , Anna Bielawska , Przemysław Spurek

Interval analysis (or interval bound propagation, IBP) is a popular technique for verifying and training provably robust deep neural networks, a fundamental challenge in the area of reliable machine learning. However, despite substantial…

Machine Learning · Computer Science 2021-12-15 Matthew Mirman , Maximilian Baader , Martin Vechev

Bottom-up knowledge compilation is a paradigm for generating representations of functions by iteratively conjoining constraints using a so-called apply function. When the input is not efficiently compilable into a language - generally a…

Computational Complexity · Computer Science 2021-12-24 Alexis de Colnet , Stefan Mengel

A major open problem in proof complexity is to demonstrate that random 3-CNFs with a linear number of clauses require super-polynomial size refutations in bounded-depth Frege systems. We take the first step towards addressing this question…

Computational Complexity · Computer Science 2024-09-04 Svyatoslav Gryaznov , Navid Talebanfard

We consider the ILP Feasibility problem: given an integer linear program $\{Ax = b, x\geq 0\}$, where $A$ is an integer matrix with $k$ rows and $\ell$ columns and $b$ is a vector of $k$ integers, we ask whether there exists…

Data Structures and Algorithms · Computer Science 2019-07-24 Dušan Knop , Michał Pilipczuk , Marcin Wrochna

As a natural extension of the SAT problem, an array of proof systems for quantified Boolean formulas (QBF) have been proposed, many of which extend a propositional proof system to handle universal quantification. By formalising the…

Logic in Computer Science · Computer Science 2023-06-22 Olaf Beyersdorff , Joshua Blinkhorn , Luke Hinde

Least-absolute-deviations (LAD) line fitting is robust to outliers but computationally more involved than least squares regression. Although the literature includes linear and near-linear time algorithms for the LAD line fitting problem,…

Machine Learning · Statistics 2025-12-25 Stefan Volz , Martin Storath , Andreas Weinmann

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 -- within the framework of propositional proof complexity -- the problem of certifying unsatisfiability of CNF formulas under the promise that any satisfiable formula has many satisfying assignments, where ``many'' stands for an…

Computational Complexity · Computer Science 2010-04-19 Nachum Dershowitz , Iddo Tzameret

We present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over…

Artificial Intelligence · Computer Science 2009-06-08 P. Mancarella , G. Terreni , F. Sadri , F. Toni , U. Endriss

We prove the #P-hardness of the counting problems associated with various satisfiability, graph and combinatorial problems, when restricted to planar instances. These problems include \begin{romannum} \item[{}] {\sc 3Sat, 1-3Sat, 1-Ex3Sat,…

Computational Complexity · Computer Science 2007-05-23 Harry B. Hunt , Madhav V. Marathe , Venkatesh Radhakrishnan , Richard E. Stearns

The basic problem in the PAC model of computational learning theory is to determine which hypothesis classes are efficiently learnable. There is presently a dearth of results showing hardness of learning problems. Moreover, the existing…

Machine Learning · Computer Science 2014-03-11 Amit Daniely , Nati Linial , Shai Shalev-Shwartz

Random constraint satisfaction problems (CSPs) such as random $3$-SAT are conjectured to be computationally intractable. The average case hardness of random $3$-SAT and other CSPs has broad and far-reaching implications on problems in…

Computational Complexity · Computer Science 2019-11-11 Jonah Brown-Cohen , Prasad Raghavendra

We present a novel approach that integrates unfitted finite element methods and neural networks to approximate partial differential equations on complex geometries. Easy-to-generate background meshes (e.g., a simple Cartesian mesh) that cut…

Numerical Analysis · Mathematics 2025-12-04 Wei Li , Alberto F. Martín , Santiago Badia

The main purpose of this paper is to develop further the integrated theory of the probe and singular sources methods (IPS) which may work for a group of inverse obstacle problems. Here as a representative and typical member of the group, an…

Analysis of PDEs · Mathematics 2025-08-25 Masaru Ikehata