Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

Publisher Summary The past few years have seen enormous progress in the performance of
Boolean satisfiability (SAT) solvers. Despite the worst-case exponential run time of all known …

Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

Solving computationally hard problems using conventional computing architectures is often
slow and energetically inefficient. Quantum computing may help with these challenges, but it …

Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …

We present a learning-based approach to computing solutions for certain NP-hard
problems. Our approach combines deep learning techniques with useful algorithmic …

The SMT-LIB initiative is an international effort, supported by several research groups
worldwide, with the two-fold goal of producing an extensive on-line library of benchmarks …

Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

This paper describes the TPTP problem library and associated infrastructure, from its use of
Clause Normal Form (CNF), via the First-Order Form (FOF) and Typed First-order Form …

Event-B is a formal method for system-level modelling and analysis. Key features of Event-B
are the use of set theory as a modelling notation, the use of refinement to represent systems …