Chapter 14. Minimal Unsatisfiability and Autarkies

2021 ◽  
Author(s):  
Hans Kleine Büning ◽  
Oliver Kullmann
Keyword(s):  
Linear Programming ◽  
Matching Theory ◽  
Problem Instance ◽  
Quantified Boolean Formulas ◽  
Algorithmic Problems ◽  
Boolean Formulas ◽  
Solid Foundation ◽  
Problem Instances ◽  
Basic Ideas ◽  
Minimal Unsatisfiability

Minimal unsatisfiability describes the reduced kernel of unsatisfiable formulas. The investigation of this property is very helpful in understanding the reasons for unsatisfiability as well as the behaviour of SAT-solvers and proof calculi. Moreover, for propositional formulas and quantified Boolean formulas the computational complexity of various SAT-related problems are strongly related to the complexity of minimal unsatisfiable formulas. While “minimal unsatisfiability” studies the structure of problem instances without redundancies, the study of “autarkies” considers the redundancies themselves, in various guises related to partial assignments which satisfy some part of the problem instance while leaving the rest “untouched”. As it turns out, autarky theory creates many bridges to combinatorics, algebra and logic, and the second part of this chapter provides a solid foundation of the basic ideas and results of autarky theory: the basic algorithmic problems, the algebra involved, and relations to various combinatorial theories (e.g., matching theory, linear programming, graph theory, the theory of permanents). Also the general theory of autarkies as a kind of combinatorial “meta theory” is sketched (regarding its basic notions).

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

Analysis of Incomplete Circuits Using Dependency Quantified Boolean Formulas

2017 ◽  
pp. 151-168 ◽  
Author(s):  
Ralf Wimmer ◽  
Karina Wimmer ◽  
Christoph Scholl ◽  
Bernd Becker
Keyword(s):  
Quantified Boolean Formulas ◽  
Boolean Formulas

Chapter 31. Quantified Boolean Formulas

2021 ◽  
Author(s):  
Olaf Beyersdorff ◽  
Mikoláš Janota ◽  
Florian Lonsing ◽  
Martina Seidl
Keyword(s):  
Performance Characteristics ◽  
Proof Complexity ◽  
Proof Systems ◽  
Quantified Boolean Formulas ◽  
Boolean Formulas ◽  
Clause Learning ◽  
Dependency Schemes

Solvers for quantified Boolean formulas (QBF) have become powerful tools for tackling hard computational problems from various application domains, even beyond the scope of SAT. This chapter gives a description of the main algorithmic paradigms for QBF solving, including quantified conflict driven clause learning (QCDCL), expansion-based solving, dependency schemes, and QBF preprocessing. Particular emphasis is laid on the connections of these solving approaches to QBF proof systems: Q-Resolution and its variants in the case of QCDCL, expansion QBF resolution calculi for expansion-based solving, and QRAT for preprocessing. The chapter also surveys the relations between the various QBF proof systems and results on their proof complexity, thereby shedding light on the diverse performance characteristics of different solving approaches that are observed in practice.

Chapter 29. Theory of Quantified Boolean Formulas

2021 ◽  
Author(s):  
Hans Kleine Büning ◽  
Uwe Bubeck
Keyword(s):  
Normal Form ◽  
Boolean Function ◽  
Expressive Power ◽  
Modeling Language ◽  
Complex Problems ◽  
Quantified Boolean Formulas ◽  
Horn Formulas ◽  
Boolean Formulas ◽  
Function Models ◽  
Resolution Calculus

Quantified Boolean formulas (QBF) are a generalization of propositional formulas by allowing universal and existential quantifiers over variables. This enhancement makes QBF a concise and natural modeling language in which problems from many areas, such as planning, scheduling or verification, can often be encoded in a more compact way than with propositional formulas. We introduce in this chapter the syntax and semantics of QBF and present fundamental concepts. This includes normal form transformations and Q-resolution, an extension of the propositional resolution calculus. In addition, Boolean function models are introduced to describe the valuation of formulas and the behavior of the quantifiers. We also discuss the expressive power of QBF and provide an overview of important complexity results. These illustrate that the greater capabilities of QBF lead to more complex problems, which makes it interesting to consider suitable subclasses of QBF. In particular, we give a detailed look at quantified Horn formulas (QHORN) and quantified 2-CNF (Q2-CNF).

Sign in / Sign up

Export Citation Format

Share Document