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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
An algorithm for Boolean satisfiability based on generali...
Virendra Sule · 2014-06-18 · via cs.DS updates on arXiv.org

This paper proposes an algorithm for deciding consistency of systems of Boolean equations in several variables with co-efficients in the two element Boolean algebra $B_{0}=\{0,1\}$ and find all satisfying assignments. The algorithm is based on the application of a well known generalized Boole-Shannon orthonormal (ON) expansion of Boolean functions. A necessary and sufficient consistency condition for a special class of functions was developed in \cite{sule} using such an expansion. Paper \cite{sule} develops a condition for consistency of the equation $f(X)=0$ for the special classes of Boolean functions 1) $f$ in $B(Φ(X))$ for an ON set $Φ$ of Boolean functions in $X$ over a general Boolean algebra $B$ and 2) $f$ in $B(X_{2})(Φ(X_{1}))$. The present paper addresses the problem of obtaining the consistency conditions for arbitrary Boolean functions in $B_{0}(X)$. Next, the consistency for a single equation is shown equivalent to another system of Boolean equations which involves the ON functions and characterizes all solutions. This result is then extended for Boolean systems in several variables over the algebra $B_{0}=\{0,1\}$ which does not convert the system into a single equation. This condition leads to the algorithm for computing all solutions of the Boolean system without using analogous resolution and determine satisfiability. For special systems defined by CNF formulas this algorithm results into an extension of the DPLL algorithm in which the \emph{splitting rule} is generalized to several variables in terms of ON terms in the sense that splitting of CNF set in a single variable $x$ is equivalent to ON terms $x,x'$.