Compression with wildcards: All k-models of a binary decision diagram
Marcel Wild, Yves Semegni

TL;DR
This paper introduces a method for efficiently enumerating all models of a Boolean function with fixed Hamming-weight using compressed representations and wildcards, extending existing enumeration techniques.
Contribution
It presents a novel approach to enumerate all fixed Hamming-weight models of Boolean functions efficiently using wildcards, improving on prior enumeration methods.
Findings
Efficient enumeration of all fixed Hamming-weight models in polynomial time.
Introduction of wildcards for compressed model representation.
Extension of enumeration techniques to fixed Hamming-weight models.
Abstract
Given a Binary Decision Diagram of a Boolean function in variables, it is well known that all -models can be enumerated in output polynomial time, and in a compressed way (using don't-care symbols). We show that all many -models of fixed Hamming-weight can be enumerated as well in time polynomial in and and . Furthermore, using novel wildcards, again enables a compressed enumeration of these models.
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Taxonomy
TopicsFormal Methods in Verification · Machine Learning and Algorithms · Advanced Database Systems and Queries
