RDNF Oriented Analytics to Random Boolean Functions
Sábháilte in:
| Foilsithe in: | arXiv.org (Feb 1, 2024), p. n/a |
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| Príomhchruthaitheoir: | |
| Rannpháirtithe: | , , |
| Foilsithe / Cruthaithe: |
Cornell University Library, arXiv.org
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| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full text outside of ProQuest |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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MARC
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| 001 | 2922281708 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2922281708 | ||
| 045 | 0 | |b d20240201 | |
| 100 | 1 | |a Aslanyan, Levon | |
| 245 | 1 | |a RDNF Oriented Analytics to Random Boolean Functions | |
| 260 | |b Cornell University Library, arXiv.org |c Feb 1, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Dominant areas of computer science and computation systems are intensively linked to the hypercube-related studies and interpretations. This article presents some transformations and analytics for some example algorithms and Boolean domain problems. Our focus is on the methodology of complexity evaluation and integration of several types of postulations concerning special hypercube structures. Our primary goal is to demonstrate the usual formulas and analytics in this area, giving the necessary set of common formulas often used for complexity estimations and approximations. The basic example under considered is the Boolean minimization problem, in terms of the average complexity of the so-called reduced disjunctive normal form (also referred to as complete, prime irredundant, or Blake canonical form). In fact, combinatorial counterparts of the disjunctive normal form complexities are investigated in terms of sets of their maximal intervals. The results obtained compose the basis of logical separation classification algorithmic technology of pattern recognition. In fact, these considerations are not only general tools of minimization investigations of Boolean functions, but they also prove useful structures, models, and analytics for constraint logic programming, machine learning, decision policy optimization and other domains of computer science. | |
| 653 | |a Decision analysis | ||
| 653 | |a Boolean functions | ||
| 653 | |a Mathematical analysis | ||
| 653 | |a Computer science | ||
| 653 | |a Combinatorial analysis | ||
| 653 | |a Boolean | ||
| 653 | |a Optimization | ||
| 653 | |a Logic programming | ||
| 653 | |a Algorithms | ||
| 653 | |a Hypercubes | ||
| 653 | |a Canonical forms | ||
| 653 | |a Complexity | ||
| 653 | |a Constraint modelling | ||
| 653 | |a Machine learning | ||
| 653 | |a Domains | ||
| 653 | |a Pattern recognition | ||
| 700 | 1 | |a Arsenyan, Irina | |
| 700 | 1 | |a Karakhanyan, Vilik | |
| 700 | 1 | |a Sahakyan, Hasmik | |
| 773 | 0 | |t arXiv.org |g (Feb 1, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2922281708/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2402.00999 |