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Pseudo-Boolean optimization
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“The potentially large overhead in the number of variables lead to the development of several methods for degree reduction (e.g., References [160, 167, 168, 180, 188])”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [168]
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Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
(1979) · Cited by 43,841
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
Theory of games and economic behavior
Garcia, S. R., & Miller, S. J. (2019) · American Mathematical Society eBooks · doi:10.1090/mbk/121/32 · Cited by 16,777
Reducibility among Combinatorial Problems
Karp, R. M. (1972) · Complexity of Computer Computations · doi:10.1007/978-1-4684-2001-2_9 · Cited by 11,511
Shapley, L. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511528446.003 · Cited by 10,698
17. A Value for n-Person Games
Shapley, L. S. (1953) · Contributions to the Theory of Games (AM-28), Volume II · doi:10.1515/9781400881970-018 · Cited by 6,950
Glover, F. (1990) · INFORMS journal on computing · doi:10.1287/ijoc.2.1.4 · Cited by 5,777
Glover, F. (1989) · INFORMS journal on computing · doi:10.1287/ijoc.1.3.190 · Cited by 5,469
The Theory of Games and Economic Behavior
Harrison, R. W., von Neumann, J., & Morgenstern, O. (1945) · Journal of Farm Economics · doi:10.2307/1232672 · Cited by 5,318
Research works printed but not linked: 17
- T. Badics, Approximation of some Nonlinear Binary Optimization Problems, Ph.D. Thesis, RUTCOR, Rutgers University, 1996.
- Balas, 1967, Extension de l'algorithme additif a la programmation en nombres at a la programmation non lineare, C. R. Acad. Sci. Paris, 258, 5136
- Banzhaf III, 1965, Weighted voting does not work: a mathematical analysis, Rutgers Law Rev., 19, 317
- Benzaken, 1983, Adjoints of pure bidirected graphs, Congr. Numer., 39, 123
- Fortet, 1959, L'algebre de Boole en recherche operationelle, Cahiers Études Centre Rech. Oper., 1, 5
- Fortet, 1960, Applications de l'algebre de Boole en recherche operationelle, Rev. Francaise Rech. Oper., 4, 17
- Hammer, 1968, Plant location—a pseudo-Boolean approach, Israel J. Technol., 6, 330
- Hammer, 1977, Pseudo-Boolean functions and game theory. I. Core elements and Shapley value, Cahiers Centre Etudes Rech. Oper., 19, 159
- Hammer, 1963, Application of discrete linear programming to the minimization of Boolean functions, Rev. Math. Pures Appl., 8, 459
- P. Hansen, B. Simeone, A class of quadratic pseudoboolean functions whose maximization is reducible to a network flow problem, CORR 79-39, Department of Comb. Optim., University of Waterloo.
- Laughhunn, 1971, Computational experience with capital expenditure programming models under risk, J. Business Finance, 3, 43
- Rosenberg, 1972, 0–1 optimization and non-linear programming, Rev. Française Automatique, Informatique Rech. Opér. (Série Bleue), 2, 95
- Rosenberg, 1975, Reduction of bivalent maximization to the quadratic case, Cahiers Centre Etudes Rech. Oper., 17, 71
- Rosenberg, 1980, Linear decompositions of positive real function of binary arguments, Utilitas Math., 17, 17
- I. Shepanik, Use of pseudo-boolean preference functions in metagame analysis, Ph.D. Thesis, Department of System Design, 1973.
- B. Simeone, Quadratic 0–1 programming, Boolean functions and graphs, Ph.D. Thesis in Combinatorics and Optimization, Waterloo, 1979.
- Trubin, 1969, On a method of solution of integer linear programming problems of special kind, Soviet Math. Dokl., 10, 1544
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Metadata
- Authors
- Boros, E., & Hammer, P. L.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Boros, Endre
- Venue
- Discrete Applied Mathematics
- Publisher
- Elsevier BV
- Published
- 2002-11
- Volume
- 123
- Issue
- 1-3
- Pages
- 155-225
- ISSN
- 0166-218X (ISSN-L)
- OpenAlex
- W2074078071
- Cited by
- 827
- References
- 162
- Citations by year
- 2026: 34 · 2025: 34 · 2024: 27 · 2023: 33 · 2022: 32 · 2021: 28 · 2020: 35 · 2019: 39 · 2018: 42 · 2017: 38 · 2016: 35 · 2015: 43 · 2014: 62 · 2013: 58 · 2012: 52
- FWCI
- 9.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Discrete Applied Mathematics (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- ORBi (University of Liège) (submittedVersion, other-oa)
- Funders
- National Science Foundation (9806389)
- Office of Naval Research (N00014-92-J-1375, N00014)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Graph Theory Research
- Keywords
- pseudo-Boolean optimization, set functions, algebraic expressions, approximation algorithms, combinatorial optimization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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