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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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Citations
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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