The DOI matches its Crossref record; 159 of the 162 references it cites were found in a registry or an index.
Pseudo-Boolean optimization
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Cited by
Sorted by citations
Items 401–500 of 825
Global optimality conditions for quadratic 0-1 optimization problems
Chen, W., & Zhang, L. (2009) · Journal of Global Optimization · doi:10.1007/s10898-009-9416-0 · Cited by 10
Several versions of the devour digest tidy-up heuristic for unconstrained binary quadratic problems
Hanafi, S., Rebaï, A., & Vasquez, M. (2011) · Journal of Heuristics · doi:10.1007/s10732-011-9169-z · Cited by 10
A Set Theoretic Approach to Lifting Procedures for 0,1 Integer Programming
Zuckerberg, M. (2004) · Cited by 10
From matchings to independent sets
Lozin, V. (2016) · Discrete Applied Mathematics · doi:10.1016/j.dam.2016.04.012 · Cited by 10
Joint routing and topology formation in multihop UWB networks
Wu, Q., Xiong, Y., Zhang, Q., Guo, Z., Xia, X., & Li, Z. (2006) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2005.863864 · Cited by 10
A Novel Algebraic Geometry Compiling Framework for Adiabatic Quantum ComputationsPreprint
Dridi, R., Alghassi, H., & TAYUR, S. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.01440 · Cited by 11
Portfolio Optimization: Applications in Quantum ComputingPreprint
Marzec, M. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2278729 · Cited by 14
Introducing ℓ1-regularized logistic regression in Markov Networks based EDAs
Malagò, L., Matteucci, M., & Valentini, G. (2011) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2011.5949804 · Cited by 9
Log-supermodular functions, functional clones and counting CSPs
Bulatov, A. A., Dyer, M., Goldberg, L. A., & Jerrum, M. (2012) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.4230/lipics.stacs.2012.302 · Cited by 9
Larsson, M., Arnab, A., Kahl, F., Zheng, S., & Torr, P. H. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-78199-0_37 · Cited by 9
On the complexity of binary polynomial optimization over acyclic hypergraphs
Pia, A. D., & Gregorio, S. D. (2022) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611977073.105 · Cited by 9
The expressive power of valued constraints: Hierarchies and collapses
Cohen, D. A., Jeavons, P., & Živný, S. (2008) · Theoretical Computer Science · doi:10.1016/j.tcs.2008.08.036 · Cited by 9
Generalized person-by-person optimization in team problems with binary decisions
Bauso, D., & Pesenti, R. (2008) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2008.4586577 · Cited by 9
Model Reductions for Inference: Generality of Pairwise, Binary, and Planar Factor Graphs
Eaton, F., & Zoubin Ghahramani, A. (2013) · Neural Computation · doi:10.1162/neco_a_00441 · Cited by 9
Local Submodularization for Binary Pairwise Energies
Gorelick, L., Boykov, Y. Y., Veksler, O., Ayed, I. B., & Delong, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2630686 · Cited by 9
Yao, W., Polewski, P., & Krzystek, P. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w7-971-2017 · Cited by 9
A New Method for Stock Price Prediction Based on MRFs and SSVM
Lai, L., Li, C., & Long, W. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.113 · Cited by 9
Shan, G., & Zhu, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2912674 · Cited by 9
Understanding transforms of pseudo-boolean functions
Whitley, D., Aguirre, H. E., & Sutton, A. M. (2020) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3377930.3390144 · Cited by 9
QUBO formulations of the longest path problem
McCollum, J., & Krauss, T. (2021) · Theoretical Computer Science · doi:10.1016/j.tcs.2021.02.021 · Cited by 10
Approximate MRF Inference Using Bounded Treewidth Subgraphs
Fix, A., Chen, J., Boros, E., & Ramin Zabih, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_28 · Cited by 9
Self-citationTractable Triangles and Cross-Free Convexity in Discrete Optimisation
Cooper, M., & Zivny, S. (2012) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3598 · Cited by 9
FPGA implementation of a stochastic neural network for monotonic pseudo-Boolean optimization
Grossi, G., & Pedersini, F. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.06.018 · Cited by 9
A global optimization algorithm for solving the minimum multiple ratio spanning tree problem
Ursulenko, O., Butenko, S. I., & Prokopyev, O. A. (2012) · Journal of Global Optimization · doi:10.1007/s10898-011-9832-9 · Cited by 9
Facet-inducing web and antiweb inequalities for the graph coloring polytope
Palubeckis, G. (2010) · Discrete Applied Mathematics · doi:10.1016/j.dam.2010.08.016 · Cited by 9
Robust Myelin Quantitative Imaging from Multi-echo T2 MRI Using Edge Preserving Spatial Priors
Shen, X., Nguyen, T. D., Gauthier, S. A., & Raj, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40811-3_78 · Cited by 9
Cascading Edge Failures: A Dynamic Network Process
Zhang, J., & Moura, J. M. F. (2017) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2017.2764921 · Cited by 9
Minimizing Dynamic and Higher Order Energy Functions using Graph Cuts
Kohli, P. (2010) · Cited by 9
On Quadratization of Pseudo-Boolean Functions1Preprint
Boros, E., & Gruber, A. (2014) · Cited by 9
Self-citationBernal, D. E., Brown, R., Sathe, P., Wudarski, F., Pavone, M., Rieffel, E. G., & Venturelli, D. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00311-2 · Cited by 8
Data Correcting Approach for Routing and Location in Networks
Goldengorin, B. I. (2013) · Springer eBooks · doi:10.1007/978-1-4419-7997-1_84 · Cited by 8
QUBO.jl : a Julia ecosystem for quadratic unconstrained binary optimization
Xavier, P. M., Ripper, P., Andrade, T., Garcia, J. D., Maculan, N., & Bernal, D. E. (2026) · Optimization methods & software · doi:10.1080/10556788.2026.2702926 · Cited by 18
Optimization of Image Acquisition for Earth Observation Satellites via Quantum Computing
Makarov, A., Taddei, M. M., Osaba, E., Franceschetto, G., Villar-Rodríguez, E., & Oregi, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48232-8_1 · Cited by 8
Universality of the Local Marginal Polytope
Průša, D., & Werner, T. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2353626 · Cited by 8
The Power of LP Relaxation for MAP Inference
Živný, S., Werner, T., & Průša, D. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9969.003.0004 · Cited by 8
Alloy design as an inverse problem of cluster expansion models
Larsen, P. M., Kalidindi, A. R., Schmidt, S., & Schuh, C. A. (2017) · Acta Materialia · doi:10.1016/j.actamat.2017.08.008 · Cited by 8
Optimum polymorphic circuits synthesis method
Fišer, P., & Šimek, V. (2018) · International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS) · doi:10.1109/dtis.2018.8368585 · Cited by 8
Role of subgraphs in epidemics over finite-size networks under the scaled SIS process
Zhang, J., & Moura, J. M. F. (2015) · Journal of Complex Networks · doi:10.1093/comnet/cnv006 · Cited by 8
Discrete Dynamical System Approaches for Boolean Polynomial Optimization
Niu, Y.-S., & Glowinski, R. (2022) · Journal of Scientific Computing · doi:10.1007/s10915-022-01882-z · Cited by 8
FastHare: Fast Hamiltonian Reduction for Large-scale Quantum Annealing
Thai, P. D., Thai, M. T., Vu, T. N., & Dinh, T. N. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00030 · Cited by 10
Fuzzy relational equations: Resolution and optimization
Li, P. (2009) · NCSU Libraries Repository (North Carolina State University Libraries) · Cited by 8
The Bipartite Boolean Quadric Polytope
Sripratak, P., Punnen, A. P., & Stephen, T. (2021) · Discrete Optimization · doi:10.1016/j.disopt.2021.100657 · Cited by 8
Solving Intensional Weighted CSPs by Incremental Optimization with BDDs
Bofill, M., Palahí, M., Suy, J., & Villaret, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10428-7_17 · Cited by 8
Economou, J. T., Kladis, G. P., Tsourdos, A., & White, B. A. (2007) · Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · doi:10.1243/09544100jaero191 · Cited by 8
Dynamic Parallel and Distributed Graph Cuts
Yu, M., Shen, S., & Hu, Z. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2609819 · Cited by 8
Randomized Subexponential Algorithms for Infinite Games
Björklund, H., Sandberg, S., & Vorobyov, S. G. (2004) · Cited by 8
OPTIMIZING THE EFFICIENCY OF THE UNITED STATES ORGAN ALLOCATION SYSTEM THROUGH REGION REORGANIZATION
Kong, N. (2006) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 8
QUBO Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem
Güney, E., Ehrenthal, J. C. F., & Hanne, T. (2025) · IEEE Access · doi:10.1109/access.2025.3550788 · Cited by 7
Decomposing Global Cost Functions
Bessière, C., Boizumault, P., de Givry, S., Gutierrez, P., Loudni, S., Métivier, J.-P., & Schiex, T. (2011) · Cited by 7
Core-guided method for constraint-based multi-objective combinatorial optimization
Tian, N., Ouyang, D., Wang, Y., Hou, Y., & Zhang, L. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01998-5 · Cited by 7
Decomposition of transition systems into sets of synchronizing state machines
Teren, V., Cortadella, J., & Villa, T. (2021) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd53832.2021.00021 · Cited by 7
Fast Three-Valued Abstract Bit-Vector Arithmetic
Onderka, J., & Ratschan, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-94583-1_12 · Cited by 7
Decomposition of transition systems into sets of synchronizing Free-choice Petri Nets
Teren, V., Cortadella, J., & Villa, T. (2022) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd57027.2022.00031 · Cited by 7
Dual expressions of decomposed subspaces of finite games
Liu, T., Qi, H., & Cheng, D. (2015) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2015.7261086 · Cited by 8
Binary Unconstrained Quadratic Optimization Problem
Kochenberger, G. A., Glover, F., & Wang, H. (2013) · Springer eBooks · doi:10.1007/978-1-4419-7997-1_15 · Cited by 7
Markov Chain Monte Carlo combined with deterministic methods for Markov random field optimization
Kim, W., & Lee, K. M. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206504 · Cited by 7
Higher order maximum persistency and comparison theorems
Shekhovtsov, A. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.05.002 · Cited by 9
Solving constrained quadratic binary problems via quantum adiabatic evolution
Ronagh, P., Woods, B., & Iranmanesh, E. (2016) · Quantum Information and Computation · doi:10.26421/qic16.11-12-6 · Cited by 8
Software evolution: a requirements engineering approach
Mylopoulos, J., Borgida, A. T., & Ernst, N. (2012) · Cited by 7
Maximum Persistency via Iterative Relaxed Inference in Graphical Models
Shekhovtsov, A., Swoboda, P., & Savchynskyy, B. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2730884 · Cited by 7
SAT-Based Generation of Optimum Function Implementations with XOR Gates
Fišer, P., Háleček, I., & Schmidt, J. (2017) · Euromicro Conference on Digital System Design (DSD) · doi:10.1109/dsd.2017.74 · Cited by 7
Identification of boolean control networks incorporating prior knowledge
Zhang, Z., Leifeld, T., & Zhang, P. (2017) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2017.8264542 · Cited by 7
Enhancing Constraint-Based Multi-Objective Combinatorial Optimization
Terra-Neves, M., Lynce, I., & Manquinho, V. M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12212 · Cited by 7
Quadratic unconstrained binary optimization formulation for rectified-linear-unit-type functions
Sato, G., Konoshima, M., Ohwa, T., Tamura, H., & Ohkubo, J. (2019) · Physical review. E · doi:10.1103/physreve.99.042106 · Cited by 7
The Moran process on 2-chromatic graphs
Kaveh, K., McAvoy, A., Chatterjee, K., & Nowak, M. A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008402 · Cited by 7
Exact MAP-Inference by Confining Combinatorial Search With LP Relaxation
Haller, S., Swoboda, P., & Savchynskyy, B. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12202 · Cited by 7
Connections and Integration with SAT Solvers: A Survey and a Case Study in Computational Biology
Corblin, F., Bordeaux, L., Fanchon, É., Hamadi, Y., & Trilling, L. (2010) · Springer optimization and its applications · doi:10.1007/978-1-4419-1644-0_13 · Cited by 7
Boros, E., & Meñkov, V. (2004) · Discrete Applied Mathematics · doi:10.1016/j.dam.2004.06.006 · Cited by 7
Self-citationImproved QUBO Formulations for D-Wave Quantum Computing
Fowler, A. (2017) · ResearchSpace (University of Auckland) · doi:10.13140/rg.2.2.31829.73445 · Cited by 8
Generalized Flows for Optimal Inference in Higher Order MRF-MAP
Arora, C., Banerjee, S., Kalra, P. K., & Maheshwari, S. N. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2388218 · Cited by 6
The Problem of Cell Formation: Ideas and Their Applications
Goldengorin, B. I., Krushinsky, D., & Pardalos, P. M. (2013) · Springer optimization and its applications · doi:10.1007/978-1-4614-8002-0_1 · Cited by 6
QUBO formulations of three NP problems
Mahasinghe, A., Fernando, V., & Samarawickrama, P. (2021) · Journal of Information and Optimization Sciences · doi:10.1080/02522667.2021.1930657 · Cited by 6
Efficiently Distributed Watertight Surface Reconstruction
Caraffa, L., Marchand, Y., Brédif, M., & Vallet, B. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00150 · Cited by 6
sSfS: Segmented Shape from Silhouette Reconstruction of the Human Body
Krajnik, W., Markiewicz, Ł., & Sitnik, R. (2022) · Sensors · doi:10.3390/s22030925 · Cited by 6
On the Complexity of Binary Polynomial Optimization Over Acyclic Hypergraphs
Pia, A. D., & Gregorio, S. D. (2022) · Algorithmica · doi:10.1007/s00453-022-01086-9 · Cited by 6
Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation
Kumar, M., Turki, H., Preston, D., & Koller, D. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2372766 · Cited by 8
Discovering combinatorial interactions in survival data
duVerle, D. A., Takeuchi, I., Murakami‐Tonami, Y., Kadomatsu, K., & Tsuda, K. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt532 · Cited by 6
Basso, R. S., & Strusevich, V. A. (2015) · Discrete Applied Mathematics · doi:10.1016/j.dam.2015.10.006 · Cited by 6
Higher-Order Models in Computer Vision
Kohli, P., & Rother, C. (2017) · CRC Press eBooks · doi:10.1201/b12281-3 · Cited by 6
Discovering, quantifying, and displaying attacks
Vigo, R., Nielson, F., & Nielson, H. R. (2017) · Logical Methods in Computer Science · doi:10.2168/lmcs-12(4:5)2016 · Cited by 6
Aghazadeh, A., Öçal, O., & Ramchandran, K. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa505 · Cited by 7
Classes of Submodular Constraints Expressible by Graph Cuts
Živný, S., & Jeavons, P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85958-1_8 · Cited by 6
Dynamic Programming on a Quantum Annealer: Solving the RBC ModelPreprint
Fernández‐Villaverde, J., & Hull, I. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4477988 · Cited by 6
A Predictive Approach for Selecting the Best Quantum Solver for an Optimization Problem
Volpe, D., Quetschlich, N., Graziano, M., Turvani, G., & Wille, R. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00121 · Cited by 5
Relaxation-Based Preprocessing Techniques for Markov Random Field Inference
Wang, C., & Ramin Zabih, A. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.628 · Cited by 5
Self-healing in Service Mashups Through Feature Adaptation
Bashari, M., Ebrahim Bagheri, A., & Du, W. (2017) · International Software Product Line Conference (SPLC) · doi:10.1145/3106195.3106215 · Cited by 5
Matroid optimization problems with monotone monomials in the objective
Fischer, A., Fischer, F., & McCormick, S. T. (2020) · Discrete Applied Mathematics · doi:10.1016/j.dam.2020.04.004 · Cited by 5
Quadratization and convexification in polynomial binary optimization
Crama, Y., Elloumi, S., Lambert, A., & Rodríguez-Heck, E. (2025) · Journal of Combinatorial Optimization · doi:10.1007/s10878-025-01334-y · Cited by 5
A polyhedral study of lifted multicuts
Andres, B., Gregorio, S. D., Irmai, J., & Lange, J.-H. (2023) · Discrete Optimization · doi:10.1016/j.disopt.2022.100757 · Cited by 5
Efficient linear reformulations for binary polynomial optimization problems
Elloumi, S., & Verchère, Z. (2023) · Computers & Operations Research · doi:10.1016/j.cor.2023.106240 · Cited by 5
Partition Crossover can Linearize Local Optima Lattices of k-bounded Pseudo-Boolean Functions
Whitley, D., Ochoa, G., & Chicano, F. (2023) · Foundations of Genetic Algorithms · doi:10.1145/3594805.3607129 · Cited by 5
Wroński, M., Burek, E., & Leśniak, M. (2025) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2024.3474856 · Cited by 12
Fast Multi-labelling for Stereo Matching
Zhang, Y., Hartley, R. I., & Wang, L. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15558-1_38 · Cited by 5
Submodular Relaxation for Inference in Markov Random Fields
Osokin, A., & Vetrov, D. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2369046 · Cited by 5
Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures.
Motwani, K., Adluru, N., Hinrichsts, C., Alexander, A. L., & Singh, V. (2010) · PubMed · Cited by 5
MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves
Kappes, J. H., Beier, T., & Schnörr, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16181-5_37 · Cited by 5
Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs
Weller, A. (2015) · Apollo (University of Cambridge) · Cited by 5
Performance Models for Split-Execution Computing Systems
Humble, T., McCaskey, A., Schrock, J., Seddiqi, H., Britt, K. A., & Imam, N. (2016) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2016.113 · Cited by 6
Kellerer, H. G., Basso, R. S., & Strusevich, V. A. (2016) · Theoretical Computer Science · doi:10.1016/j.tcs.2016.11.009 · Cited by 5
Stratification for Constraint-Based Multi-Objective Combinatorial Optimization
Terra-Neves, M., Lynce, I., & Manquinho, V. M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/191 · Cited by 5
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 ›
Are you an author, or do you see a problem with this page? Request a review
