Fetching the paper…
Reading the bibliography…
Semidefinite programming is an important optimization task, often used in time-sensitive applications.
J. S. Bell, On the Einstein Podolsky Rosen paradox , Physics Physique Fizika 1
1964
Earlier work this paper cites.
J. F. Clauser, M. A. Horne, A. Shimony, and R. A. Holt, Proposed Experiment to Test Local Hidden-Variable Theories , Physical Review Letters 23
1969
Earlier work this paper cites.
B. S. Cirel’son, Quantum generalizations of Bells inequality , Letters in Mathematical Physics 4
1980
Earlier work this paper cites.
R. L. Smith, Efficient Monte Carlo Procedures for Generating Points Uniformly Distributed Over Bounded Regions , Operations Research 32
1984
Earlier work this paper cites.
P. Rastall, Locality Bells Theorem and Quantum Mechanics
1985
Earlier work this paper cites.
L. A. Khalfin and B. Tsirelson, Quantum and quasi-classical analogs of Bell inequalities. , Symposium on the Foundations of Modern Physics pp. 441–460 (1985)
1985
Earlier work this paper cites.
J. J. Hopfield and D. W. Tank, “Neural” computation of decisions in optimization problems , Biological Cybernetics 52
1985
Earlier work this paper cites.
S. Popescu and D. Rohrlich, Quantum nonlocality as an axiom , Foundations of Physics 24
1994
Earlier work this paper cites.
M. X. Goemans and D. P. Williamson, Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming , Journal of the ACM 42
1995
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Semidefinite Programming Relaxations of Non-Convex Problems in Control and Combinatorial Optimization , in Communications, Computation, Control, and Signal Processing: a tribute to Thomas Kailath , edited by A. Paulraj, V. Roychowdhury, and C. D. Schaper (Springer US, Boston, MA, 1997), pp. 279–287
1997
Earlier work this paper cites.
C. Bélisle, A. Boneh, and R. Caron, Convergence properties of Hit-and-Run samplers
1998
Earlier work this paper cites.
L. Vandenberghe and S. Boyd, Applications of semidefinite programming , Applied Numerical Mathematics 29
1999
Earlier work this paper cites.
D. Jiang and J. Wang, A recurrent neural network for real-time semidefinite programming
1999
Earlier work this paper cites.
M. Nakata, H. Nakatsuji, M. Ehara, M. Fukuda, K. Nakata, and K. Fujisawa, Variational calculations of fermion second-order reduced density matrices by semidefinite programming algorithm , The Journal of Chemical Physics 114
2001
Earlier work this paper cites.
S. Prajna and A. Jadbabaie, in Hybrid Systems: Computation and Control , edited by R. Alur and G. J. Pappas (Springer, Berlin, Heidelberg, 2004), Lecture Notes in Computer Science, pp. 477–492
2004
Earlier work this paper cites.
D. A. Mazziotti, First-order semidefinite programming for the direct determination of two-electron reduced density matrices with application to many-electron atoms and molecules , The Journal of Chemical Physics 121
2004
Earlier work this paper cites.
S. Arora, E. Hazan, and S. Kale, in 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05) (2005), pp. 339–348
2005
Earlier work this paper cites.
J. Barrett, N. Linden, S. Massar, S. Pironio, S. Popescu, and D. Roberts, Nonlocal correlations as an information-theoretic resource , Physical Review A 71
2005
Cited alongside, same era.
M. Fukuda, B. J. Braams, M. Nakata, M. L. Overton, J. K. Percus, M. Yamashita, and Z. Zhao, Large-scale semidefinite programs in electronic structure calculation , Mathematical Programming 109
2007
Cited alongside, same era.
M. Navascués, S. Pironio, and A. Acín, Bounding the Set of Quantum Correlations , Physical Review Letters 98
2007
Cited alongside, same era.
M. Nakata, B. J. Braams, K. Fujisawa, M. Fukuda, J. K. Percus, M. Yamashita, and Z. Zhao, Variational calculation of second-order reduced density matrices by strong N-representability conditions and an accurate semidefinite programming solver , The Journal of Chemical Physics 128
2008
Cited alongside, same era.
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015)
2015
Later among the works it cites.
F. Chollet and others, Keras (2015)
2015
Later among the works it cites.
E. Hazan and T. Koren, A linear-time algorithm for trust region problems , Mathematical Programming 158
2016
Later among the works it cites.
S. Shah, A. K. Yadav, C. D. Castillo, D. W. Jacobs, C. Studer, and T. Goldstein, in Computer Vision – ECCV 2016 , edited by B. Leibe, J. Matas, N. Sebe, and M. Welling (Springer International Publishing, Cham, 2016), Lecture Notes in Computer Science, pp. 717–735
2016
Later among the works it cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT Press, 2016)
2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. Navascués, S. Pironio, and A. Acín, A convergent hierarchy of semidefinite programs characterizing the set of quantum correlations , New Journal of Physics 10
2008
Cited alongside, same era.
A. C. Doherty, Y. Liang, B. Toner, and S. Wehner, in 2008 23rd Annual IEEE Conference on Computational Complexity (2008), pp. 199–210
2008
Cited alongside, same era.
S. Pironio, A. Acín, S. Massar, A. B. de la Giroday, D. N. Matsukevich, P. Maunz, S. Olmschenk, D. Hayes, L. Luo, T. A. Manning, et al., Random numbers certified by Bell’s theorem , Nature 464
2010
Cited alongside, same era.
R. Jain and P. Yao, in 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (2011), pp. 463–471
2011
Cited alongside, same era.
D. A. Mazziotti, Large-Scale Semidefinite Programming for Many-Electron Quantum Mechanics , Physical Review Letters 106
2011
Cited alongside, same era.
H. Wolkowicz, R. Saigal, and L. Vandenberghe, Handbook of Semidefinite Programming: Theory Algorithms and Applications
2012
Cited alongside, same era.
E. Frazzoli, Z.-H. Mao, J.-H. Oh, and E. Feron, Resolution of Conflicts Involving Many Aircraft via Semidefinite Programming , Journal of Guidance, Control, and Dynamics (2012)
2012
Cited alongside, same era.
M. Navascués, A. García-Sáez, A. Acín, S. Pironio, and M. B. Plenio, A paradox in bosonic energy computations via semidefinite programming relaxations , New Journal of Physics 15
2013
Cited alongside, same era.
Later among the works it cites.
J. B. Brask, A. Martin, W. Esposito, R. Houlmann, J. Bowles, H. Zbinden, and N. Brunner, Megahertz-Rate Semi-Device-Independent Quantum Random Number Generators Based on Unambiguous State Discrimination , Physical Review Applied 7
2017
Later among the works it cites.
S. Bravyi, D. Gosset, R. König, and K. Temme, Approximation algorithms for quantum many-body problems , Journal of Mathematical Physics 60
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Canabarro, S. Brito, and R. Chaves, Machine Learning Nonlocal Correlations , Physical Review Letters 122
2019
Later among the works it cites.
A. Pozas-Kerstjens, R. Rabelo, Ł. Rudnicki, R. Chaves, D. Cavalcanti, M. Navascués, and A. Acín, Bounding the Sets of Classical and Quantum Correlations in Networks , Physical Review Letters 123
2019
Later among the works it cites.
A. Majumdar, G. Hall, and A. A. Ahmadi, Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning Control and Robotics , Annual Review of Control, Robotics, and Autonomous Systems 3
2020
Closest in time.
T. Kriváchy, Y. Cai, D. Cavalcanti, A. Tavakoli, N. Gisin, and N. Brunner, A neural network oracle for quantum nonlocality problems in networks , npj Quantum Information 6
2020
Closest in time.
A. A. Melnikov, P. Sekatski, and N. Sangouard, Setting Up Experimental Bell Tests with Reinforcement Learning , Physical Review Letters 125
2020
Closest in time.
2020
Closest in time.
J. Bowles, F. Baccari, and A. Salavrakos, Bounding sets of sequential quantum correlations and device-independent randomness certification , Quantum 4
2020
Closest in time.
Y. Bengio, A. Lodi, and A. Prouvost, Machine learning for combinatorial optimization: A methodological tour d’horizon , European Journal of Operational Research (2020)
2020
Closest in time.