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We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems.
1905
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1906
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2012
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2012
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Guang-Feng Deng, Woo-Tsong Lin, and Chih-Chung Lo, “Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization,” Expert Systems with Applications 39
2012
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“Code for unsupervised generative modeling using matrix product states,” https://github.com/congzlwag/UnsupGenModbyMPS (2018)
2018
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Stuart Hadfield, Zhihui Wang, Bryan O’Gorman, Eleanor G Rieffel, Davide Venturelli, and Rupak Biswas, “From the quantum approximate optimization algorithm to a quantum alternating operator ansatz,” Algorithms 12
2019
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Alejandro Perdomo-Ortiz, Alexander Feldman, Asier Ozaeta, Sergei V. Isakov, Zheng Zhu, Bryan O’Gorman, Helmut G. Katzgraber, Alexander Diedrich, Hartmut Neven, Johan de Kleer, Brad Lackey, and Rupak Biswas, “Readiness of quantum optimization machines for industrial applications,” Phys. Rev. Applied 12
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2014
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2014
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Adil Baykasoğlu, Mualla Gonca Yunusoglu, and F Burcin Özsoydan, “A grasp based solution approach to solve cardinality constrained portfolio optimization problems,” Computers & Industrial Engineering 90
2015
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Edwin Stoudenmire and David J Schwab, “Supervised learning with tensor networks,” in Advances in Neural Information Processing Systems 29 , edited by D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Curran Associates, Inc., 2016) pp. 4799–4807
2016
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The GPyOpt authors, “Gpyopt: A bayesian optimization framework in python,” http://github.com/SheffieldML/GPyOpt (2016)
2016
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Song Cheng, Jing Chen, and Lei Wang, “Information perspective to probabilistic modeling: Boltzmann machines versus Born machines,” Entropy 20
2017
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Can B Kalayci, Okkes Ertenlice, Hasan Akyer, and Hakan Aygoren, “An artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for cardinality constrained portfolio optimization,” Expert Systems with Applications 85
2017
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Song Cheng, Jing Chen, and Lei Wang, “Information perspective to probabilistic modeling: Boltzmann machines versus born machines,” Entropy 20
2018
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Song Cheng, Lei Wang, Tao Xiang, and Pan Zhang, “Tree tensor networks for generative modeling,” Phys. Rev. B 99
2019
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Matthew T. Perry and Richard J. Wagner, “Python module for simulated annealing,” https://github.com/perrygeo/simanneal (2019)
2019
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Tai-Danae Bradley, E M Stoudenmire, and John Terilla, “Modeling sequences with quantum states: a look under the hood,” Machine Learning: Science and Technology 1
2020
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Can B Kalayci, Olcay Polat, and Mehmet A Akbay, “An efficient hybrid metaheuristic algorithm for cardinality constrained portfolio optimization,” Swarm and Evolutionary Computation 54
2020
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Mehmet Anil Akbay, Can B Kalayci, and Olcay Polat, “A parallel variable neighborhood search algorithm with quadratic programming for cardinality constrained portfolio optimization,” Knowledge-Based Systems 198
2020
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Joachim Dahl Martin Andersen and Lieven Vandenberghe, “Python software for convex optimization,” http://cvxopt.org (2020)
2020
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Javier Alcazar, Vicente Leyton-Ortega, and Alejandro Perdomo-Ortiz, “Classical versus quantum models in machine learning: insights from a finance application,” Machine Learning: Science and Technology 1
2020
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Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio, “Flow network based generative models for non-iterative diverse candidate generation,” (2021)
2021
Closest in time.
Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema, Roger G. Melko, and Juan Carrasquilla, “Variational neural annealing,” Nature Machine Intelligence 3
2021
Closest in time.
Tunchan Cura, “A rapidly converging artificial bee colony algorithm for portfolio optimization,” Knowledge-Based Systems 233
2021
Closest in time.