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Combinatorial optimization problems can be solved by heuristic algorithms such as simulated annealing (SA) which aims to find the optimal solution within a large search space through thermal fluctuations.
Reducibility among combinatorial problems
Richard M Karp · 1972
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Solvable model of a spin-glass
David Sherrington and Scott Kirkpatrick · 1975
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Theory of spin glasses
Samuel Frederick Edwards and Phil W Anderson · 1975
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The euclidean travelling salesman problem is np-complete
Christos H Papadimitriou · 1977
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Optimization by simulated annealing
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi · 1983
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An heuristic approach to the structure of local minima of the sherrington-kirkpatrick model
K Nokura · 1987
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Green function monte carlo with stochastic reconfiguration
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TSP Cuts Which Do Not Conform to the Template Paradigm
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Theory of quantum annealing of an ising spin glass
Giuseppe E. Santoro, Roman Martoňák, Erio Tosatti, and Roberto Car · 2002
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Quantum annealing of the traveling-salesman problem
Roman Martoň ák, Giuseppe E. Santoro, and Erio Tosatti · 2004
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Parallel tempering: Theory, applications, and new perspectives
David J Earl and Michael W Deem · 2005
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Class of quantum many-body states that can be efficiently simulated
G. Vidal · 2008
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Ising formulations of many np problems
Andrew Lucas · 2014
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On the landscape of combinatorial optimization problems
Mohammad-H. Tayarani-N. and Adam Prügel-Bennett · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Fast and accurate deep network learning by exponential linear units (elus)
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Wishart planted ensemble: A tunably rugged pairwise ising model with a first-order phase transition
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