Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Aramon, Maliheh"'
Autor:
Perera, Dilina, Akpabio, Inimfon, Hamze, Firas, Mandra, Salvatore, Rose, Nathan, Aramon, Maliheh, Katzgraber, Helmut G.
We present Chook, an open-source Python-based tool to generate discrete optimization problems of tunable complexity with a priori known solutions. Chook provides a cross-platform unified environment for solution planting using a number of techniques,
Externí odkaz:
http://arxiv.org/abs/2005.14344
Publikováno v:
Phys. Rev. E 100, 043311 (2019)
Parallel tempering Monte Carlo has proven to be an efficient method in optimization and sampling applications. Having an optimized temperature set enhances the efficiency of the algorithm through more-frequent replica visits to the temperature limits
Externí odkaz:
http://arxiv.org/abs/1907.03906
The feasibility pump algorithm is an efficient primal heuristic for finding feasible solutions to mixed-integer programming problems. The algorithm suffers mainly from fast convergence to local optima. In this paper, we investigate the effect of an a
Externí odkaz:
http://arxiv.org/abs/1906.06434
Autor:
Aramon, Maliheh, Rosenberg, Gili, Valiante, Elisabetta, Miyazawa, Toshiyuki, Tamura, Hirotaka, Katzgraber, Helmut G.
Publikováno v:
Front. Phys. 7, 48 (2019)
The Fujitsu Digital Annealer (DA) is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 1024 variables. The DA'
Externí odkaz:
http://arxiv.org/abs/1806.08815
Autor:
Hernandez, Maritza, Aramon, Maliheh
Publikováno v:
Quantum Information Processing, volume 16, number 5, May 2017
Quantum annealing is a promising technique which leverages quantum mechanics to solve hard optimization problems. Considerable progress has been made in the development of a physical quantum annealer, motivating the study of methods to enhance the ef
Externí odkaz:
http://arxiv.org/abs/1701.04433
In this paper, we tackle the problem of measuring similarity among graphs that represent real objects with noisy data. To account for noise, we relax the definition of similarity using the maximum weighted co-$k$-plex relaxation method, which allows
Externí odkaz:
http://arxiv.org/abs/1601.06693
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