Digital Annealer for High-Speed Solving of Combinatorial optimization Problems and Its Applications

Matsubara, S.; Takatsu, Motomu; Miyazawa, T.; Shibasaki, Takayuki; Watanabe, Yuji; Takemoto, Kazuhiro; Tamura, Hirotaka

doi:10.1109/asp-dac47756.2020.9045100

Cited by 119 publications

(102 citation statements)

References 13 publications

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“…In this section we present the results of the Ising models formulation found in [9] on instances taken from [13] using an Ising machine called a "Digital Annealer" (DA) [14], [15] as well as a "simulated annealing sampler"(SA) [16] for a baseline comparison.…”

Section: Simple Qkp Trial On An Ising Machinementioning

confidence: 99%

Analysis and Acceleration of the Quadratic Knapsack Problem on an Ising Machine

Parizy

Togawa

2021

IEICE Trans. Fundamentals

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The binary quadratic knapsack problem (QKP) aims at optimizing a quadratic cost function within a single knapsack. Its applications and difficulty make it appealing for various industrial fields. In this paper we present an efficient strategy to solve the problem by modeling it as an Ising spin model using an Ising machine to search for its ground state which translates to the optimal solution of the problem. Secondly, in order to facilitate the search, we propose a novel technique to visualize the landscape of the search and demonstrate how difficult it is to solve QKP on an Ising machine. Finally, we propose two software solution improvement algorithms to efficiently solve QKP on an Ising machine.

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Section: Simple Qkp Trial On An Ising Machinementioning

confidence: 99%

Analysis and Acceleration of the Quadratic Knapsack Problem on an Ising Machine

Parizy

Togawa

2021

IEICE Trans. Fundamentals

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“…In the end, they offered a model that can be implemented on purpose-built hardware designed to circumvent the NP-hardness of such reformulated combinatorial optimization problems. Indeed, this reformulation allows us to take advantage of recent hardware advances specifically designed for QUBO formulations Aramon et al (2019); Matsubara et al (2020). In the same vein, to circumvent the NP-hardness of a problem closely related to graph clustering, the maximum weighted independent set of the graph, and produce a well diversified portfolio of Dow Jones stocks, Marzec (2013) implemented the work of Boginski et al (2005) using the D-Wave adiabatic quantum computation system.…”

Section: Clustering For Index-trackingmentioning

confidence: 99%

“…0.85 0.9 0.95 1.0 1.05 1.1 1.15 1.2 1.25 1.3 1.35 1.4 1.45 1.5 1.55 1.6 1.65 1.7 1.75 1.8 gamma To circumvent the NP-hard nature of the clustering problem, we use a purpose built computer architecture, the Fujitsu Digital Annealer (DA). The DA provides fast compu-tation and is designed specifically for combinatorial optimization problems expressed in QUBO form Aramon et al (2019); Matsubara et al (2020).…”

Section: Parametersmentioning

confidence: 99%

Market Graph Clustering via QUBO and Digital Annealing

Hong

Miasnikof

Kwon

et al. 2021

JRFM

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We present a novel technique for cardinality-constrained index-tracking, a common task in the financial industry. Our approach is based on market graph models. We model our reference indices as market graphs and express the index-tracking problem as a quadratic K-medoids clustering problem. We take advantage of a purpose-built hardware architecture to circumvent the NP-hard nature of the problem and solve our formulation efficiently. The main contributions of this article are bridging three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a binary quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of various market indices, using only a small subset of their constituent assets. Moreover, our binary quadratic formulation allows us to take advantage of recent hardware advances to overcome the NP-hard nature of the problem and obtain solutions faster than with traditional architectures and solvers.

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“…The DA [23] is a massively parallel hardware architecture for solving combinatorial optimization problems. This architecture performs a Markov Chain Monte Carlo (MCMC) search to minimize the Ising energy…”

Section: Digital Annealermentioning

confidence: 99%

Near-Optimal Resampling in Particle Filters Using the Ising Energy Model

Rahman

Javad-Kalbasi

Valaee

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Resampling increasing the variance of the tracking algorithm in Particle Filtering (PF). Instead of utilizing resampling procedures that rely on asymptotic convergence properties, we show that intelligently selecting and replicating a set of samples can better represent the posterior approximation and improve the overall performance of the PF. To this end, we formulate the resampling procedure as an integer program that minimizes an upper bound on the Kullback-Leibler divergence (KLD) between the resampled distribution and the posterior approximation. We then transform the problem into an Ising energy minimization problem, which we are able to efficiently solve. Applying our novel paradigm to a challenging sequential importance resampling (SIR) simulation shows faster convergence over the number of resampled particles and a 35% improvement in the median KLD for a fixed number of particles.

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Digital Annealer for High-Speed Solving of Combinatorial optimization Problems and Its Applications

Cited by 119 publications

References 13 publications

Analysis and Acceleration of the Quadratic Knapsack Problem on an Ising Machine

Analysis and Acceleration of the Quadratic Knapsack Problem on an Ising Machine

Market Graph Clustering via QUBO and Digital Annealing

Near-Optimal Resampling in Particle Filters Using the Ising Energy Model

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