Digitally Annealed Solution for the Vertex Cover Problem with Application in Cyber Security

Javad-Kalbasi, Mohammad; Dabiri, Keivan; Valaee, Shahrokh; Sheikholeslami, Ali

doi:10.1109/icassp.2019.8683696

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…ROCKET is thus applicable to radiotherapy planning optimization in general and is not restricted a single modality or hardware platform. Reports have also shown these computational approaches to be powerful in other applications such as medical image segmentation(36), finance(37), telecommunications (38,39), and computer science (40).…”

Section: Discussionmentioning

confidence: 99%

A computational optimization approach for the automatic generation of Gamma Knife radiosurgery treatment plans

Walker

Malekmohammadi

Coolens

et al. 2021

Preprint

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Gamma knife (GK) radiosurgery is a non-invasive treatment modality which allows single fraction delivery of focused radiation to one or more brain targets. Treatment planning mostly involves manual placement and shaping of shots to conform the prescribed dose to a surgical target. This process can be time consuming and labour intensive. An automated method is needed to determine the optimum combination of treatment parameters to decrease planning time and chance for operator-related error. Recent advancements in hardware platforms which employ parallel computational methods with stochastic optimization schemes are well suited to solving such combinatorial optimization problems efficiently. We present a method of generating optimized GK radiosurgery treatment plans using these techniques, which we name ROCKET (Radiosurgical Optimization Configuration Kit for Enhanced Treatments). Our approach consists of two phases in which shot isocenter positions are generated based on target geometry, followed by optimization of sector collimator parameters. Using this method, complex treatment plans can be generated, on average, in less than one minute, a substantial decrease relative to manual planning. Our results also demonstrate improved selectivity and treatment safety through decreased exposure to nearby organs-at-risk (OARs), compared to manual reference plans with matched coverage. Stochastic optimization is therefore shown to be a robust and efficient clinical tool for the automatic generation of GK radiosurgery treatment plans.

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Section: Discussionmentioning

confidence: 99%

A computational optimization approach for the automatic generation of Gamma Knife radiosurgery treatment plans

Walker

Malekmohammadi

Coolens

et al. 2021

Preprint

Self Cite

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“…Graph problems comprise another application area that has been addressed using DA. Javad-Kalbasi et al (2019) use DA in order to a solve sensor placement problem by modeling it as a minimum vertex cover problem, and report that DA is able to find optimal solutions to all tested benchmark instances and delivers high-quality solutions for a large suite of randomly generated graphs in comparison to well-known alternatives from the literature. Another such study is by Naghsh, Javad-Kalbasi, and Valaee (2019), in which the authors consider the maximum weighted clique problem on both benchmark and randomly generated instances and report that DA outperforms various existing algorithms as well as the state-of-the-art solver CPLEX.…”

Section: Applications Of Damentioning

confidence: 99%

“…DA is one such emerging technology, which is a quantum-inspired hardware architecture and has an algorithm based on simulated annealing. DA has been shown to succeed in yielding high-quality (not necessarily optimal) solutions in a short amount of computation time for a variety of problems, including the routing and wavelength assignment problem (S ¸eker, Bodur, and Pouya 2020), minimum vertex cover problem (Javad-Kalbasi et al 2019), maximum clique problem (Naghsh, Javad-Kalbasi, and Valaee 2019), and many more.…”

Section: Da Approachmentioning

confidence: 99%

Digital Annealer for quadratic unconstrained binary optimization: a comparative performance analysis

Şeker¹,

Tanoumand²,

Bodur³

2020

Preprint

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Digital Annealer (DA) is a computer architecture designed for tackling combinatorial optimization problems formulated as quadratic unconstrained binary optimization (QUBO) models. In this paper, we present the results of an extensive computational study to evaluate the performance of DA in a systematic way in comparison to multiple state-of-the-art solvers for different problem classes. We examine pure QUBO models, as well as QUBO reformulations of three constrained problems, namely quadratic assignment, quadratic cycle partition, and selective graph coloring, with the last two being new applications for DA. For the selective graph coloring problem, we also present a size reduction heuristic that significantly increases the number of eligible instances for DA. Our experimental results show that despite being in its development stage, DA can provide high-quality solutions quickly and in that regard rivals the state of the art, particularly for large instances. Moreover, as opposed to established solvers, within its limit on the number of decision variables, DA's solution times are not affected by the increase in instance size. These findings illustrate that DA has the potential to become a successful technology in tackling combinatorial optimization problems.

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“…DA is designed to solve QUBO models, and uses an algorithm based on simulated annealing. In many applications, such as minimum vertex cover problem (Javad-Kalbasi et al 2019), maximum clique problem (Naghsh et al 2019) and outlier rejection (Rahman et al 2019), it has been shown to significantly improve upon the state of the art and yield high-quality solutions quickly.…”

Section: Our Workmentioning

confidence: 99%

Routing and Wavelength Assignment with Protection: A Quadratic Unconstrained Binary Optimization Approach

Şeker,

Bodur,

Pouya

2020

Preprint

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The routing and wavelength assignment with protection (RWA-P) is an important problem in telecommunications. Given an optical network and incoming connection requests, the problem aims to grant maximum number of requests by assigning lightpaths at minimum network resource usage level, while ensuring the provided services remain functional in case of a single-link failure. We consider a practically relevant case of RWA-P where alternative lightpaths for requests are assumed to be given as a precomputed set, and show that it is NP-hard. We formulate RWA-P as an integer programming (IP) model, then use it as a foundation to develop a novel quadratic unconstrained binary optimization (QUBO) model. Moreover, we present conditions on model parameters to achieve a desired objective prioritization, and to ensure the exactness of the QUBO model. We use a new technology, Digital Annealer (DA), to solve the QUBO model, and compare it with three different alternatives that employ GUROBI, namely providing the models directly to the solver, and applying a branch-and-cut method. We conduct computational experiments on a large suite of instances for performance comparison and sensitivity analysis on model parameters. The results show that the emerging solution technology DA outperforms or is comparable to the established techniques coupled with the state-of-the-art solvers in addressing the need of generating high-quality solutions quickly.

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Digitally Annealed Solution for the Vertex Cover Problem with Application in Cyber Security

Cited by 12 publications

References 17 publications

A computational optimization approach for the automatic generation of Gamma Knife radiosurgery treatment plans

A computational optimization approach for the automatic generation of Gamma Knife radiosurgery treatment plans

Digital Annealer for quadratic unconstrained binary optimization: a comparative performance analysis

Routing and Wavelength Assignment with Protection: A Quadratic Unconstrained Binary Optimization Approach

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