Center for Discrete Mathematics and Theoretical Computer Science

About: Center for Discrete Mathematics and Theoretical Computer Science is a facility organization based out in Piscataway, New Jersey, United States. It is known for research contribution in the topics: Local search (optimization) & Optimization problem. The organization has 140 authors who have published 175 publications receiving 2345 citations.

Highly efficient evaluation design (HEED) for comparing algorithms used to detect nuclear materials

Abstract: ID: 1750 Paul Kantor, Christie Nelson, Fred Roberts, William M. Pottenger CCICADA, Rutgers University Piscataway, New Jersey

An Adaptive Multimeme Memetic Algorithm for the VLSI Standard Cell Placement Problem

Abstract: Placement has a tremendous impact on the final performance of a VLSI chip, and the problem itself is a combinatorial optimization problem. Memetic algorithms with problem-specific designs can solve the problem with high quality solutions, but they are generally time-consuming. To address this issue, adopting an adaptive strategy has become a key way to reduce the runtime of a memetic algorithm. In this paper, we present an adaptive multimeme memetic algorithm (AMMA) for the VLSI standard cell placement problem. In accordance with the distinctive features of the solution space of the problem, we propose a set of adaptive strategies to greatly improve the runtime efficiency. The main novelty of the proposed strategy lies in its constructing of crossover multimeme and the acceptance criteria of placement candidates. The experimental results and comparisons on Peko suite3 and ISPD04 benchmark circuits verified the efficacy and efficiency of the proposed adaptive strategies. Compared to the memetic algorithm without these main adaptive strategies, the AMMA reduces the runtime by around 32.58% on average.

A dynamic graph-based approach to ranking firms for identifying key players using inter-firm transactions

Abstract: Ranking firms in an inter-firm transaction network is a crucial task for identifying key players in an industry, thereby explaining the agglomeration of economic activities and assisting with competitor identification. To the best of our knowledge, despite the advantages of network-based approaches in market analysis, few studies have employed network analysis tools to rank firms. However, these studies failed to capture the characteristics of inter-firm transaction networks (i.e., evolving over time, having multiple edges between nodes, among others). In this study, we propose a new ranking method, FirmRank, that identifies key players based on centrality metrics in network analysis, leveraging inter-firm transactions to discern the characteristics of an inter-firm transaction network. Our proposed ranking method is evaluated using real-world datasets from a corporate information database, and the evaluation results demonstrate the superiority of our method over well-known ranking methods—PageRank and age-based PageRank.

A Data-Validated Host-Parasite Model for Infectious Disease Outbreaks.

Abstract: The use of model experimental systems and mathematical models is important to further understanding of infectious disease dynamics and strategize disease mitigation. Gyrodactylids are helminth ectoparasites of teleost fish which have many dynamical characteristics of microparasites but offer the advantage that they can be quantified and tracked over time, allowing further insight into within-host and epidemic dynamics. In this paper, we design a model to describe host-parasite dynamics of the well-studied guppy-Gyrodactylus turnbulli system, using experimental data to estimate parameters and validate it. We estimate the basic reproduction number (R_0), for this system. Sensitivity analysis reveals that parasite growth rate, and the rate at which the guppy mounts an immune response have the greatest impact on outbreak peak and timing both for initial outbreaks and on longer time scales. These findings highlight guppy population resistance and parasite virulence as key factors in disease control, and future work should focus on incorporating heterogeneity in host resistance into disease models and extrapolating to other host-parasite systems.

A VLSI global placement solver based on proximal alternating direction method

Abstract: In nonlinear global placement methods, the very large scale integration (VLSI) global placement problem is formulated as a nonlinear mathematical programming problem that contains the wirelength objective and the non-overlapping constraints, and it is usually solved by the penalty function method. In this paper, unlike using the penalty function method, a proximal alternating direction method (PADM) based solver is attempted to solve the VLSI global placement problem. In the solver, a alternating direction method combining with proximal point algorithm is used to optimize the VLSI placement problem according to its features. Moreover, local convergence of the PADM method is proved under some conditions. In addition, the multilevel framework is adopted to make the PADM based solver scalable. Preliminary numerical results on the IBM standard cell benchmarks show that the proposed solver is promising.