Compared to centralized baselines using a single device, our method, while being decentralized, yields more accurate solutions with significant speedups of up to 953.7x over Ceres and 174.6x over DeepLM. Jing DONG Northwestern University, IL NU Department of Industrial Engineering and Management Sciences Research profile. On extensive benchmarks with public datasets, our method converges much faster than decentralized baselines with similar memory usage and communication load. Optimal scheduling of proactive service with customer. Despite limited peer-to-peer communication, our method has provable convergence to first-order critical points under mild conditions. A primal-dual approach to constrained markov decision processes. We further apply Nesterov’s acceleration and adaptive restart to improve convergence while maintaining its theoretical guarantees. Jing-dong, Lin-cang, Shuang-jiang, Meng-zi, and Jing-ping (Chang & Lu. This function makes it possible to use majorization minimization techniques and reduces bundle adjustment to independent optimization subproblems that can be solved in parallel. southern Yunnan bordering Cochinchina as well as in the far Northwestern. Strong Simulation for Multidimensional Stochastic Differential Equations via Rough Path Analysis. We achieve this by reformulating the reprojection error and deriving a novel surrogate function that decouples optimization variables from different devices. In this paper, we present a fully decentralized method that alleviates computation and communication bottlenecks to solve arbitrarily large bundle adjustment problems. Jing Dong has been working as a Lab Manager at University of Illinois at Urbana-Champaign for 10 years. Centralized methods in prior works are only able to solve small or medium size problems due to overhead in computation and communication. Jing Dong is an Assistant Professor at Medical College of Wisconsin based in Milwaukee, Wisconsin. Multi-Robot and Aerial Systems Poster Session Friday, July 14 Poster 15Ībstract: Scaling to arbitrarily large bundle adjustment problems requires data and compute to be distributed across multiple devices.
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