348,418 research outputs found
Optimization Based Self-localization for IoT Wireless Sensor Networks
In this paper we propose an embedded optimization framework for the simultaneous self-localization of all sensors in wireless sensor networks making use of range measurements from ultra-wideband (UWB) signals. Low-power UWB radios, which provide time-of-arrival measurements with decimeter accuracy over large distances, have been increasingly envisioned for realtime localization of IoT devices in GPS-denied environments and large sensor networks. In this work, we therefore explore different non-linear least-squares optimization problems to formulate the localization task based on UWB range measurements. We solve the resulting optimization problems directly using non-linear-programming algorithms that guarantee convergence to locally optimal solutions. This optimization framework allows the consistent comparison of different optimization methods for sensor localization. We propose and demonstrate the best optimization approach for the self-localization of sensors equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for the plug-and-play deployment of the optimal localization algorithm. Numerical results indicate that the proposed approach improves localization accuracy and decreases computation times relative to existing iterative methods
A smart local moving algorithm for large-scale modularity-based community detection
We introduce a new algorithm for modularity-based community detection in
large networks. The algorithm, which we refer to as a smart local moving
algorithm, takes advantage of a well-known local moving heuristic that is also
used by other algorithms. Compared with these other algorithms, our proposed
algorithm uses the local moving heuristic in a more sophisticated way. Based on
an analysis of a diverse set of networks, we show that our smart local moving
algorithm identifies community structures with higher modularity values than
other algorithms for large-scale modularity optimization, among which the
popular 'Louvain algorithm' introduced by Blondel et al. (2008). The
computational efficiency of our algorithm makes it possible to perform
community detection in networks with tens of millions of nodes and hundreds of
millions of edges. Our smart local moving algorithm also performs well in small
and medium-sized networks. In short computing times, it identifies community
structures with modularity values equally high as, or almost as high as, the
highest values reported in the literature, and sometimes even higher than the
highest values found in the literature
Comparison of RL Algorithms for Learning to Learn Problems
Machine learning has been applied to many different problems successfully due to the expressiveness of neural networks and simplicity of first order optimization algorithms. The latter being a vital piece needed for training large neural networks efficiently. Many of these algorithms were produced with behavior produced by experiments and intuition. An interesting question that comes to mind is that rather than observing and then designing algorithms with beneficial behaviors, can these algorithms be learned through a reinforcement learning by modeling optimization as a game. This paper explores several reinforcement learning algorithms which are applied to learn policies suited for optimization
Optimization Algorithms for Machine Learning Designed for Parallel and Distributed Environments
This thesis proposes several optimization methods that utilize parallel algorithms for large-scale machine learning problems. The overall theme is network-based machine learning algorithms; in particular, we consider two machine learning models: graphical models and neural networks. Graphical models are methods categorized under unsupervised machine learning, aiming at recovering conditional dependencies among random variables from observed samples of a multivariable distribution. Neural networks, on the other hand, are methods that learn an implicit approximation to underlying true nonlinear functions based on sample data and utilize that information to generalize to validation data. The goal of finding the best methods relies on an optimization problem tasked with training such models. Improvements in current methods of solving the optimization problem for graphical models are obtained by parallelization and the use of a new update and a new step-size selection rule in the coordinate descent algorithms designed for large-scale problems. For training deep neural networks, we consider the second-order optimization algorithms within trust-region-like optimization frameworks. Deep networks are represented using large-scale vectors of weights and are trained based on very large datasets. Hence, obtaining second-order information is very expensive for these networks. In this thesis, we undertake an extensive exploration of algorithms that use a small number of curvature evaluations and are hence faster than other existing methods
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