401 research outputs found
Prox-DBRO-VR: A Unified Analysis on Decentralized Byzantine-Resilient Composite Stochastic Optimization with Variance Reduction and Non-Asymptotic Convergence Rates
Decentralized Byzantine-resilient stochastic gradient algorithms resolve
efficiently large-scale optimization problems in adverse conditions, such as
malfunctioning agents, software bugs, and cyber attacks. This paper targets on
handling a class of generic composite optimization problems over multi-agent
cyberphysical systems (CPSs), with the existence of an unknown number of
Byzantine agents. Based on the proximal mapping method, two variance-reduced
(VR) techniques, and a norm-penalized approximation strategy, we propose a
decentralized Byzantine-resilient and proximal-gradient algorithmic framework,
dubbed Prox-DBRO-VR, which achieves an optimization and control goal using only
local computations and communications. To reduce asymptotically the variance
generated by evaluating the noisy stochastic gradients, we incorporate two
localized variance-reduced techniques (SAGA and LSVRG) into Prox-DBRO-VR, to
design Prox-DBRO-SAGA and Prox-DBRO-LSVRG. Via analyzing the contraction
relationships among the gradient-learning error, robust consensus condition,
and optimal gap, the theoretical result demonstrates that both Prox-DBRO-SAGA
and Prox-DBRO-LSVRG, with a well-designed constant (resp., decaying) step-size,
converge linearly (resp., sub-linearly) inside an error ball around the optimal
solution to the optimization problem under standard assumptions. The trade-offs
between the convergence accuracy and the number of Byzantine agents in both
linear and sub-linear cases are characterized. In simulation, the effectiveness
and practicability of the proposed algorithms are manifested via resolving a
sparse machine-learning problem over multi-agent CPSs under various Byzantine
attacks.Comment: 14 pages, 0 figure
On classification of singular matrix difference equations of mixed order
This paper is concerned with singular matrix difference equations of mixed
order. The existence and uniqueness of initial value problems for these
equations are derived, and then the classification of them is obtained with a
similar classical Weyl's method by selecting a suitable quasi-difference. An
equivalent characterization of this classification is given in terms of the
number of linearly independent square summable solutions of the equation. The
influence of off-diagonal coefficients on the classification is illustrated by
two examples. In particular, two limit point criteria are established in terms
of coefficients of the equation.Comment: 27 page
Real-space Formalism for the Euler Class and Fragile Topology in Quasicrystals and Amorphous Lattices
We propose a real-space formalism of the topological Euler class, which
characterizes the fragile topology of two-dimensional systems with real wave
functions. This real-space description is characterized by local Euler markers
whose macroscopic average coincides with the Euler number, and it applies
equally well to periodic and open boundary conditions for both crystals and
noncrystalline systems. We validate this by diagnosing topological phase
transitions in clean and disordered crystalline systems with the reality
endowed by the space-time inversion symmetry . Furthermore,
we demonstrated the topological Euler phases in quasicrystals and even in
amorphous lattices lacking any spatial symmetries. Our work not only provides a
local characterization of the fragile topology but also significantly extends
its territory beyond -symmetric crystalline materials.Comment: 41 pages,8 figure
A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction
In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup
Privacy-Preserving Push-Pull Method for Decentralized Optimization via State Decomposition
Distributed optimization is manifesting great potential in multiple fields,
e.g., machine learning, control, and resource allocation. Existing
decentralized optimization algorithms require sharing explicit state
information among the agents, which raises the risk of private information
leakage. To ensure privacy security, combining information security mechanisms,
such as differential privacy and homomorphic encryption, with traditional
decentralized optimization algorithms is a commonly used means. However, this
would either sacrifice optimization accuracy or incur heavy computational
burden. To overcome these shortcomings, we develop a novel privacy-preserving
decentralized optimization algorithm, called PPSD, that combines gradient
tracking with a state decomposition mechanism. Specifically, each agent
decomposes its state associated with the gradient into two substates. One
substate is used for interaction with neighboring agents, and the other
substate containing private information acts only on the first substate and
thus is entirely agnostic to other agents. For the strongly convex and smooth
objective functions, PPSD attains a -linear convergence rate. Moreover, the
algorithm can preserve the agents' private information from being leaked to
honest-but-curious neighbors. Simulations further confirm the results
Projective Lag Synchronization of Delayed Neural Networks Using Intermittent Linear State Feedback
The problem of projective lag synchronization of coupled neural networks with time delay is investigated. By means of the Lyapunov stability theory, an intermittent controller is designed for achieving projective lag synchronization between two delayed neural networks systems. Numerical simulations on coupled Lu neural systems illustrate the effectiveness of the results
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