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We propose a new Iteratively Reweighted Least Squares (IRLS) algorithm for the problem of recovering a matrix X ∈ ℝd1 × d2 of rank r ≪ min(d1, d2) from incomplete linear observations, solving a sequence of quadratic problems. The easily implementable algorithm, which we call Harmonic Mean Iteratively Reweighted Least Squares (HM-IRLS), is superior compared to state-of-the-art algorithms for the low-rank...
Recent work has demonstrated the effectiveness of gradient descent for recovering low-rank matrices from random linear measurements in a globally convergent manner. However, their performance is highly sensitive in the presence of outliers that may take arbitrary values, which is common in practice. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against...
Fruit fly optimization algorithm (FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified Fruit Fly Optimization Algorithm (SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value...
Randomized gradient-free algorithms through sequential Gaussian smoothing are proposed for distributed optimization over time-varying random network, where the collective goal of agents is to minimize the sum of locally known cost functions. Each agent has access to its own nonsmooth convex function, constrained to a commonly known convex set. Based on sequential Gaussian smoothing of the objective...
This paper presents a novel nonlinear adaptive filter method, namely, Hammerstein adaptive filter with single feedback under minimum mean square error (HAF-SF-MMSE). A single delayed output is incorporated into the estimation of the current output based on minimum mean square error criterion, and therefore the history information of output is considered. Moreover, hybrid learning rates and adaptive...
A novel differential evolution algorithm is proposed for constrained optimization problems (COPs). The proposed algorithm combines the ideas between the self-adaptive differential evolution algorithm (JDE) and simple penalty function method (SPFM). Simulation results on the bump problem show that the solutions of the new algorithm is better than those of the algorithms in the almost exiting literature...
This paper studies the consensus based Kalman filtering problem for discrete-time linear systems in sensor networks. Considering the fact that just part of sensors in the network can measure the target, the filtering algorithms of the sensors are assigned differently according to the availability to get the direct measurements. For the sensors that can directly get the measurement outputs, we call...
The present paper develops a distributed protocol solving the distributed optimization problem for multi-agent systems with the discrete-time dynamics under Markovian switching topologies. Both the completely known probabilities and partially unknown probabilities in the transition matrices are taken into account. Through the proper coordination of transformation, the optimization under consideration...
We investigate the problem of distributed source seeking with velocity actuated and force actuated vehicles by developing distributed Kiefer-Wolfowitz algorithm. First, based on stochastic approximation algorithm with expanding truncations, we present the distributed Kiefer-Wolfowitz algorithm, in which two noisy observations of each agent's objective function is used to estimate its gradient and...
Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e.g. Bayesian neural networks. Algorithms that implement adaptive ICA converge slower than their nonadaptive counterparts, however, they are capable of tracking changes in underlying distributions of input features. This intrinsically...
The consensus algorithm is proposed to solve the bounded consensus tracking problems of leader-following multi-agent systems under directed networks, where the control input of an agent can only use the information measured at the sampling instants from its neighbors or the virtual leader. Based on the delay decomposition approach, the augmented matrix method and the frequency domain analysis, the...
In the distributed optimization, multiple agents aim to minimize the average of all local cost functions corresponding to one decision variable. Recently, the resilient algorithms for distributed optimization against attacks have received some attention, where it is assumed that the maximum number of tolerable attacks is strictly limited by the network connectivity. To relax this assumption, in this...
In this paper, we investigate a distributed Nash equilibrium seeking problem for a class of aggregative games that the strategic interaction is characterized by a sum of nonlinear mapping of heterogeneous local decisions. We consider non-quadratic local cost functions and constrained strategy sets. We propose a novel continuous-time distributed algorithm for equilibrium seeking based on dynamic average...
In this paper we revisit the well known and popular Normalized Subband Adaptive Filter (NSAF). Based on an analysis of the algorithm in the mean and using an analysis strategy presented in [1], we find that the NSAF can be seen as a Richardson iteration applied to a preconditioned augmented Wiener-Hopf equation. This equation is formulated in such a way that its convergence speed can be predicted...
This paper develops a distributed stochastic subgrandient-based support vector machine algorithm when training data to train support vector machines are distributed in the network. In this situation, all the data are decentralized stored and unavailable to all agents and each agent has to make its own update based on its computation and communication with neighbors. With mild connectivity conditions,...
Neural network algorithms on MCA (minor component analysis) are of importance in signal processing. Coupled algorithm can mitigate the speed-stability problem which exists in most non-coupled algorithms. Although some coupled algorithms have been proposed so far, there exists complex computation in them. In this paper, based on a novel information criterion and by modifying the Newton's method, we...
This paper investigates an event-triggered distributed cooperative learning (DCL) algorithm using radial basis function networks (RBFNs), where training samples are often extremely large-scale, high-dimensional and located on distributed nodes over strongly connected and weight-balanced networks. The algorithm is based on Zero-Gradient-Sum (ZGS) distributed optimization strategy and works in a fully...
The Ethylene cracking is highly nonlinear, complex process with many constraints because of the complex running state of cracking furnace groups. For the solutions to multi-objective problems of yield, cost and benefits, this paper puts forward a biogeography-based multi-objective optimization algorithm with hybrid migration (BBMOHM). The hybrid migration strategy combines the self-adaptive migration...
With the development of mobile Internet technology and smart mobile devices, campus path navigation as a new trend have taken more attention. In this paper, we propose a new method based on quantum ant colony algorithm for campus path navigation. Quantum evolution algorithm has high effectiveness for solving combinational optimization problem. We combine ant colony algorithm with quantum evolution...
Large scale monitoring systems require reliable and efficient in-network information extraction mechanisms able to effectively track events at the field level. The study of consensus algorithms for distributed data processing has gained a lot of interest in the last decade. Average consensus algorithms used for decentralized sensor fusion in wireless sensor networks, iteratively compute the global...
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