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We consider the problem of modeling data matrices with locally low rank (LLR) structure, a generalization of the popular low rank structure widely used in a variety of real world application domains ranging from medical imaging to recommendation systems. While LLR modeling has been found to be promising in real world application domains, limited progress has been made on the design of scalable algorithms...
This article aims to the problems that the particle swarm optimization (PSO) algorithm has slow convergence and easy to fall into local optimum, provides an improved adaptive particle swarm optimization algorithm based on Levy flight mechanism (LFAPSO). The long jumps of Levy flight will step out of the local optimum in the local search. The convergence speed and accuracy of the LFAPSO algorithm are...
We consider the following basic problem: given an n-variate degree-d homogeneous polynomial f with real coefficients, compute a unit vector x in R{\string^}n that maximizes abs(f(x)). Besides its fundamental nature, this problem arises in diverse contexts ranging from tensor and operator norms to graph expansion to quantum information theory. The homogeneous degree-2 case is efficiently solvable as...
We study streaming principal component analysis (PCA), that is to find, in O(dk) space, the top k eigenvectors of a d× d hidden matrix \bold \Sigma with online vectors drawn from covariance matrix \bold \Sigma.We provide global convergence for Ojas algorithm which is popularly used in practice but lacks theoretical understanding for k≈1. We also provide a modified variant \mathsf{Oja}^{++}...
Dual methods can handle easily complicated constraints in convex problems, but they have typically slow (sublinear) convergence rate in an average primal point, even when the original problem has smooth strongly convex objective function. Primal projected gradient-based methods achieve linear convergence for constrained, smooth and strongly convex optimization, but it is difficult to implement them,...
Least-squares temporal difference learning (LSTD) has been used mainly for improving the data efficiency of the critic in actor-critic (AC). However, convergence analysis of the resulted algorithms is difficult when policy is changing. In this paper, a new AC method is proposed based on LSTD under discount criterion. The method comprises two components as the contribution: (1) LSTD works in an on-policy...
This paper considers the identification of impulse responses of systems with multiple inputs. An existing technique utilizing signal sets which are correlated with one another is modified to improve its convergence properties. A detailed example is presented to compare the performance between identification using a set of correlated signals and identification using a set of uncorrelated signals. In...
Fuelled by the proliferation of smartphones, wireless traffic has experienced huge growth, which will continue with the emergence of ultra-broadband 5G applications, and exacerbate the capacity strain in cellular networks. Deployment of pico access points, reducing cell sizes and allowing more efficient reuse of limited radio spectrum, provides a powerful approach to cope with traffic hot spots and...
Applications of compressive sensing (CS) theory involve recovering sparse signals by solving ℓ1 norm regularized objective functions. Due to discontinuity of ℓ1-norm, usage of gradient based algorithm for reconstruction of sparse signals is not possible. Different smooth surrogate functions have been used to approximate ℓ1 norm. This article presents a performance comparison of two such surrogate...
The simultaneous perturbation stochastic approximation(SPSA) belongs to the class of iterative gradient-free algorithm. However, because of its slow convergence rate, the experimental effect is not ideal for large-scale problems. In order to accelerate the SPSA algorithm, this paper proposes a parallelized combined direction SPSA algorithm. Gradient directions among the master and slaves are combined...
The accuracy of PV array model is very important for grid connected operation and scheduling of large scale PV system. Based on the measured data of a PV power station, the hybrid artificial fish swarm and frog leaping algorithm is adopted to identify the unknown parameters in the mechanism model of PV array. The hybrid algorithm combines the advantages of the fast convergence of artificial fish swarm...
This paper presents a distributed algorithm to solve an economic dispatch problem, which takes the form of a linearly-constrained resource allocation problem. Distributed gradient-based methods are commonly used to solve problems of this form, which inherit slow convergence. The Newton method is a centralized alternative which uses second-order information to provide faster convergence. However, computing...
This paper describes new algorithms that incorporates the non-uniform norm constraint into the zero-attracting and reweighted modified filtered-x affine projection or pseudo affine projection algorithms for active noise control. The simulations indicate that the proposed algorithms can obtain better performance for primary and secondary paths with various sparseness levels with insignificant numerical...
We present a novel online learning paradigm for nonlinear function estimation based on iterative orthogonal projections in an L2 space reflecting the stochastic property of input signals. An online algorithm is built upon the fact that any finite dimensional subspace has a reproducing kernel, which is given in terms of the Gram matrix of its basis. The basis used in the present study involves multiple...
Due to the asymptotically orthogonal channel, minimum mean square error detection algorithm is near-optimal for uplink massive MIMO systems, but it involves matrix inversion with high complexity. This paper proposes a high-parallelism detection algorithm in an iterative way to avoid the complicated matrix inversion. The parallelism level is analyzed and convergence is proved in detail. The proposed...
This paper considers queue-driven, real-time interference mitigation aiming to maximize wireless network throughput. A best-response power control algorithm is derived from successive convex approximations, and a single power update is performed at each scheduling interval. In the high-SINR regime, the power control algorithm achieves maximum network throughput. Extending the approach to general SINR...
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...
We prove convergence of the projected gradient algorithm with inexact projections when applied to linear inverse problems with constraint sets that are unions of subspaces. Such an algorithm is useful for joint angle and delay estimation in MIMO radar, where classical estimators for angle estimation can be integrated into compressive sensing methods for range estimation.
The most widely used method applied in the context of off-line dynamic demand calibration is Simultaneous Perturbation Stochastic Approximation (SPSA). In the research following the SPSA approach single origin-destination (O-D) demand components were mostly considered as calibration parameters. However, basic SPSA, especially in high dimensions, shows convergence issues, as proven by various authors...
The LMS-type algorithms are effectively used in diverse adaptive filtering applications such as; blind system identification, channel equalization, etc. For some applications such as echo cancellation, which requires a large filter length, a very long time is required to estimate the coefficients via the conventional least-mean-square (LMS) algorithm. Actually, updating the filter coefficients block...
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