A GREEDY ALGORITHM FOR SPARSE PRECISION MATRIX APPROXIMATION

Precision matrix estimation is an important problem in statistical data analysis.This paper proposes a sparse precision matrix estimation approach,based on CLIME estimator and an efficient algorithm GISSP that was originally proposed for li sparse signal recovery in compressed sensing.The asymptotic convergence rate for sparse precision matrix estimation is analyzed with respect to the new stopping criteria of the proposed GISSP algorithm.Finally,numerical comparison of GISSP with other sparse recovery algorithms,such as ADMM and HTP in three settings of precision matrix estimation is provided and the numerical results show the advantages of the proposed algorithm.

May 2022 Greedy Kaczmarz Algorithm Using Optimal Intermediate Projection Technique for Coherent Linear Systems

The Kaczmarz algorithm is a common iterative method for solving linear systems.As an effective variant of Kaczmarz algorithm,the greedy Kaczmarz algorithm utilizes the greedy selection strategy.The two-subspace projection method performs an optimal intermediate projection in each iteration.In this paper,we introduce a new greedy Kaczmarz method,which give full play to the advantages of the two improved Kaczmarz algorithms,so that the generated iterative sequence can exponentially converge to the optimal solution.The theoretical analysis reveals that our algorithm has a smaller convergence factor than the greedy Kaczmarz method.Experimental results confirm that our new algorithm is more effective than the greedy Kaczmarz method for coherent systems and the two-subspace projection method for appropriate scale systems.

Mesh deformation on 3D complex configurations using multistep radial basis functions interpolation

The Radial Basis Function（RBF） method with data reduction is an effective way to perform mesh deformation. However, for large deformations on meshes of complex aerodynamic configurations, the efficiency of the RBF mesh deformation method still needs to be further improved to fulfill the demand of practical application. To achieve this goal, a multistep RBF method based on a multilevel subspace RBF algorithm is presented to further improve the efficiency of the mesh deformation method in this research. A whole deformation is divided into a series of steps, and the supporting radius is adjusted in accordance with the maximal displacement error. Furthermore, parallel computing is applied to the interpolation to enhance the efficiency. Typical deformation problems of the NASA Common Research Model（CRM） configuration, the DLR-F6 wing-body-nacellepylon configuration, and the DLR-F11 high-lift configuration are tested to verify the feasibility of this method. Test results show that the presented multistep RBF mesh deformation method is efficient and robust in dealing with large deformation problems over complex geometries.

Energy-efficient trajectory planning for amulti-UAV-assisted mobile edge computing system

We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things devices.Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs.This problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and SPs.To tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three phases.In the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time.In the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same UAV.In the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each cluster.Compared with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales.

Data-driven sensor placement for efficient thermal field reconstruction

Complete temperature field estimation from limited local measurements is widely desired in many industrial and scientific applications of thermal engineering. Since the sensor configuration dominates the reconstruction performance, some progress has been made in designing sensor placement methods. But these approaches remain to be improved in terms of both accuracy and efficiency due to the lack of comprehensive schemes and efficient optimization algorithms. In this work, we develop a datadriven sensor placement framework for thermal field reconstruction. Specifically, we first tailor the low-dimensional model from the prior thermal maps to represent the high-dimensional temperature distribution states by virtue of proper orthogonal decomposition technique. Then, on such subspace, a recursive greedy algorithm with determinant maximization as the objective function is developed to optimize the sensor placement configuration. Furthermore, we find that the same sensor configuration can be yielded faster by the standard procedures of column-pivoted QR factorization, which allows concise software implementation with readily available function packages. When the sensor locations are determined, we advocate using the databased closed-form estimator to minimize the reconstruction error. Real-time thermal monitoring on the multi-core processor is employed as the case to demonstrate the effectiveness of the proposed methods for thermal field reconstruction. Extensive evaluations are conducted on simulation or experimental datasets of three processors with different architectures. The results show that our method achieves state-of-the-art reconstruction performance while possessing the lowest computational complexity when compared with the existing methods.

Modeling chunk-based content placement in information centric networking

Content-centric networking （CCN） proposes a content-centric paradigm which changes the waist hourglass from Internet protocol （IP） to content chunk. In this paper, based on content chunks, an optimization model of minimizing the total delay time in information centric networking （ICN） is established, and branch-and-bound method and greedy （BG） algorithm is proposed to get the content placement method. As the multipath is natural supported in CCN, chunk-based content placement can decline delay time obviously, even it would increase the calculation amount which can be solved easily by the node＇s capacity. Simulation results indicate that the chunk-based content placement scheme is better than the single-based cache policy on the network total delay time, and the best number of each content chunk split is decided by the link density and the number of the nodes in the network.