EFFICIENT NONNEGATIVE MATRIX FACTORIZATION VIA MODIFIED MONOTONE BARZILAI-BORWEIN METHOD WITH ADAPTIVE STEP SIZES STRATEGY

In this paper,we develop an active set identification technique.By means of the active set technique,we present an active set adaptive monotone projected Barzilai-Borwein method(ASAMPBB)for solving nonnegative matrix factorization(NMF)based on the alternating nonnegative least squares framework,in which the Barzilai-Borwein(BB)step sizes can be adaptively picked to get meaningful convergence rate improvements.To get optimal step size,we take into account of the curvature information.In addition,the larger step size technique is exploited to accelerate convergence of the proposed method.The global convergence of the proposed method is analysed under mild assumption.Finally,the results of the numerical experiments on both synthetic and real-world datasets show that the proposed method is effective.

An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

A newly proposed competent population-based optimization algorithm called RUN,which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism,has gained wider interest in solving optimization problems.However,in high-dimensional problems,the search capabilities,convergence speed,and runtime of RUN deteriorate.This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN.Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms.Unlike the original RUN where population size is fixed throughout the search process,Adaptive-RUN automatically adjusts population size according to two population size adaptation techniques,which are linear staircase reduction and iterative halving,during the search process to achieve a good balance between exploration and exploitation characteristics.In addition,the proposed methodology employs an adaptive search step size technique to determine a better solution in the early stages of evolution to improve the solution quality,fitness,and convergence speed of the original RUN.Adaptive-RUN performance is analyzed over 23 IEEE CEC-2017 benchmark functions for two cases,where the first one applies linear staircase reduction with adaptive search step size(LSRUN),and the second one applies iterative halving with adaptive search step size(HRUN),with the original RUN.To promote green computing,the carbon footprint metric is included in the performance evaluation in addition to runtime and fitness.Simulation results based on the Friedman andWilcoxon tests revealed that Adaptive-RUN can produce high-quality solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent metaheuristics.Therefore,with its higher computation efficiency,Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.

Source Quantitative Identification by Reference-Based Cubic Blind Deconvolution Algorithm

The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information.However,the separation performance depends largely on the construction of reference signals.To improve the robustness of the semi-blind deconvolution algorithm to the reference signals and the convergence speed,the reference-based cubic blind deconvolution algorithm is proposed in this paper.The proposed algorithm can be combined with the contribution evaluation to provide trustworthy guidance for suppressing satellite micro-vibration.The normalized reference-based cubic contrast function is proposed and the validity of the new contrast function is theoretically proved.By deriving the optimal step size of gradient iteration under the new contrast function,we propose an efficient adaptive step optimization method.Furthermore,the contribution evaluation method based on vector projection is presented to implement the source contribution evaluation.Numerical simulation analysis is carried out to validate the availability and superiority of this method.Further tests given by the simulated satellite experiment and satellite ground experiment also confirm the effectiveness.The signals of control moment gyroscope and flywheel were extracted,respectively,and the contribution evaluation of vibration sources to the sensitive load area was realized.This research proposes a more accurate and robust algorithm for the source separation and provides an effective tool for the quantitative identification of the mechanical vibration sources.

An optimized time-adaptive aerothermal coupling calculation method for aerothermal analysis of disc cavity system

In order to further achieve the balance between the calculation accuracy and efficiency of the transient analysis of the aero-engine disc cavity system,an Optimized Time-adaptive Aerother-mal Coupling calculation(OTAC)method has been proposed.It combines one-dimensional tran-sient calculation of air system,Conventional Sequence Staggered(CSS)method,Time-adaptive Aerothermal Coupling calculation(TAC)method and differential evolution optimization algorithm to obtain an efficient and high-precision aerothermal coupling calculation method of air system.Considering both the heat conduction in the solid domain and the flow in the fluid domain as unsteady states in the OTAC,the interaction of fluid-solid information within a single coupling time step size was implemented based on the CSS method.Furthermore,the coupling time step size was automatically adjusted with the number of iterations by using the Proportional-Integral-Deri vative(PID)controller.Results show that when compared with the traditional loosely coupling method with a fixed time step size,the computational accuracy and efficiency of the OTAC method are improved by 8.9%and 30%,respectively.Compared with the tight coupling calculation,the OTAC method can achieve a speedup of 1 to 2 orders of magnitude,while the calculation error is maintained within 6.1%.

An algorithm of adaptive step size factor in adaptive filtering

In this paper a model of transversal filter is presented to study the adaptive match of the time variant channel. The least mean square error filtering method is used to obtain the weighting coefficients of the filter. With the purpose of speeding up the convergence of the iteration equation of adaptive filtering, an adaptive factor of the iteration step size μa is derived in this paper. The result of computer simulation shows that in the case of using adaptive μa, the convergence speed of the iteration equation is increased 2 times approximately in comparison with constant μ1. The study suggests that the adaptive filter with adaptive μa have the performance to follow the change of time-variant characteristics of the channel.

An efficient equivariant adaptive separation via independence algorithm for acoustical source separation and identification

To balance the convergence rate and steadystate error of blind source separation(BSS) algorithms, an efficient equivariant adaptive separation via independence(Efficient EASI) algorithm is proposed based on separating indicator, which was derived from the convergence condition of EASI, and can be used to evaluate the separation degree of separated signals. Furthermore, a nonlinear monotone increasing function between suitable step sizes and separating indicator is constructed to adaptively adjust step sizes, and forgetting factor is employed to weaken effects of data at the initial stage. Numerical case studies and experimental studies on a test bed with shell structures are provided to validate the efficiency improvement of the proposed method. This study can benefit for vibration & acoustic monitoring and control, and machinery condition monitoring and fault diagnosis.

GRID-INDEPENDENT CONSTRUCTION OF MULTISTEP METHODS

A new polynomial formulation of variable step size linear multistep methods is pre- sented, where each k-step method is characterized by a fixed set of k - 1 or k parameters. This construction includes all methods of maximal order （p = k for stiff, and p = k ＋ 1 for nonstiff problems）. Supporting time step adaptivity by construction, the new formulation is not based on extending classical fixed step size methods; instead classical methods are obtained as fixed step size restrictions within a unified framework. The methods are imple- mented in MATLAB, with local error estimation and a wide range of step size controllers. This provides a platform for investigating and comparing different multistep method in realistic operational conditions. Computational experiments show that the new multi- step method construction and implementation compares favorably to existing software, although variable order has not yet been included.