一类二次半定规划Gauss-Newton方向的存在唯一性

本文在半定规划中的Gauss-Newton搜索方向的基础上研究一类特殊的二次半定规划(QSDP)求解问题,基于矩阵论和和凸规划理论中原始-对偶算法的NT搜索方向将此类二次半定规划问题转化为求解线性半定规划的最小二乘问题,为了验证此理论的可行性本文验证了Gauss-Newton搜索方向在最小二乘问题中的存在性和唯一性.

LOCALIZATION ALGORITHM USING DISTANCE AND ANGLE INFORMATION IN WIRELESS SENSOR NETWORKS

A localization algorithm using distance and angle information is proposed in wireless sensor networks. Assuming that node axial orientations are unknown, all angles are measured to calculate the angle differences between two nodes viewed by the third one. Then, localization problems are formulated as convex optimization ones and all geometric relationships among different nodes in the communication range are transformed into linear or quadratic constraints. If all measurements are accurate, the localization problem can be formulated as linear programming （LP）. Otherwise, by incorporating auxiliary variables, it can be regarded as quadratic programming （QP）. Simulations show the effectiveness of the proposed algorithm.

Priority approach based on quadratic programming model to fuzzy preference relation

We investigate the decision-making problem with a finite set of alternatives,in which the decision information takes the form of a fuzzy preference relation. We develop asimple and practical approach to obtaining the priority vector of a fuzzy preference relation. Theprominent characteristic of the developed approach is that the priority vector can generally beobtained by a simple formula, which is derived from a quadratic programming model. We utilize theconsistency ratio to check the consistency of fuzzy preference relation. If the fuzzy preferencerelation is of unacceptable consistency, then we can return it to the decision maker to reconsiderstructuring a new fuzzy preference relation until the fuzzy preference relation with acceptableconsistency is obtained. We finally illustrate the priority approach by two numerical examples. Thenumerical results show that the developed approach is straightforward, effective, and can easily beperformed on a computer.

APPLICATION OF HYBRID AERO-ENGINE MODEL FOR INTEGRATED FLIGHT/PROPULSION OPTIMAL CONTROL

The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gramming （SQP）. Firstly, a steady-state hybrid aero-engine model is designed in the whole flight envelope with a dramatic enhancement of real-time capability. Secondly, the aero-engine performance seeking control including the maximum thrust mode and the minimum fuel-consumption mode is performed by SQP. Finally, digital simu- lations for cruise and accelerating flight are carried out. Results show that the proposed method improves real- time capability considerably with satisfactory effectiveness of optimization.

An Efficient Partitioning Method in Quadratic Placement

A method of combining the MFFC clustering and hMETIS partitioning based quadratic placement algorithm is proposed. Experimental results show that it can gain good results but consume long running time.In order to cut down the running time,an improved MFFC clustering method (IMFFC) based Q-place algorithm is proposed.Comparing with the combining clustering and partitioning based method,it is much faster but with a little increase in total wire length.

Simultaneous Partitioning and Scheduling Algorithm for Clustered Architecture

Clustered architecture is selected for high level synthesis,and a simultaneous partitioning and scheduling algorithm are proposed.Compared with traditional methods,circuit performance can be improved.Experiments show the efficiency of the method.

Parameter estimation methods in generalized weighted functional mean combining forecasting model

A kind of combining forecasting model based on the generalized weighted functional mean is proposed. Two kinds of parameter estimation methods with its weighting coefficients using the algorithm of quadratic programming are given. The efficiencies of this combining forecasting model and the comparison of the two kinds of parameter estimation methods are demonstrated with an example. A conclusion is obtained, which is useful for the correct application of the above methods.

对世界游泳冠军张雨霏100m蝶泳技术的分析与研究

张雨霏在 2020 年游泳冠军赛中 4x100 米男女混合泳接力中游蝶泳一棒并打破世界纪录,本文通过水下技术录像,数理统计,专家访谈等研究方法对张雨霏蝶泳动作进行技术分析,经过比赛时的视频分析得出结论,张雨霏 100 米蝶泳第一个 50 米 20 次动作,第二个 50 米 22 次动作,前 50 米出发后前四次动作为闭息,之后平均两下一呼吸。后 50 米为两呼一憋的技术。在游程中动作轻松自然,同时双手移臂迅速,身体起伏明显,动作连贯节奏感强。

Authorized Identity-Based Public Cloud Storage Auditing Scheme with Hierarchical Structure for Large-Scale User Groups

Identity-based public cloud storage auditing schemes can check the integrity of cloud data, and reduce the complicated certificate management. In such a scheme, one Private Key Generator(PKG) is employed to authenticate the identity and generate private keys for all users, and one Third Party Auditor(TPA) is employed to by users to check the integrity of cloud data. This approach is undesirable for large-scale users since the PKG and the TPA might not be able to afford the heavy workload. To solve the problem, we give a hierarchical Private Key Generator structure for large-scale user groups, in which a root PKG delegates lower-level PKGs to generate private keys and authenticate identities. Based on the proposed structure, we propose an authorized identity-based public cloud storage auditing scheme, in which the lowest-level PKGs play the role of TPA, and only the authorized lowest-level PKGs can represent users in their domains to check cloud data's integrity. Furthermore, we give the formal security analysis and experimental results, which show that our proposed scheme is secure and efficient.

Cooperative merging control strategy of connected and automated vehicles on highways

To improve traffic performance when on-ramp vehicles merge into the mainstream,a collaborative merging control strategy is proposed to determine the merging sequence and trajectory control of vehicles.Merging trajectory planning takes the minimization of vehicle acceleration as the optimization objective.Either the variational method or the quadratic programming method is utilized to determine arrival time,optimal time and control variables for each vehicle.As a supplement,the adaptive cruise control(ACC)model is used to calculate each control variable in each time interval on special occasions.Simulation results show that the cooperative merging control strategy outperforms the optimal control strategy.The root mean square(RMS)of acceleration and the root mean square error(RMSE)of time headway are significantly decreased,with the reductions up to 90.1%and 25.2%,respectively.Under the cooperative control strategy,the difference between the average speed and desired speed consistently approaches zero.In addition,few or no collisions occur.To conclude,the proposed strategy favours the improvements in passenger comfort,traffic efficiency,traffic stability and safety around highway on-ramps.

A novel PID controller tuning method based on optimization technique

An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) based algorithm that derives its search directions by solving quadratic programming(QP) subproblems via an infeasible interior point method(IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver.The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems.To assess the performance of the proposed method,a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin(GPM) method and Ziegler-Nichols(ZN) method.The results reveal that,for both step and impulse response tests,the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time,settling-time and remarkably lower overshoot compared to GPM and ZN methods,and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.

An Interval Maximum Entropy Method for Quadratic Programming Problem

With the idea of maximum entropy function and penalty function methods, we transform the quadratic programming problem into an unconstrained differentiable optimization problem, discuss the interval extension of the maximum entropy function, provide the region deletion test rules and design an interval maximum entropy algorithm for quadratic programming problem. The convergence of the method is proved and numerical results are presented. Both theoretical and numerical results show that the method is reliable and efficient.

WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION

Wavelet, a powerful tool for signal processing, can be used to approximate the target func-tion. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with bet-ter sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target fun-citon with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation ex-periment show the feasibility and validity of wavelet kernel support vector machines.

A Tabu Search Algorithm for Quadratic 0-1 Programming Problem

In this paper,quadratic 0-1 programming problem (I) is considered, in terms of its features quadratic 0-1 programming problem is solved by linear approxity heurstic algrothm and a developed tabu search ahgrothm .

Upper bound solutions of stability factor of shallow tunnels in saturated soil based on strength reduction technique

Based on the upper bound theorem of limit analysis,the factor of safety for shallow tunnel in saturated soil is calculated in conjunction with the strength reduction technique.To analyze the influence of the pore pressure on the factor of safety for shallow tunnel,the power of pore pressure is regarded as a power of external force in the energy calculation.Using the rigid multiple-block failure mechanism,the objective function for the factor of safety is constructed and the optimal solutions are derived by employing the sequential quadratic programming.According to the results of optimization calculation,the factor of safety of shallow tunnel for different pore pressure coefficients and variational groundwater tables are obtained.The parameter analysis shows that the pore pressure coefficient and the location of the groundwater table have significant influence on the factor of safety for shallow tunnel.

Construction of improved rigid blocks failure mechanism for ultimate bearing capacity calculation based on slip-line field theory

Based on the slip-line field theory, a two-dimensional slip failure mechanism with mesh-like rigid block system was constructed to analyze the ultimate bearing capacity problems of rough foundation within the framework of the upper bound limit analysis theorem. In the velocity discontinuities in transition area, the velocity changes in radial and tangent directions are allowed. The objective functions of the stability problems of geotechnical structures are obtained by equating the work rate of external force to internal dissipation along the velocity discontinuities, and then the objective functions are transformed as an upper-bound mathematic optimization model. The upper bound solutions for the objective functions are obtained by use of the nonlinear sequential quadratic programming and interior point method. From the numerical results and comparative analysis, it can be seen that the method presented in this work gives better calculation results than existing upper bound methods and can be used to establish the more accurate plastic collapse load for the ultimate bearing capacity of rough foundation.

A Comparison of Arithmetic Operations for Dynamic Process Optimization Approach

A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming(rSQP)simultaneous approach with respect to equality constrained optimization problems is presented.Through the detail comparison of arithmetic operations,it is concluded that the average iteration number within differential algebraic equations(DAEs)integration of quasi-sequential approach could be regarded as a criterion.One formula is given to calculate the threshold value of average iteration number.If the average iteration number is less than the threshold value,quasi-sequential approach takes advantage of rSQP simultaneous approach which is more suitable contrarily.Two optimal control problems are given to demonstrate the usage of threshold value.For optimal control problems whose objective is to stay near desired operating point,the iteration number is usually small.Therefore,quasi-sequential approach seems more suitable for such problems.

A Preliminary Application of the Differential Evolution Algorithm to Calculate the CNOP

A projected skill is adopted by use of the differential evolution (DE) algorithm to calculate a conditional nonlinear optimal perturbation (CNOP). The CNOP is the maximal value of a constrained optimization problem with a constraint condition, such as a ball constraint. The success of the DE algorithm lies in its ability to handle a non-differentiable and nonlinear cost function. In this study, the DE algorithm and the traditional optimization algorithms used to obtain the CNOPs are compared by analyzing a theoretical grassland ecosystem model and a dynamic global vegetation model. This study shows that the CNOPs generated by the DE algorithm are similar to those by the sequential quadratic programming (SQP) algorithm and the spectral projected gradients (SPG2) algorithm. If the cost function is non-differentiable, the CNOPs could also be caught with the DE algorithm. The numerical results suggest the DE algorithm can be employed to calculate the CNOP, especially when the cost function is non-differentiable.

A quadratic programming method for optimal degree reduction of Bézier curves with G^1-continuity

This paper presents a quadratic programming method for optimal multi-degree reduction of B6zier curves with G^1-continuity. The L2 and I2 measures of distances between the two curves are used as the objective functions. The two additional parameters, available from the coincidence of the oriented tangents, are constrained to be positive so as to satisfy the solvability condition. Finally, degree reduction is changed to solve a quadratic problem of two parameters with linear constraints. Applications of degree reduction of Bezier curves with their parameterizations close to arc-length parameterizations are also discussed.

A new hybrid algorithm for global optimization and slope stability evaluation

A new hybrid optimization algorithm was presented by integrating the gravitational search algorithm （GSA） with the sequential quadratic programming （SQP）, namely GSA-SQP, for solving global optimization problems and minimization of factor of safety in slope stability analysis. The new algorithm combines the global exploration ability of the GSA to converge rapidly to a near optimum solution. In addition, it uses the accurate local exploitation ability of the SQP to accelerate the search process and find an accurate solution. A set of five well-known benchmark optimization problems was used to validate the performance of the GSA-SQP as a global optimization algorithm and facilitate comparison with the classical GSA. In addition, the effectiveness of the proposed method for slope stability analysis was investigated using three ease studies of slope stability problems from the literature. The factor of safety of earth slopes was evaluated using the Morgenstern-Price method. The numerical experiments demonstrate that the hybrid algorithm converges faster to a significantly more accurate final solution for a variety of benchmark test functions and slope stability problems.