A NEW DERIVATIVE FREE OPTIMIZATION METHOD BASED ON CONIC INTERPOLATION MODEL

In this paper, a new derivative free trust region method is developed based on the conic interpolation model for the unconstrained optimization. The conic interpolation model is built by means of the quadratic model function, the collinear scaling formula, quadratic approximation and interpolation. All the parameters in this model are determined by objective function interpolation condition. A new derivative free method is developed based upon this model and the global convergence of this new method is proved without any information on gradient.

Sources of inaccuracy when estimating economically optimum N fertilizer rates

Nitrogen rate trials are often performed to determine the economically optimum N application rate. For this purpose, the yield is modeled as a function of the N application. The regression analysis provides an estimate of the modeled function and thus also an estimate of the economic optimum, Nopt. Obtaining the accuracy of such estimates by confidence intervals for Nopt is subject to the model assumptions. The dependence of these assumptions is a further source of inaccuracy. The Nopt estimate also strongly depends on the N level design, i.e., the area on which the model is fitted. A small area around the supposed Nopt diminishes the dependence of the model assumptions, but prolongs the confidence interval. The investigations of the impact of the mentioned sources on the inaccuracy of the Nopt estimate rely on N rate trials on the experimental field Sieblerfeld (Bavaria). The models applied are the quadratic and the linear-plus-plateau yield regression model.

医院季度收入预测的比较研究

目的 探讨具有季度波动和长期趋势特征事物预测问题的方法论,为卫生领域同类预测问题提供定量决策方法论依据。方法 以某所医院季度收入预测作为实证算例载体,分别用乘积季节SARIMA法、X11-ARIMA法、X11-曲线函数外推法建立简单组合模型以及二次加权组合模型。结果 乘积季节SARIMA模型、X11-ARIMA模型、X11-二次多项式的平均相对误差分别为1.28%、0.96%、1.11%。对以上简单组合模型继续进行深度加权组合,预测误差为0.70%,模型预测性能更优。结论 SARIMA模型、X11-ARIMA模型、X11-二次多项式以及二次组合模型对于局部周期性波动、全局稳定性趋势的时序数据资料拟合效果不错;对于卫生管理领域预测问题有适用性,鉴于多种匹配方法原理不同、信息利用差异大,基于多种方法的二次组合模型取得更优性能。

A CASE STUDY IN THE APPLICATION OF SUPERSATURATED DESIGNS TO COMPUTER EXPERIMENTS

Supersaturated design is essentially a fractional factorial design in which the number of potential effects is greater than the number of runs. In this article, the supersaturated design is applied to a computer experiment through an example of steady current circuit model problem. A uniform mixed-level supersaturated design and the centered quadratic regression model are used. This example shows that supersaturated design and quadratic regression modeling method are very effective for screening effects and building the predictor. They are not only useful in computer experiments but also in industrial and other scientific experiments.

Optimal layout model of feeder automation equipment oriented to the type of fault detection and local action

In feeder automation transformation there are difficulties in equipment and location selection.To help with this,an optimal layout model of feeder automation equipment oriented to the type of fault detection and local action is pro-posed.It analyzes the coordination relationship of the three most common types of automation equipment,i.e.,fault indicator,over-current trip switch and non-voltage trip switch in the fault handling process,and the explicit expres-sions of power outage time caused by a fault on different layouts of the above three types of equipment are given.Given constraints of power supply reliability and the goal of minimizing the sum of equipment-related capital invest-ment and power interruption cost,a mixed-integer quadratic programming model for optimal layout is established,in which the functional failure probability of equipment is linearized using the 3δprinciple in statistics.Finally,the basic characteristics of the proposed model are illustrated by different scenarios on the IEEE RBTS-BUS6 system.It can not only take into account fault location and fault isolation to enhance user power consumption perception,but also can guide precise investment to improve the operational quality and efficiency of a power company.

A Derivative-Free Optimization Algorithm Combining Line-Search and Trust-Region Techniques

The speeding-up and slowing-down(SUSD)direction is a novel direction,which is proved to converge to the gradient descent direction under some conditions.The authors propose the derivative-free optimization algorithm SUSD-TR,which combines the SUSD direction based on the covariance matrix of interpolation points and the solution of the trust-region subproblem of the interpolation model function at the current iteration step.They analyze the optimization dynamics and convergence of the algorithm SUSD-TR.Details of the trial step and structure step are given.Numerical results show their algorithm’s efficiency,and the comparison indicates that SUSD-TR greatly improves the method’s performance based on the method that only goes along the SUSD direction.Their algorithm is competitive with state-of-the-art mathematical derivative-free optimization algorithms.

A TRUST-REGION METHOD FOR NONSMOOTH NONCONVEX OPTIMIZATION

We propose a trust-region type method for a class of nonsmooth nonconvex optimization problems where the objective function is a summation of a(probably nonconvex)smooth function and a(probably nonsmooth)convex function.The model function of our trust-region subproblem is always quadratic and the linear term of the model is generated using abstract descent directions.Therefore,the trust-region subproblems can be easily constructed as well as efficiently solved by cheap and standard methods.When the accuracy of the model function at the solution of the subproblem is not sufficient,we add a safeguard on the stepsizes for improving the accuracy.For a class of functions that can be“truncated”,an additional truncation step is defined and a stepsize modification strategy is designed.The overall scheme converges globally and we establish fast local convergence under suitable assumptions.In particular,using a connection with a smooth Riemannian trust-region method,we prove local quadratic convergence for partly smooth functions under a strict complementary condition.Preliminary numerical results on a family of Ei-optimization problems are reported and demonstrate the eficiency of our approach.

An asymptotically optimal public parking lot location algorithm based on intuitive reasoning

In order to solve the problems of road traffic congestion and the increasing parking time caused by the imbalance of parking lot supply and demand,this paper proposes an asymptotically optimal public parking lot location algorithm based on intuitive reasoning to optimize the parking lot location problem.Guided by the idea of intuitive reasoning,we use walking distance as indicator to measure the variability among location data and build a combinatorial optimization model aimed at guiding search decisions in the solution space of complex problems to find optimal solutions.First,Selective Attention Mechanism(SAM)is introduced to reduce the search space by adaptively focusing on the important information in the features.Then,Quantum Annealing(QA)algorithm with quantum tunneling effect is used to jump out of the local extremum in the search space with high probability and further approach the global optimal solution.Experiments on the parking lot location dataset in Luohu District,Shenzhen,show that the proposed method has improved the accuracy and running speed of the solution,and the asymptotic optimality of the algorithm and its effectiveness in solving the public parking lot location problem are verified.