In this paper,following the method of replacing the lower level problem with its Kuhn-Tucker optimality condition,we transform the nonlinear bilevel programming problem into a normal nonlinear programming problem with...In this paper,following the method of replacing the lower level problem with its Kuhn-Tucker optimality condition,we transform the nonlinear bilevel programming problem into a normal nonlinear programming problem with the complementary slackness constraint condition.Then,we get the penalized problem of the normal nonlinear programming problem by appending the complementary slackness condition to the upper level objective with a penalty.We prove that this penalty function is exact and the penalized problem and the nonlinear bilevel programming problem have the same global optimal solution set.Finally,we propose an algorithm for the nonlinear bilevel programming problem.The numerical results show that the algorithm is feasible and efficient.展开更多
以电梯运行数据在高维空间分布特性为依据,提出一种正负类双超球体支持向量数据描述的电梯故障监测与诊断模型,将实时电梯数据分为健康样本、故障样本和未知漂移异常样本.针对电梯设备老化产生的漂移异常样本判别精度低的问题,将支持向...以电梯运行数据在高维空间分布特性为依据,提出一种正负类双超球体支持向量数据描述的电梯故障监测与诊断模型,将实时电梯数据分为健康样本、故障样本和未知漂移异常样本.针对电梯设备老化产生的漂移异常样本判别精度低的问题,将支持向量数据描述(support vector data description,SVDD)与凸二次双层规划方法结合,形成一种双层支持向量数据描述方法(bilevel-support vector data description,B-SVDD).该方法首先对电梯运行数据进行凸集区间化处理,接下来通过5次迭代更新超球体球心与半径,最后计算数据到正、负球心的距离判别未知漂移异常样本的类别.此外,利用该模型的堆叠形式划分故障样本空间,诊断四类常见的电梯故障.实验结果表明,漂移异常样本的判别最终可以达到98.3%的平均分类准确率,为电梯故障监测与诊断提供一种快速有效的方法.展开更多
In this paper, a new primal-dual interior-point algorithm for convex quadratic optimization （CQO） based on a kernel function is presented. The proposed function has some properties that are easy for checking. These ...In this paper, a new primal-dual interior-point algorithm for convex quadratic optimization （CQO） based on a kernel function is presented. The proposed function has some properties that are easy for checking. These properties enable us to improve the polynomial complexity bound of a large-update interior-point method （IPM） to O（√n log nlog n/e）, which is the currently best known polynomial complexity bound for the algorithm with the large-update method. Numerical tests were conducted to investigate the behavior of the algorithm with different parameters p, q and θ, where p is the growth degree parameter, q is the barrier degree of the kernel function and θ is the barrier update parameter.展开更多
In this paper, we present a large-update interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function. The proposed function is strongly convex. It is not self-regular functi...In this paper, we present a large-update interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function. The proposed function is strongly convex. It is not self-regular function and also the usual logarithmic function. The goal of this paper is to investigate such a kernel function and show that the algorithm has favorable complexity bound in terms of the elegant analytic properties of the kernel function. The complexity bound is shown to be O（√n（logn）2 log e/n）. This bound is better than that by the classical primal-dual interior-point methods based on logarithmic barrier function and in optimization fields. Some computational results recent kernel functions introduced by some authors have been provided.展开更多
基金Supported by the Key Project on Science and Technology of Hubei Provincial Department of Education (D20103001)
文摘In this paper,following the method of replacing the lower level problem with its Kuhn-Tucker optimality condition,we transform the nonlinear bilevel programming problem into a normal nonlinear programming problem with the complementary slackness constraint condition.Then,we get the penalized problem of the normal nonlinear programming problem by appending the complementary slackness condition to the upper level objective with a penalty.We prove that this penalty function is exact and the penalized problem and the nonlinear bilevel programming problem have the same global optimal solution set.Finally,we propose an algorithm for the nonlinear bilevel programming problem.The numerical results show that the algorithm is feasible and efficient.
文摘以电梯运行数据在高维空间分布特性为依据,提出一种正负类双超球体支持向量数据描述的电梯故障监测与诊断模型,将实时电梯数据分为健康样本、故障样本和未知漂移异常样本.针对电梯设备老化产生的漂移异常样本判别精度低的问题,将支持向量数据描述(support vector data description,SVDD)与凸二次双层规划方法结合,形成一种双层支持向量数据描述方法(bilevel-support vector data description,B-SVDD).该方法首先对电梯运行数据进行凸集区间化处理,接下来通过5次迭代更新超球体球心与半径,最后计算数据到正、负球心的距离判别未知漂移异常样本的类别.此外,利用该模型的堆叠形式划分故障样本空间,诊断四类常见的电梯故障.实验结果表明,漂移异常样本的判别最终可以达到98.3%的平均分类准确率,为电梯故障监测与诊断提供一种快速有效的方法.
基金the Foundation of Scientific Research for Selecting and Cultivating Young Excellent University Teachers in Shanghai (Grant No.06XPYQ52)the Shanghai Pujiang Program (Grant No.06PJ14039)
文摘In this paper, a new primal-dual interior-point algorithm for convex quadratic optimization （CQO） based on a kernel function is presented. The proposed function has some properties that are easy for checking. These properties enable us to improve the polynomial complexity bound of a large-update interior-point method （IPM） to O（√n log nlog n/e）, which is the currently best known polynomial complexity bound for the algorithm with the large-update method. Numerical tests were conducted to investigate the behavior of the algorithm with different parameters p, q and θ, where p is the growth degree parameter, q is the barrier degree of the kernel function and θ is the barrier update parameter.
基金Supported by Natural Science Foundation of Hubei Province of China (Grant No. 2008CDZ047)
文摘In this paper, we present a large-update interior-point algorithm for convex quadratic semi-definite optimization based on a new kernel function. The proposed function is strongly convex. It is not self-regular function and also the usual logarithmic function. The goal of this paper is to investigate such a kernel function and show that the algorithm has favorable complexity bound in terms of the elegant analytic properties of the kernel function. The complexity bound is shown to be O（√n（logn）2 log e/n）. This bound is better than that by the classical primal-dual interior-point methods based on logarithmic barrier function and in optimization fields. Some computational results recent kernel functions introduced by some authors have been provided.
