A polynomial interior-point algorithm is presented for monotone linear complementarity problem （MLCP） based on a class of kernel functions with the general barrier term, which are called general kernel functions. Un...A polynomial interior-point algorithm is presented for monotone linear complementarity problem （MLCP） based on a class of kernel functions with the general barrier term, which are called general kernel functions. Under the mild conditions for the barrier term, the complexity bound of algorithm in terms of such kernel function and its derivatives is obtained. The approach is actually an extension of the existing work which only used the specific kernel functions for the MLCP.展开更多
We will give the definition of the linear kernel of boolean functions and prove that, by a reversible linear transformation, any linear structure boolean function can be transformed into a boolean function which is li...We will give the definition of the linear kernel of boolean functions and prove that, by a reversible linear transformation, any linear structure boolean function can be transformed into a boolean function which is linear to some variables, is non-relative to some variables and is of non-linear structure to other variables; any Partially-Bent Function can be transformed into a boolean function which is linear to some variables, is nonrelativeto some variables ans is bent to other variables. We will also discuss the Walsh Spectral Characterization of Partially-Bent Functions.展开更多
In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barr...In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barrier term. Iteration bounds both for large-and small-update methods are derived, namely, O（nlog（n/c）） and O（√nlog（n/ε））. This new kernel function has simple algebraic expression and the proximity function has not been used before. Analogous to the classical logarithmic kernel function, our complexity analysis is easier than the other pri- mal-dual interior-point methods based on logarithmic barrier functions and recent kernel functions.展开更多
Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlin...Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.展开更多
针对行人再识别过程中存在获取的训练样本较少,真实样本分布不一定线性可分和算法识别率低的问题,提出基于卡方核的正则化线性判别分析行人再识别算法(KRLDA,kemel regularized linear discriminant analysis)。该算法首先利用核函数将...针对行人再识别过程中存在获取的训练样本较少,真实样本分布不一定线性可分和算法识别率低的问题,提出基于卡方核的正则化线性判别分析行人再识别算法(KRLDA,kemel regularized linear discriminant analysis)。该算法首先利用核函数将样本从线性不可分的原始空间映射到线性可分的高维特征空间,然后在高维空间中构造描述数据之间邻近关系的散度矩阵,再利用正则化线性判别分析获得高维到低维空间的投影矩阵,使得数据在低维空间能够保持高维空间的可分性,从而提升行人再识别算法的识别率。在VIPeR、iLIDS、CAVIAR和3DPeS数据集上,实验结果表明所提出的算法具有较高识别率。展开更多
基金supported by the National Natural Science Foundation of China (Grant No.10771133)the Shanghai Pujiang Program (Grant No.06PJ14039)
文摘A polynomial interior-point algorithm is presented for monotone linear complementarity problem （MLCP） based on a class of kernel functions with the general barrier term, which are called general kernel functions. Under the mild conditions for the barrier term, the complexity bound of algorithm in terms of such kernel function and its derivatives is obtained. The approach is actually an extension of the existing work which only used the specific kernel functions for the MLCP.
文摘We will give the definition of the linear kernel of boolean functions and prove that, by a reversible linear transformation, any linear structure boolean function can be transformed into a boolean function which is linear to some variables, is non-relative to some variables and is of non-linear structure to other variables; any Partially-Bent Function can be transformed into a boolean function which is linear to some variables, is nonrelativeto some variables ans is bent to other variables. We will also discuss the Walsh Spectral Characterization of Partially-Bent Functions.
基金Supported by the Natural Science Foundation of Hubei Province (2008CDZD47)
文摘In this paper, we design a primal-dual interior-point algorithm for linear optimization. Search directions and proximity function are proposed based on a new kernel function which includes neither growth term nor barrier term. Iteration bounds both for large-and small-update methods are derived, namely, O（nlog（n/c）） and O（√nlog（n/ε））. This new kernel function has simple algebraic expression and the proximity function has not been used before. Analogous to the classical logarithmic kernel function, our complexity analysis is easier than the other pri- mal-dual interior-point methods based on logarithmic barrier functions and recent kernel functions.
基金Project supported by the National Outstanding Youth ScienceFoundation of China (No. 60025308) and the Teach and ResearchAward Program for Outstanding Young Teachers in Higher EducationInstitutions of MOE, China
文摘Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis Function (RBF) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.
文摘针对行人再识别过程中存在获取的训练样本较少,真实样本分布不一定线性可分和算法识别率低的问题,提出基于卡方核的正则化线性判别分析行人再识别算法(KRLDA,kemel regularized linear discriminant analysis)。该算法首先利用核函数将样本从线性不可分的原始空间映射到线性可分的高维特征空间,然后在高维空间中构造描述数据之间邻近关系的散度矩阵,再利用正则化线性判别分析获得高维到低维空间的投影矩阵,使得数据在低维空间能够保持高维空间的可分性,从而提升行人再识别算法的识别率。在VIPeR、iLIDS、CAVIAR和3DPeS数据集上,实验结果表明所提出的算法具有较高识别率。
