Principal component analysis(PCA)is one of the most popular multivariate data analysis techniques for dimension reduction and data mining,and is widely used in many fields ranging from industry and biology to finance ...Principal component analysis(PCA)is one of the most popular multivariate data analysis techniques for dimension reduction and data mining,and is widely used in many fields ranging from industry and biology to finance and social development.When working on big data,it is of great necessity to consider the online version of PCA,in which only a small subset of samples could be stored.To handle the online PCA problem,Oja(1982)presented the stochastic power method under the assumption of zero-mean samples,and there have been lots of theoretical analysis and modified versions of this method in recent years.However,a common circumstance where the samples have nonzero mean is seldom studied.In this paper,we derive the convergence rate of a nonzero-mean version of Oja’s algorithm with diminishing stepsizes.In the analysis,we succeed in handling the dependency between each iteration,which is caused by the updated mean term for data centering.Furthermore,we verify the theoretical results by several numerical tests on both artificial and real datasets.Our work offers a way to deal with the top-1 online PCA when the mean of the given data is unknown.展开更多
In this paper, we consider the problem of finding sparse solutions for underdetermined systems of linear equations, which can be formulated as a class of L_0 norm minimization problem. By using the least absolute resi...In this paper, we consider the problem of finding sparse solutions for underdetermined systems of linear equations, which can be formulated as a class of L_0 norm minimization problem. By using the least absolute residual approximation, we propose a new piecewis, quadratic function to approximate the L_0 norm.Then, we develop a piecewise quadratic approximation(PQA) model where the objective function is given by the summation of a smooth non-convex component and a non-smooth convex component. To solve the(PQA) model,we present an algorithm based on the idea of the iterative thresholding algorithm and derive the convergence and the convergence rate. Finally, we carry out a series of numerical experiments to demonstrate the performance of the proposed algorithm for(PQA). We also conduct a phase diagram analysis to further show the superiority of(PQA) over L_1 and L_(1/2) regularizations.展开更多
Marginal risk represents the risk contribution of an individual asset to the risk of the entire portfolio In this paper, we investigate the portfolio selection problem with direct marginal risk control in a linear con...Marginal risk represents the risk contribution of an individual asset to the risk of the entire portfolio In this paper, we investigate the portfolio selection problem with direct marginal risk control in a linear conic programming framework. ＇The optimization model involved is a nonconvex quadratically constrained quadratic programming （QCQP） problem. We first transform the QCQP problem into a linear conic programming problem, and then approximate the problem by semidefinite programming （SDP） relaxation problems over some subrectangles. In order to improve the lower bounds obtained from the SDP relaxation problems, linear and quadratic polar cuts are introduced for designing a branch-and-cut algorithm, that may yield an e -optimal global solution （with respect to feasibility and optimality） in a finite number of iterations. By exploring the special structure of the SDP relaxation problems, an adaptive branch-and-cut rule is employed to speed up the computation. The proposed algorithm is tested and compared with a known method in the literature for portfolio selection problems with hundreds of assets and tens of marginal risk control constraints.展开更多
基金supported by National Natural Science Foundation of China(Grant No.11771275)。
文摘Principal component analysis(PCA)is one of the most popular multivariate data analysis techniques for dimension reduction and data mining,and is widely used in many fields ranging from industry and biology to finance and social development.When working on big data,it is of great necessity to consider the online version of PCA,in which only a small subset of samples could be stored.To handle the online PCA problem,Oja(1982)presented the stochastic power method under the assumption of zero-mean samples,and there have been lots of theoretical analysis and modified versions of this method in recent years.However,a common circumstance where the samples have nonzero mean is seldom studied.In this paper,we derive the convergence rate of a nonzero-mean version of Oja’s algorithm with diminishing stepsizes.In the analysis,we succeed in handling the dependency between each iteration,which is caused by the updated mean term for data centering.Furthermore,we verify the theoretical results by several numerical tests on both artificial and real datasets.Our work offers a way to deal with the top-1 online PCA when the mean of the given data is unknown.
基金supported by National Natural Science Foundation of China (Grant No. 11771275)
文摘In this paper, we consider the problem of finding sparse solutions for underdetermined systems of linear equations, which can be formulated as a class of L_0 norm minimization problem. By using the least absolute residual approximation, we propose a new piecewis, quadratic function to approximate the L_0 norm.Then, we develop a piecewise quadratic approximation(PQA) model where the objective function is given by the summation of a smooth non-convex component and a non-smooth convex component. To solve the(PQA) model,we present an algorithm based on the idea of the iterative thresholding algorithm and derive the convergence and the convergence rate. Finally, we carry out a series of numerical experiments to demonstrate the performance of the proposed algorithm for(PQA). We also conduct a phase diagram analysis to further show the superiority of(PQA) over L_1 and L_(1/2) regularizations.
基金supported by the Edward P.Fitts Fellowship at North Carolina State Universitythe National Natural Science Foundation of China Grant Numbers 11171177,11371216 and 11371242the US National Science Foundation Grant No.DMI-0553310
文摘Marginal risk represents the risk contribution of an individual asset to the risk of the entire portfolio In this paper, we investigate the portfolio selection problem with direct marginal risk control in a linear conic programming framework. ＇The optimization model involved is a nonconvex quadratically constrained quadratic programming （QCQP） problem. We first transform the QCQP problem into a linear conic programming problem, and then approximate the problem by semidefinite programming （SDP） relaxation problems over some subrectangles. In order to improve the lower bounds obtained from the SDP relaxation problems, linear and quadratic polar cuts are introduced for designing a branch-and-cut algorithm, that may yield an e -optimal global solution （with respect to feasibility and optimality） in a finite number of iterations. By exploring the special structure of the SDP relaxation problems, an adaptive branch-and-cut rule is employed to speed up the computation. The proposed algorithm is tested and compared with a known method in the literature for portfolio selection problems with hundreds of assets and tens of marginal risk control constraints.
