Identification of linear systems using long periods of observation

A new method is presented for identifying parameters in a linear differential system arising, e.g., from compartment models in drug kinetics. The linearity of the system is used to produce a series of recurrence relations that help reduce the computational load. The method is especially useful when a long period of observation is used to identify the parameters. Numerical experiments are described.This research was sponsored by the National Institutes of Health, Grant No. GM-16197-01. Computing assistance was obtained from the Health Sciences Computing Facility, sponsored by NIH Grant No. FR-3.     

Implementing proximal point methods for linear programming

We describe the application of proximal point methods to the linear programming problem. Two basic methods are discussed. The first, which has been investigated by Mangasarian and others, is essentially the well-known method of multipliers. This approach gives rise at each iteration to a weakly convex quadratic program which may be solved inexactly using a point-SOR technique. The second approach is based on the proximal method of multipliers, originally proposed by Rockafellar, for which the quadratic program at each iteration is strongly convex. A number of techniques are used to solve this subproblem, the most promising of which appears to be a two-metric gradient-projection approach. Convergence results are given, and some numerical experience is reported.This research was supported by National Science Foundation, Grant Nos. ASC-87-14009 and DMS-86-19903, and by Air Force Office of Scientific Research, Grant No. AFOSR-ISSA-87-0092. Part of this work was performed at Argonne National Laboratory. Computation was partly performed at the Pittsburgh Supercomputer Center under NSF Grant No. DMS-88-0033P and at the Argonne Advanced Computing Research Facility, whose support is gratefully acknowledged. The author thanks Olvi Mangasarian and Renato DeLeone for helpful discussions, and Jorge Moré for his valuable advice on the algorithms discussed in Section 3. The contributions of a referee, who provided helpful comments on an earlier version of this paper, are also acknowledged.     

Gaining efficiency via weighted estimators for multivariate failure time data

Multivariate failure time data arise frequently in survival analysis. A commonly used technique is the working independence estimator for marginal hazard models. Two natural questions are how to improve the efficiency of the working independence estimator and how to identify the situations under which such an estimator has high statistical efficiency. In this paper, three weighted estimators are proposed based on three different optimal criteria in terms of the asymptotic covariance of weighted estimators. Simplified close-form solutions are found, which always outperform the working independence estimator. We also prove that the working independence estimator has high statistical efficiency, when asymptotic covariance of derivatives of partial log-likelihood functions is nearly exchangeable or diagonal. Simulations are conducted to compare the performance of the weighted estimator and working independence estimator. A data set from Busselton population health surveys is analyzed using the proposed estimators. This work was supported by National Natural Science Foundation of China (Grant No. 10628104), Fan was also supported by National Institutes of Health (Grant No. R01-GM072611) and Natural Science Foundation (Grant No. DMS-0714554), Zhou was supported by National Natural Science Funds for Distinguisheel Young Scholar (Grant No. 70825004), National Natural Science Foundation of China (Grant Nos. 10731010, 10628104), the National Basic Research Program (Grant No. 2007CB814902), Creative Research Groups of China (Grant No. 10721101) and Leading Academic Disipline Program, the 10 ^th five year plan of 211 Project for Shanghai University of Finance and Economics (the 3 ^rd phase), Cai was supported by National Institutes of Health (Grant No. R01-HL57444)     

A sequential method for nonlinear filtering: Numerical implementation and comparisons

Exact equations are presented for sequentially updating the optimal solution for a discrete-time analog of the basic Sridhar nonlinear filtering problem as the process length increases and new observations are obtained. A tabular method is described for implementing numerically the sequential filtering equations. The accuracy and efficiency of the tabular method are illustrated by means of several numerical examples.Dedicated to R. SridharThe work of R. Kalaba and L. Tesfatsion was partially supported by the National Science Foundation under Grant No. ENG-77-28432 and by the National Institutes of Health under Grant No. GM-23732-03.     

Bifurcation method for solving multiple positive solutions to Henon equation

Three algorithms based on the bifurcation method are applied to solving the D4 symmetric positive solutions to the boundary value problem of Henon equation. Taking r in Henon equation as a bi- furcation parameter, the D4-Σd(D4-Σ1, D4-Σ2) symmetry-breaking bifurcation points on the branch of the D4 symmetric positive solutions are found via the extended systems. Finally, Σd(Σ1, Σ2) sym- metric positive solutions are computed by the branch switching method based on the Liapunov-Schmidt reduction.     

Modeling and prediction of children’s growth data via functional principal component analysis

We use the functional principal component analysis (FPCA) to model and predict the weight growth in children. In particular, we examine how the approach can help discern growth patterns of underweight children relative to their normal counterparts, and whether a commonly used transformation to normality plays any constructive roles in a predictive model based on the FPCA. Our work supplements the conditional growth charts developed by Wei and He (2006) by constructing a predictive growth model based on a small number of principal components scores on individual’s past. This work was supported by National Natural Science Foundation of China (Grant No. 10828102), a Changjiang Visiting Professorship, the Training Fund of Northeast Normal University’s Scientific Innovation Project (Grant No. NENU-STC07002) and the National Institutes of Health Grant of USA (Grant No. R01GM080503-01A1).     

Asymptotic power comparison of the chi-square and likelihood ratio tests

Summary A Hoeffding-type power comparison is made between the likelihood ratio and chi-square tests for a simple hypothesis in a multinomial. The power comparison is based on the fact that under an alternative hypothesis the distribution of the test statistic can be approximated by a normal distribution. The theory of large deviations is used to match the significance levels. This research was partially supported by the National Science Foundation Grant No. GP-33697X2, U.S. Energy Research and Development Agency Grant 7064100, Environmental Protection Agency Grant R805379-01-0 and U.S. Public Health Service Grant USPHS ES01299-15, at the University of California-Berkeley.     

Auxiliary space preconditioning in H 0(curl; Ω)

We adapt the principle of auxiliary space preconditioning as presented in [J. Xu, The auxiliary space method and optimal multigrid preconditioning techniques for unstructured grids, Computing, 56 (1996), pp. 215–235.] to H (curl; ω)-elliptic variational problems discretized by means of edge elements. The focus is on theoretical analysis within the abstract framework of subspace correction. Employing a Helmholtz-type splitting of edge element vector fields we can establish asymptotic h-uniform optimality of the preconditioner defined by our auxiliary space method. This author was fully supported by Hong Kong RGC grant (Project No. 403403) This author acknowledges the support from a Direct Grant of CUHK during his visit at The Chinese University of Hong Kong.     

On the integral equations of optimal signal detection and estimation theory

The analysis of many optimal signal detection and estimation processes involves the solution of Fredholm integral equations. A New approach to these equations is presented. It consists of reducing the Fredholm integral equation to a Cauchy system which is well suited to numerical solution.This research was supported by the National Institutes of Health, Grant No. GM-16437-03.     

A robust secant method for optimization problems with inequality constraints

This paper presents a secant method, based on R. B. Wilson's formula for the solution of optimization problems with inequality constraints. Global convergence properties are ensured by grafting the secant method onto a phase I - phase II feasible directions method, using a rate of convergence test for crossover control.This research was sponsored by the National Science Foundation, Grant No. ENG-73-08214 and Grant No. (RANN)-ENV-76-04264, and by the Joint Services Electronics Program. Contract No. F44620-76-C-0100.     

Three kinds of extraneous factors in Dixon resultants

Dixon resultant is a basic elimination method which has been used widely in the high technology fields of automatic control, robotics, etc. But how to remove extraneous factors in Dixon resultants has been a very difficult problem. In this paper, we discover some extraneous factors by expressing the Dixon resultant in a linear combination of original polynomial system. Furthermore, it has been proved that the factors mentioned above include three parts which come from Dixon derived polynomials, Dixon matrix and the resulting resultant expression by substituting Dixon derived polynomials respectively. This work was supported by the National Key Basic Special Funds of China (Grant No. 2004CB318003), the Knowledge Innovation Project of the Chinese Academy of Sciences (Grant No. KJCX2-YW-S02), the National Natural Science Foundation of China (Grant No. 90718041), Shanghai Leading Academic Discipline Project (Grant No. B412) and the Doctor Startup Foundation of East China Normal University (Grant No. 790013J4)     

Cutting planes and column generation techniques with the projective algorithm

The problem studied is that of solving linear programs defined recursively by column generation techniques or cutting plane techniques using, respectively, the primal projective method or the dual projective method.This research has been supported in part by FCAR of Quebec, Grant Nos. CE-130 and EQ-3078, by NSERC of Canada, Grant No. A4152, and by the Fonds National Suisse de la Recherche Scientifique, Grant No. 1.467.0.86.     

The block decomposition of a vandermonde matrix and its applications

This paper presents a new algorithm for the solution of linear equations with a Vandermonde coefficient matrix. The algorithm can also be used to solve the dual problem. Since the algorithm uses a block decomposition of the matrix, it is especially suitable for parallel computation. A variation of the block decomposition leads to the efficient solution of the interpolation problem with complex-conjugate interpolation points where the coefficients of the interpolating polynomial are real. In addition the algorithm can be used to solve some kinds of confluent Vandermonde systems.W. P. Tang is a graduate student in the Computer Science Department at Stanford University, on leave from the Institute of Computing Technology of the Chinese Academy of Science in Peking.The work of Professor Golub was in part supported by NSF Grant No. MCS78-11985.     

Variational equations for the eigenvalues and eigenvectors of nonsymmetric matrices

The tracking of eigenvalues and eigenvectors for parameterized matrices is of major importance in optimization and stability problems. In the present paper, we consider a one-parameter family of matrices with distinct eigenvalues. A complete system of differential equations is developed for both the eigenvalues and the right and left eigenvectors. The computational feasibility of the differential system is demonstrated by means of a numerical example.The work of R. Kalaba and L. Tesfatsion was partially supported by the National Science Foundation under Grant No. ENG-77-28432 and by the National Institutes of Health under Grant No. GM-23732-03.     

Fuzzy Topology Based on Residuated Lattice-Valued Logic

We use a semantical method of complete residuated lattice-valued logic to give a generalization of fuzzy topology as a partial answer to a problem by Roser and Turquette. This work is supported by the National Foundation for Distinguished Young Scholars (Grant No: 69725004), Research and Development Project of High-Technology (Grant No: 863-306-ZT06-04-3) and Foundation of Natural Sciences (Grant No: 69823001) of China and Fok Ying-Tung Education Foundation     

Pointwise quasi-Newton method for unconstrained optimal control problems,II

In this paper, the necessary optimality conditions for an unconstrained optimal control problem are used to derive a quasi-Newton method where the update involves only second-order derivative terms. A pointwise update which was presented in a previous paper by the authors is changed to allow for more general second-order sufficiency conditions in the control problem. In particular, pointwise versions of the Broyden, PSB, and SR1 update are considered. A convergence rate theorem is given for the Broyden and PSB versions.This research was supported by NSF Grant No. DMS-89-00410, by NSF Grant No. INT-88-00560, by AFOSR Grant No. AFOSR-89-0044, and by the Deutsche Forschungsgemeinschaft.     

Multiplier and gradient methods

The main purpose of this paper is to suggest a method for finding the minimum of a functionf(x) subject to the constraintg(x)=0. The method consists of replacingf byF=f+g+1/2cg ², wherec is a suitably large constant, and computing the appropriate value of the Lagrange multiplier. Only the simplest algorithm is presented. The remaining part of the paper is devoted to a survey of known methods for finding unconstrained minima, with special emphasis on the various gradient techniques that are available. This includes Newton's method and the method of conjugate gradients.The preparation of this paper was sponsored by the U.S. Army Research Office, Grant No. DA-31-124-ARO(D)-355. This paper was presented at the Second International Conference on Computing Methods in Optimization Problems, San Remo, Italy, 1968.     

Inversion of tridiagonal matrices

Summary This paper presents a simple algorithm for inverting nonsymmetric tridiagonal matrices that leads immediately to closed forms when they exist. Ukita's theorem is extended to characterize the class of matrices that have tridiagonal inverses.Journal Paper No. J-10137 of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa. Project 1669, Partial support by National Institutes of Health, Grant GM 13827     

Convergence of Newton‘’s Method and Uniqueness of the Solution of Equations in Banach SpacesⅡ

Some results on convergence of Newton‘s method in Banach spaces are established under the assumption that the derivative of the opderators satisfies the radius or center Lipschitz condition with a weak L average.     

Consistency and asymptotic normality of profilekernel and backfitting estimators in semiparametric reproductive dispersion nonlinear models

Semiparametric reproductive dispersion nonlinear model (SRDNM) is an extension of nonlinear reproductive dispersion models and semiparametric nonlinear regression models, and includes semiparametric nonlinear model and semiparametric generalized linear model as its special cases. Based on the local kernel estimate of nonparametric component, profile-kernel and backfitting estimators of parameters of interest are proposed in SRDNM, and theoretical comparison of both estimators is also investigated in this paper. Under some regularity conditions, strong consistency and asymptotic normality of two estimators are proved. It is shown that the backfitting method produces a larger asymptotic variance than that for the profile-kernel method. A simulation study and a real example are used to illustrate the proposed methodologies. This work was supported by National Natural Science Foundation of China (Grant Nos. 10561008, 10761011), Natural Science Foundation of Department of Education of Zhejiang Province (Grant No. Y200805073), PhD Special Scientific Research Foundation of Chinese University (Grant No. 20060673002) and Program for New Century Excellent Talents in University (Grant No. NCET-07-0737)