• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.12
• /
• pp.176-182
• /
• 2003

An optimal control approach to robust control design is proposed in this study for rigid robotic systems under the unknown load and the other uncertainties. The uncertainties are quadratically bounded for some positive definite matrix. Iterative method to find the matrix is shown. Simulations are made for a weight-lifting operation of a two-link manipulator and the robust control performance of robotic systems by the proposed algorithm is remarkable.

• Communications for Statistical Applications and Methods
• /
• v.23 no.6
• /
• pp.575-585
• /
• 2016

Longitudinal studies repeatedly measure outcomes over time. Therefore, repeated measurements are serially correlated from same subject (within-subject variation) and there is also variation between subjects (between-subject variation). The serial correlation and the between-subject variation must be taken into account to make proper inference on covariate effects (Diggle et al., 2002). However, estimation of the covariance matrix is challenging because of many parameters and positive definiteness of the matrix. To overcome these limitations, we propose autoregressive moving average Cholesky decomposition (ARMACD) for the linear mixed models. The ARMACD allows a class of flexible, nonstationary, and heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the random effects covariance matrix. We analyze a real dataset to illustrate our proposed methods.

• Journal of applied mathematics & informatics
• /
• v.28 no.3_4
• /
• pp.569-582
• /
• 2010

The following "Periodic Jacobi Procrustes" problem is studied: find the Periodic Jacobi matrix X which minimizes the Frobenius (or Euclidean) norm of AX - B, with A and B as given rectangular matrices. The class of Procrustes problems has many application in the biological, physical and social sciences just as in the investigation of elastic structures. The different problems are obtained varying the structure of the matrices belonging to the feasible set. Higham has solved the orthogonal, the symmetric and the positive definite cases. Andersson and Elfving have studied the symmetric positive semidefinite case and the (symmetric) elementwise nonnegative case. In this contribution, we extend and develop these research, however, in a relatively simple way. Numerical difficulties are discussed and illustrated by examples.

• Bulletin of the Korean Mathematical Society
• /
• v.53 no.6
• /
• pp.1893-1908
• /
• 2016

In this paper, we obtain some behaviours of theta sums of higher index for the $Schr{\ddot{o}}dinger$-Weil representation of the Jacobi group associated with a positive definite symmetric real matrix of degree m.

• Journal of the Korean Mathematical Society
• /
• v.34 no.2
• /
• pp.345-370
• /
• 1997

In this paper, we introduce the Fock representation $U^{F, M}$ of the Heisenberg group $H_R^(g, h)$ associated with a positive definite symmetric half-integral matrix $M$ of degree h and prove that $U^{F, M}$ is unitarily equivalent to the Schrodinger representation of index $M$.

• Kyungpook Mathematical Journal
• /
• v.61 no.3
• /
• pp.455-459
• /
• 2021

We partially characterize criteria for the n-universality of positive-definite integer-matrix quadratic forms. We then obtain the uniqueness of Oh's 8-universality criterion [11] as a corollary.

• Korean Management Science Review
• /
• v.19 no.2
• /
• pp.155-168
• /
• 2002

In this paper, some efficient implementation techniques for the multifrontal method, which can be used to compute the Cholesky factor of a symmetric positive definite matrix, are presented. In order to use the cache effect in the cache-based computer architecture, a hybrid method for factorizing a frontal matrix is considered. This hybrid method uses the column Cholesky method and the submatrix Cholesky method alternatively. Experiments show that the hybrid method speeds up the performance of the supernodal multifrontal method by 5%～10%, and it is superior to the Cholesky method in some problems with dense columns or large frontal matrices.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.1
• /
• pp.22-28
• /
• 1990

This paper is a study on the reduction of computation time in case of nonlinear magnetic field analysis by finite element method and Newton-Raphson method. For the purpose, the nonlinear convergence equation is computed by the conjugate gradient method which is known to be applicable to symmetric positive definite matrix equations only. As the results, we can not prove mathematically that the system Jacobian is positive definite, but when we applied this method, the diverging case did not occur. And the computation time is reduced by 25-55% and 15-45% in comparison with the case of direct and successive over-relaxation method, respectively. Therefore, we proved the utility of conjugate gradient method.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.25 no.4
• /
• pp.162-172
• /
• 2021

This paper proposes stochastic methods to find an approximate solution for the L²-Wasserstein least squares problem of Gaussian measures. The variable for the problem is in a set of positive definite matrices. The first proposed stochastic method is a type of classical stochastic gradient methods combined with projection and the second one is a type of variance reduced methods with projection. Their global convergence are analyzed by using the framework of proximal stochastic gradient methods. The convergence of the classical stochastic gradient method combined with projection is established by using diminishing learning rate rule in which the learning rate decreases as the epoch increases but that of the variance reduced method with projection can be established by using constant learning rate. The numerical results show that the present algorithms with a proper learning rate outperforms a gradient projection method.

• Communications of the Korean Mathematical Society
• /
• v.39 no.2
• /
• pp.437-460
• /
• 2024

In this paper we present the weighted shift operators having the property of moment infinite divisibility. We first review the monotone theory and conditional positive definiteness. Next, we study the infinite divisibility of sequences. A sequence of real numbers γ is said to be infinitely divisible if for any p > 0, the sequence γ^p = {γ^p_n}^∞_n=0 is positive definite. For sequences α = {α_n}^∞_n=0 of positive real numbers, we consider the weighted shift operators W_α. It is also known that W_α is moment infinitely divisible if and only if the sequences {γ_n}^∞_n=0 and {γ_n+1}^∞_n=0 of W_α are infinitely divisible. Here γ is the moment sequence associated with α. We use conditional positive definiteness to establish a new criterion for moment infinite divisibility of W_α, which only requires infinite divisibility of the sequence {γ_n}^∞_n=0. Finally, we consider some examples and properties of weighted shift operators having the property of (k, 0)-CPD; that is, the moment matrix M_γ(n, k) is CPD for any n ≥ 0.