• Title/Summary/Keyword: matrix problems

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USE OF CYCLICITY FOR SOLVING SOME MATRIX PROBLEMS

  • Park, Pil-Seong
    • Journal of applied mathematics & informatics
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    • v.5 no.3
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    • pp.571-584
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    • 1998
  • We look for methods and conditions to make use of cyclicity in come matrix problems not only for parallel computa-tion but also to reduce the problem size and accelerate convergence. It has been shown that some form of reducibility not necessarily cyclicity is enough for such purposes.

Applications of General-Purpose Packages for Fluid-Structure Interaction Problems (범용 패키지의 결합을 통한 구조-유체 상호 작용 해석 기법)

  • 홍진숙;신구균
    • Journal of KSNVE
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    • v.7 no.4
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    • pp.571-578
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    • 1997
  • Recently, many general-purpose packages for fluid-structure interaction problems have been announced. However, they have a lot of limitations to model structures in the fluid-structure interaction problems reasonably. Utilizing general-purpose packages such as MSC/NASTRAN and SYSNOISE, in this paper, a method to slove the radiation scattering problems with some accuracy in the fluid-structure interaction problems was developed. Using a simple model, the results from the presented method here are compared with those from SYSNOISE. The result shows quite a good agreement between the two methods. The problems, which could not be solved by SYSNOISE, were tried to solve with the presented method and results were presented. It was proved that this method could be safely used to solve fluid-structure interaction problems.

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ON AUGMENTED LAGRANGIAN METHODS OF MULTIPLIERS AND ALTERNATING DIRECTION METHODS OF MULTIPLIERS FOR MATRIX OPTIMIZATION PROBLEMS

  • Gue Myung, Lee;Jae Hyoung, Lee
    • Nonlinear Functional Analysis and Applications
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    • v.27 no.4
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    • pp.869-879
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    • 2022
  • In this paper, we consider matrix optimization problems. We investigate augmented Lagrangian methods of multipliers and alternating direction methods of multipliers for the problems. Following the proofs of Eckstein [3], and Eckstein and Yao [5], we prove convergence theorems for augmented Lagrangian methods of multipliers and alternating direction methods of multipliers for the problems.

A Nonlinear Programming Approach to Biaffine Matrix Inequality Problems in Multiobjective and Structured Controls

  • Lee, Joon-Hwa;Lee, Kwan-Ho;Kwon, Wook-Hyun
    • International Journal of Control, Automation, and Systems
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    • v.1 no.3
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    • pp.271-281
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    • 2003
  • In this paper, a new nonlinear programming approach is suggested to solve biaffine matrix inequality (BMI) problems in multiobjective and structured controls. It is shown that these BMI problems are reduced to nonlinear minimization problems. An algorithm that is easily implemented with existing convex optimization codes is presented for the nonlinear minimization problem. The efficiency of the proposed algorithm is illustrated by numerical examples.

Approach to BMI Problems Using Evolution Strategy

  • Chung, Tae-Jin;Chung, Chan-Soo
    • 제어로봇시스템학회:학술대회논문집
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    • 2000.10a
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    • pp.224-224
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    • 2000
  • Biaffine Matrix Inequalities(BIs) are known to give more general and flexible frameworks in control designs than Linear Matrix Inequalities(LMIs). However, BMIs are nonconvex constraints and very difficult to solve. In this paper, BMI problems are solved using Evolution Strategy(ES). Numerous BMI problems are solved to verify performances of ES solver for BMI problems and compared with those of Genetic Algorithms and Branch-and-Cut algorithm.

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MERIT FUNCTIONS FOR MATRIX CONE COMPLEMENTARITY PROBLEMS

  • Wang, Li;Liu, Yong-Jin;Jiang, Yong
    • Journal of applied mathematics & informatics
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    • v.31 no.5_6
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    • pp.795-812
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    • 2013
  • The merit function arises from the development of the solution methods for the complementarity problems defined over the cone of non negative real vectors and has been well extended to the complementarity problems defined over the symmetric cones. In this paper, we focus on the extension of the merit functions including the gap function, the regularized gap function, the implicit Lagrangian and others to the complementarity problems defined over the nonsymmetric matrix cone. These theoretical results of this paper suggest new solution methods based on unconstrained and/or simply constrained methods to solve the matrix cone complementarity problems (MCCP).

AN ASYNCHRONOUS PARALLEL SOLVER FOR SOME MATRIX PROBLEMS

  • Park, Pil-Seong
    • Journal of applied mathematics & informatics
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    • v.7 no.3
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    • pp.1045-1058
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    • 2000
  • In usual synchronous parallel computing, workload balance is a crucial factor to reduce idle times of some processors that have finished their jobs earlier than others. However, it is difficult to achieve on a heterogeneous workstation clusters where the available computing power of each processor is unpredictable. As a way to overcome such a problem, the idea of asynchronous methods has grown out and is being increasingly used and studied, but there is none for eigenvalue problems yet. In this paper, we suggest a new asynchronous method to solve some singular matrix problems, that can also be used for finding a certain eigenvector of some matrices.

ON DICHOTOMY AND CONDITIONING FOR TWO-POINT BOUNDARY VALUE PROBLEMS ASSOCIATED WITH FIRST ORDER MATRIX LYAPUNOV SYSTEMS

  • Murty, M.S.N.;Kumar, G. Suresh
    • Journal of the Korean Mathematical Society
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    • v.45 no.5
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    • pp.1361-1378
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    • 2008
  • This paper deals with the study of dichotomy and conditioning for two-point boundary value problems associated with first order matrix Lyapunov systems, with the help of Kronecker product of matrices. Further, we obtain close relationship between the stability bounds of the problem on one hand, and the growth behaviour of the fundamental matrix solution on the other hand.

User-Item Matrix Reduction Technique for Personalized Recommender Systems (개인화 된 추천시스템을 위한 사용자-상품 매트릭스 축약기법)

  • Kim, Kyoung-Jae;Ahn, Hyun-Chul
    • Journal of Information Technology Applications and Management
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    • v.16 no.1
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    • pp.97-113
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    • 2009
  • Collaborative filtering(CF) has been a very successful approach for building recommender system, but its widespread use has exposed to some well-known problems including sparsity and scalability problems. In order to mitigate these problems, we propose two novel models for improving the typical CF algorithm, whose names are ISCF(Item-Selected CF) and USCF(User-Selected CF). The modified models of the conventional CF method that condense the original dataset by reducing a dimension of items or users in the user-item matrix may improve the prediction accuracy as well as the efficiency of the conventional CF algorithm. As a tool to optimize the reduction of a user-item matrix, our study proposes genetic algorithms. We believe that our approach may relieve the sparsity and scalability problems. To validate the applicability of ISCF and USCF, we applied them to the MovieLens dataset. Experimental results showed that both the efficiency and the accuracy were enhanced in our proposed models.

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THE (0, 1)-NORMAL SANDWICH PROBLEM

  • Park, Se Won;Han, Hyuk;Park, Sung-Wook
    • Journal of the Chungcheong Mathematical Society
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    • v.16 no.1
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    • pp.25-36
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    • 2003
  • We study the question of whether a partial (0, 1)-normal matrix has a non-symmetric normal completion. Matrix sandwich problems are an important and special case of matrix completion problems. In this paper, we give some properties for the (0, 1)-normal matrices and some large classes that satisfies the normal sandwich completion.

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