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 Makoto Yamashita

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Average citations per article3.56
Citation Count32
Publication count9
Publication years2003-2015
Available for download1
Average downloads per article222.00
Downloads (cumulative)222
Downloads (12 Months)20
Downloads (6 Weeks)3
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9 results found Export Results: bibtexendnoteacmrefcsv

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1
September 2015 Optimization Methods & Software: Volume 30 Issue 5, October 2015
Publisher: Taylor & Francis, Inc.
Bibliometrics:
Citation Count: 0

Solving semidefinite programs SDPs in a short time is the key to managing various mathematical optimization problems. The matrix-completion primal–dual interior-point method MC-PDIPM extracts a sparse structure of input SDP by factorizing the variable matrices. In this paper, we propose a new factorization based on the inverse of the variable ...
Keywords: matrix completion, multithreaded computing, interior-point methods, semidefinite programs

2 published by ACM
August 2012 ACM Transactions on Mathematical Software (TOMS): Volume 38 Issue 4, August 2012
Publisher: ACM
Bibliometrics:
Citation Count: 5
Downloads (6 Weeks): 3,   Downloads (12 Months): 20,   Downloads (Overall): 222

Full text available: PDFPDF
SFSDP is a Matlab package for solving sensor network localization (SNL) problems. These types of problems arise in monitoring and controlling applications using wireless sensor networks. SFSDP implements the semidefinite programming (SDP) relaxation proposed in Kim et al. [2009] for sensor network localization problems, as a sparse version of the ...
Keywords: Sensor network localization problems, sparsity exploitation, Matlab software package, semidefinite programming relaxation

3
September 2011 Mathematical Programming: Series A and B: Volume 129 Issue 1, September 2011
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

A basic framework for exploiting sparsity via positive semidefinite matrix completion is presented for an optimization problem with linear and nonlinear matrix inequalities. The sparsity, characterized with a chordal graph structure, can be detected in the variable matrix or in a linear or nonlinear matrix-inequality constraint of the problem. We ...
Keywords: Chordal Graph, Polynomial Optimization, Positive Semidefinite Matrix Completion, 90C22, 90C30, Matrix Inequalities, Sparsity, 90C26, Semidefinite Program

4
September 2011 Mathematical Programming: Series A and B - Special Issue on Cone Programming and its Applications: Volume 129 Issue 1, September 2011
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 6

A basic framework for exploiting sparsity via positive semidefinite matrix completion is presented for an optimization problem with linear and nonlinear matrix inequalities. The sparsity, characterized with a chordal graph structure, can be detected in the variable matrix or in a linear or nonlinear matrix-inequality constraint of the problem. We ...
Keywords: Matrix Inequalities, Sparsity, Semidefinite Program, Chordal Graph, Polynomial Optimization, Positive Semidefinite Matrix Completion

5
March 2007 Mathematical Programming: Series A and B: Volume 109 Issue 2-3, March 2007
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 0

It has been a long-time dream in electronic structure theory in physical chemistry/chemical physics to compute ground state energies of atomic and molecular systems by employing a variational approach in which the two-body reduced density matrix (RDM) is the unknown variable. Realization of the RDM approach has benefited greatly from ...
Keywords: 81Q05, Computational chemistry, 68W10, 90C06, 90C22, N-representability, Parallel computation, Reduced density Matrix, Large-scale optimization, Semidefinite programming relaxation

6
January 2007 Mathematical Programming: Series A and B: Volume 109 Issue 2, January 2007
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 3

It has been a long-time dream in electronic structure theory in physical chemistry/chemical physics to compute ground state energies of atomic and molecular systems by employing a variational approach in which the two-body reduced density matrix (RDM) is the unknown variable. Realization of the RDM approach has benefited greatly from ...
Keywords: 81Q05, 68W10, 90C22

7
January 2006 Parallel Computing: Volume 32 Issue 1, January 2006
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 4

A parallel computational method SDPARA-C is presented for SDPs (semidefinite programs). It combines two methods SDPARA and SDPA-C proposed by the authors who developed a software package SDPA. SDPARA is a parallel implementation of SDPA and it features parallel computation of the elements of the Schur complement equation system and ...
Keywords: Semidefinite program, Numerical experiment, PC cluster, Parallel computation, Positive definite matrix completion, Primal-dual interior-point method

8
January 2004 SAINT-W '04: Proceedings of the 2004 Symposium on Applications and the Internet-Workshops (SAINT 2004 Workshops)
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 0

The aim of this short article is to show that grid and cluster computing provides tremendous power to optimization methods. The methods that the article picks up are a successive convex relaxation method for quadratic optimization problems, a polyhedral homotopy method for polynomial systems of equations and a primal-dual interior-point ...

9
August 2003 Parallel Computing: Volume 29 Issue 8, 1 August 2003
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 12

The SDPA (SemidDefinite Programming Algorithm) is known as efficient computer software based on the primal-dual interior-point method for solving SDPs (SemiDefinite Programs). In many applications, however, some SDPs become larger and larger, too large for the SDPA to solve on a single processor. In execution of the SDPA applied to ...
Keywords: primal-dual interior-point method, semidefinite program, software, PC cluster, optimization, parallel computation



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