TY - JOUR
T1 - SDPARA
T2 - Semidefinite programming algorithm paRAllel version
AU - Yamashita, M.
AU - Fujisawa, K.
AU - Kojima, M.
N1 - Funding Information:
Which is supported by Grants MH 23262-02 from the National Institute of Mental Health and SOC75-16151 from the National Science Foundation.
PY - 2003/8
Y1 - 2003/8
N2 - 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 large scale SDPs, the computation of the so-called Schur complement matrix and its Cholesky factorization consume most of the computational time. The SDPARA (SemiDefinite Programming Algorithm paRAllel version) is a parallel version of the SDPA on multiple processors and distributed memory, which replaces these two parts by their parallel implementation using MPI and ScaLAPACK. Through numerical results, we show that the SDPARA on a PC cluster consisting of 64 processors attains high scalability for large scale SDPs without losing the stability of the SDPA.
AB - 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 large scale SDPs, the computation of the so-called Schur complement matrix and its Cholesky factorization consume most of the computational time. The SDPARA (SemiDefinite Programming Algorithm paRAllel version) is a parallel version of the SDPA on multiple processors and distributed memory, which replaces these two parts by their parallel implementation using MPI and ScaLAPACK. Through numerical results, we show that the SDPARA on a PC cluster consisting of 64 processors attains high scalability for large scale SDPs without losing the stability of the SDPA.
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U2 - 10.1016/S0167-8191(03)00087-5
DO - 10.1016/S0167-8191(03)00087-5
M3 - Article
AN - SCOPUS:0041862659
VL - 29
SP - 1053
EP - 1067
JO - Parallel Computing
JF - Parallel Computing
SN - 0167-8191
IS - 8
ER -