Abstract
Solving large scale optimization problems requires a huge amount of computational power. The size of optimization problems that can be solved on a few CPUs has been limited due to a lack of computational power. Grid and cluster computing has received much attention as a powerful and inexpensive way of solving large scale optimization problems that an existing single-unit CPU cannot process. The aim of this paper is to show that grid and cluster computing provides tremendous power to optimization methods. The methods that this paper 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 method for semidefinite programs. Their parallel implementations on grids and clusters together with numerical results are reported. The paper also mentions a grid portal system for optimization problems briefly.
| Original language | English |
|---|---|
| Pages (from-to) | 265-274 |
| Number of pages | 10 |
| Journal | Journal of the Operations Research Society of Japan |
| Volume | 47 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Jan 1 2004 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Management Science and Operations Research
Cite this
Solving large scale optimization problems via grid and cluster computing. / Fujisawa, Katsuki; Kojima, Masakazu; Takeda, Akiko; Yamashita, Makoto.
In: Journal of the Operations Research Society of Japan, Vol. 47, No. 4, 01.01.2004, p. 265-274.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Solving large scale optimization problems via grid and cluster computing
AU - Fujisawa, Katsuki
AU - Kojima, Masakazu
AU - Takeda, Akiko
AU - Yamashita, Makoto
PY - 2004/1/1
Y1 - 2004/1/1
N2 - Solving large scale optimization problems requires a huge amount of computational power. The size of optimization problems that can be solved on a few CPUs has been limited due to a lack of computational power. Grid and cluster computing has received much attention as a powerful and inexpensive way of solving large scale optimization problems that an existing single-unit CPU cannot process. The aim of this paper is to show that grid and cluster computing provides tremendous power to optimization methods. The methods that this paper 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 method for semidefinite programs. Their parallel implementations on grids and clusters together with numerical results are reported. The paper also mentions a grid portal system for optimization problems briefly.
AB - Solving large scale optimization problems requires a huge amount of computational power. The size of optimization problems that can be solved on a few CPUs has been limited due to a lack of computational power. Grid and cluster computing has received much attention as a powerful and inexpensive way of solving large scale optimization problems that an existing single-unit CPU cannot process. The aim of this paper is to show that grid and cluster computing provides tremendous power to optimization methods. The methods that this paper 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 method for semidefinite programs. Their parallel implementations on grids and clusters together with numerical results are reported. The paper also mentions a grid portal system for optimization problems briefly.
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U2 - 10.15807/jorsj.47.265
DO - 10.15807/jorsj.47.265
M3 - Article
AN - SCOPUS:13744262112
VL - 47
SP - 265
EP - 274
JO - Journal of the Operations Research Society of Japan
JF - Journal of the Operations Research Society of Japan
SN - 0453-4514
IS - 4
ER -