Solving large scale optimization problems via grid and cluster computing

Katsuki Fujisawa, Masakazu Kojima, Akiko Takeda, Makoto Yamashita

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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 languageEnglish
Pages (from-to)265-274
Number of pages10
JournalJournal of the Operations Research Society of Japan
Issue number4
Publication statusPublished - Dec 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Decision Sciences
  • Management Science and Operations Research


Dive into the research topics of 'Solving large scale optimization problems via grid and cluster computing'. Together they form a unique fingerprint.

Cite this