| 来源类型 | Monograph (IIASA Working Paper) |
| 规范类型 | 论文 |
| A Regularized Jacobi Method for Large-Scale Linear Programming. | |
| Kallio MJ; Ruszczynski A; Salo S | |
| 发表日期 | 1993 |
| 出版者 | IIASA, Laxenburg, Austria: WP-93-061 |
| 出版年 | 1993 |
| 语种 | 英语 |
| 摘要 | A parallel algorithm based on Jacobi iterations is proposed to minimize the augmented Lagrangian functions of the multiplier method for large-scale linear programming. Sparsity is efficiently exploited for determining stepsizes (column-wise) for the Jacobi iterations. Linear convergence is shown with convergence ratio depending on sparsity but not on the penalty parameter and on problem size. Employing simulation of parallel computations, an experimental code is tested extensively on 68 Netlib problems. Results are compared with the simplex method, an interior point algorithm and a Gauss-Seidel approach. We observe that speedup against the simplex method generally increases with the problem size, while the parallel solution times increase slowly, if at all. Our preliminary results compared with the other two methods are highly encouraging as well. |
| 主题 | Optimization under Uncertainty (OPT) |
| URL | http://pure.iiasa.ac.at/id/eprint/3753/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/124115 |
| 推荐引用方式 GB/T 7714 | Kallio MJ,Ruszczynski A,Salo S. A Regularized Jacobi Method for Large-Scale Linear Programming.. 1993. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| WP-93-061.pdf（1151KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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