Question

I am working with Jama Matrix. I used it for LSI. It works all fine. However, when I pass a big matrix like, 8000x8000 it kills my whole system. I am simply calling SVD then reducing matrix size and adding up. Nothing else !

任何想法? 我如何解决这一问题?

核心2

Ram = 10GB

Java 运行时间

- Xmx5000M

在我执行《马贾马矩阵法》时没有其他方案。

Answer 1

I also use the Jama for SVD and have the same problem in solving big matrix. In order to reduce memory overflow cases, I have tuned SingularValueDecomposition.java to compact one. The tuning is that in the matrix A it has so many 0 (zero) value so the compact for all of matrices used such as A, U, V, Work, etc only allows memory for available value which is more than 0. Before you use the compact SVD, you should make A matrix file like r /t c /t value /n r /t c /t value /n ... /t and /n means tab and new line respectively.

examples, (0, 0, 0), (0, 1, 0.5), (0, 2, 0), (0, 3, 0.2), (1, 0, 1), (1, 1, 0), (1, 2, 0), (1, 3, 0.3) and the matrix size is 2*4 (R*C) then you just make file like MATRIXSIZE /t 2 /t 4 /n 0 /t 1 /t 0.5 /n 0 /t 3 /t 0.2 /n 1 /t 0 /t 1 /n 1 /t 3 /t 0.3 /n

If you would like to use it, please give me your email address (mg.hwang@gmail.com). I will give more details to use it on email.

I checked the result was right. However, I am not sure how much effective it is for the computer. Anyway, it works and shows better even if not much.

Answer 2

你也许会面临记忆状态。您可能希望通过采用Xmx方案来增加您的国际交易日志的记忆,例如Xm256m将给您的JV 256甲基溴,而不是拖欠64甲基溴。

您也可考虑使用其他图书馆,利用诸如COO、DOK、CSR等混合矩阵表处理高效的图像。查阅“零敲碎打矩阵”的Wikipedia条目,以便了解更多细节。

这一版为Jamala提供了几种替代物,可能也帮助了你。

Answer 3

Peter Taylor是绝对正确的。 www.un.org/Depts/DGACM/index_spanish.htm

这是一个指数大的问题。在计算了8 000X 8000张矩阵的DVD之后,这不是一件ake事,因为你正在谈论64 000 000 000个元素!

如果你经营日本宇宙航空研究学院,有:

32x32矩阵间隔时间为0.062 sec.
64x64 ,在1.891 sec
128x128 in 4.5 sec
160x160 in 11.109 sec
192x in 24.063 sec
224x224 sec<<<>256x256 in 83.625
512x512 in 1716.719 亵渎

“enterography

Answer 4

如果你重新做LSI的工作,那么你可以做两个重要的优化工作。首先,你的矩阵是零敲碎打的(假设你采用逐个文件矩阵重新计算)。日本宇宙航行联合会在密集的矩阵上运作,因此,你可能希望找到不同的代表处。正如Lolo提到的那样,这将大大减少你的间接费用。

第二,LSI只需要计算顶高的单体病媒。日本宇宙航行局对你来说没有必要的所有单项价值进行计算。此外,如果你只需要最多,那么你可以进一步优化使用薄的DVD,因为光盘的记忆管理大大降低。计算LSI的全部DVD几乎不可能进行大型文件收集,因此,如果你想要扩大规模,最终不得不从JAMA以外的地方转用。

SVDLIBJ是一种在 Java进行薄膜DVS的可能性。 S-Space Pack也有一个SVDLIBJ包裹和指挥系统工具,如果你想避免编写LSI的话,则实施LSI/LSA。

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