Strassen algorithm(O(n^lg7))
Let A, B be two square matrices over a ring R. We want to calculate the matrix product C as
- {\displaystyle \mathbf {C} =\mathbf {A} \mathbf {B} \qquad \mathbf {A} ,\mathbf {B} ,\mathbf {C} \in R^{2^{n}\times 2^{n}}}
If the matrices A, B are not of type 2n × 2n we fill the missing rows and columns with zeros.
We partition A, B and C into equally sized block matrices
- {\displaystyle \mathbf {A} ={\begin{bmatrix}\mathbf {A} _{1,1}&\mathbf {A} _{1,2}\\\mathbf {A} _{2,1}&\mathbf {A} _{2,2}\end{bmatrix}}{\mbox{ , }}\mathbf {B} ={\begin{bmatrix}\mathbf {B} _{1,1}&\mathbf {B} _{1,2}\\\mathbf {B} _{2,1}&\mathbf {B} _{2,2}\end{bmatrix}}{\mbox{ , }}\mathbf {C} ={\begin{bmatrix}\mathbf {C} _{1,1}&\mathbf {C} _{1,2}\\\mathbf {C} _{2,1}&\mathbf {C} _{2,2}\end{bmatrix}}}
with
- {\displaystyle \mathbf {A} _{i,j},\mathbf {B} _{i,j},\mathbf {C} _{i,j}\in R^{2^{n-1}\times 2^{n-1}}}
then
- {\displaystyle \mathbf {C} _{1,1}=\mathbf {A} _{1,1}\mathbf {B} _{1,1}+\mathbf {A} _{1,2}\mathbf {B} _{2,1}}
- {\displaystyle \mathbf {C} _{1,2}=\mathbf {A} _{1,1}\mathbf {B} _{1,2}+\mathbf {A} _{1,2}\mathbf {B} _{2,2}}
- {\displaystyle \mathbf {C} _{2,1}=\mathbf {A} _{2,1}\mathbf {B} _{1,1}+\mathbf {A} _{2,2}\mathbf {B} _{2,1}}
- {\displaystyle \mathbf {C} _{2,2}=\mathbf {A} _{2,1}\mathbf {B} _{1,2}+\mathbf {A} _{2,2}\mathbf {B} _{2,2}}
With this construction we have not reduced the number of multiplications. We still need 8 multiplications to calculate the Ci,j matrices, the same number of multiplications we need when using standard matrix multiplication.
Now comes the important part. We define new matrices
- {\displaystyle \mathbf {M} _{1}:=(\mathbf {A} _{1,1}+\mathbf {A} _{2,2})(\mathbf {B} _{1,1}+\mathbf {B} _{2,2})}
- {\displaystyle \mathbf {M} _{2}:=(\mathbf {A} _{2,1}+\mathbf {A} _{2,2})\mathbf {B} _{1,1}}
- {\displaystyle \mathbf {M} _{3}:=\mathbf {A} _{1,1}(\mathbf {B} _{1,2}-\mathbf {B} _{2,2})}
- {\displaystyle \mathbf {M} _{4}:=\mathbf {A} _{2,2}(\mathbf {B} _{2,1}-\mathbf {B} _{1,1})}
- {\displaystyle \mathbf {M} _{5}:=(\mathbf {A} _{1,1}+\mathbf {A} _{1,2})\mathbf {B} _{2,2}}
- {\displaystyle \mathbf {M} _{6}:=(\mathbf {A} _{2,1}-\mathbf {A} _{1,1})(\mathbf {B} _{1,1}+\mathbf {B} _{1,2})}
- {\displaystyle \mathbf {M} _{7}:=(\mathbf {A} _{1,2}-\mathbf {A} _{2,2})(\mathbf {B} _{2,1}+\mathbf {B} _{2,2})}
only using 7 multiplications (one for each Mk) instead of 8. We may now express the Ci,j in terms of Mk, like this:
- {\displaystyle \mathbf {C} _{1,1}=\mathbf {M} _{1}+\mathbf {M} _{4}-\mathbf {M} _{5}+\mathbf {M} _{7}}
- {\displaystyle \mathbf {C} _{1,2}=\mathbf {M} _{3}+\mathbf {M} _{5}}
- {\displaystyle \mathbf {C} _{2,1}=\mathbf {M} _{2}+\mathbf {M} _{4}}
- {\displaystyle \mathbf {C} _{2,2}=\mathbf {M} _{1}-\mathbf {M} _{2}+\mathbf {M} _{3}+\mathbf {M} _{6}}
We iterate this division process n times (recursively) until the submatrices degenerate into numbers (elements of the ring R). The resulting product will be padded with zeroes just like A and B, and should be stripped of the corresponding rows and columns.
Practical implementations of Strassen's algorithm switch to standard methods of matrix multiplication for small enough submatrices, for which those algorithms are more efficient. The particular crossover point for which Strassen's algorithm is more efficient depends on the specific implementation and hardware. Earlier authors had estimated that Strassen's algorithm is faster for matrices with widths from 32 to 128 for optimized implementations. However, it has been observed that this crossover point has been increasing in recent years, and a 2010 study found that even a single step of Strassen's algorithm is often not beneficial on current architectures, compared to a highly optimized traditional multiplication, until matrix sizes exceed 1000 or more, and even for matrix sizes of several thousand the benefit is typically marginal at best (around 10% or less).
from Wikipedia
--------------------------------------------------------------------------------------------------------------------------------------------------------------
it substitude the 8th recursive invocation(multiplication) by the liner combination of the submatrices above(cause A4,4 and B4,4 has been used before). like a*(b+c) can have less steps than a*b+a*c,it uses liner combination to simplify the tranditional multiplicate way.Strassen algorithm(O(n^lg7))的更多相关文章
- strassen algorithm
the explaination that is clear in my view is from wiki.
- Conquer and Divide经典例子之Strassen算法解决大型矩阵的相乘
在通过汉诺塔问题理解递归的精髓中我讲解了怎么把一个复杂的问题一步步recursively划分了成简单显而易见的小问题.其实这个解决问题的思路就是算法中常用的divide and conquer, 这篇 ...
- [Algorithm] 如何正确撸<算法导论>CLRS
其实算法本身不难,第一遍可以只看伪代码和算法思路.如果想进一步理解的话,第三章那些标记法是非常重要的,就算要花费大量时间才能理解,也不要马马虎虎略过.因为以后的每一章,讲完算法就是这样的分析,精通的话 ...
- Strassen优化矩阵乘法(复杂度O(n^lg7))
按照算法导论写的 还没有测试复杂度到底怎么样 不过这个真的很卡内存,挖个坑,以后写空间优化 还有Matthew Anderson, Siddharth Barman写了一个关于矩阵乘法的论文 < ...
- [Algorithm] 面试题之犄角旮旯 第贰章
闲下来后,需要讲最近涉及到的算法全部整理一下,有个indice,方便记忆宫殿的查找 MIT的算法课,地球上最好: Design and Analysis of Algorithms 本篇需要重新整理, ...
- 挑子学习笔记:两步聚类算法(TwoStep Cluster Algorithm)——改进的BIRCH算法
转载请标明出处:http://www.cnblogs.com/tiaozistudy/p/twostep_cluster_algorithm.html 两步聚类算法是在SPSS Modeler中使用的 ...
- PE Checksum Algorithm的较简实现
这篇BLOG是我很早以前写的,因为现在搬移到CNBLOGS了,经过整理后重新发出来. 工作之前的几年一直都在搞计算机安全/病毒相关的东西(纯学习,不作恶),其中PE文件格式是必须知识.有些PE文件,比 ...
- [异常解决] windows用SSH和linux同步文件&linux开启SSH&ssh client 报 algorithm negotiation failed的解决方法之一
1.安装.配置与启动 SSH分客户端openssh-client和openssh-server 如果你只是想登陆别的机器的SSH只需要安装openssh-client(ubuntu有默认安装,如果没有 ...
- [Algorithm] 使用SimHash进行海量文本去重
在之前的两篇博文分别介绍了常用的hash方法([Data Structure & Algorithm] Hash那点事儿)以及局部敏感hash算法([Algorithm] 局部敏感哈希算法(L ...
随机推荐
- SQLServer2012数据库降级至SQLServer2008R2的方法
一. 背景 因为对方的客户的服务器安装的数据版本2012,公司开发同事需要客户数据库的备份数据,但是公司数据版本是2008R2的,无法还原. 由于2012备份无法直接还原至2008R2(MSSQ ...
- Java中获得当前静态类的类名
通常在打印日志的时候需要输出类名,普通类可以用this.getClass(),但是静态类没有this,直接写类名耦合度高. 参考了: https://stackoverflow.com/questio ...
- Github SSH key 的配置
哈喽,新年好呀! 今天我又来更新一点github的内容啦~~ windows版本 一.打开git shell,输入指令操作ssh-keygen -t rsa -C “你的注册邮箱”,然后回车回车回车, ...
- django学习之——模版
为了减少模板加载调用过程及模板本身的冗余代码,Django 提供了一种使用方便且功能强大的 API ,用于从磁盘中加载模板, 要使用此模板加载API,首先你必须将模板的保存位置告诉框架. 设置的保存文 ...
- Units about ASM
1.ASM Striping and Mirroring:ASM supports two levels of striping: fine striping and coarse striping. ...
- python3练习-装饰器
在廖雪峰的官方网站学习装饰器章节时,初步理解类似与面向切面编程.记录一下自己的课后习题解法. 问题: 请编写一个decorator,能在函数调用的前后打印出'begin call'和'end call ...
- vue 小知识
图片: 1.img 的路径 <img :src="item.src"/> 2.背景图片的路径 v-bind:style="{backgroundImage: ...
- prefix super supra sex sept septi out~2
1★ super 2★ supra 超过,超出 3★ sept 4★ septi 7 5★ sex 6
- win7下使用U盘安装双系统(Ubuntu-17)
1.首先下载Ubuntu镜像文件,下载地址:http://mirrors.neusoft.edu.cn/ 2.下载 U盘操作系统安装工具- Universal USB Installer ,下载地址: ...
- shell test判断命令
判断命令test 使用test命令可以对文件,字符串等进行测试,一般配合控制语句使用,如while,if,case "字符串测试" test str1==str2 测试字符串是 ...