那么，矩阵乘法是什么意思？
,好，这就是矩阵乘法。
矩阵乘法是有结合律的。
矩阵乘法，矩阵转置返回零？
Matrix multiplication by it's transpose returning zeros matrix?
彻底解决了矩阵乘法计算的简化问题。
The simplification matter of matrix multiplication is settled thoroughly in the way given in the paper.
多个多值上下文变量将产生矩阵乘法，并且不受支持。
Multiple multi-value context variables would result in a matrix multiplication and are not supported.
如果你不知道，如何做矩阵乘法，再看看相关笔记吧。
OK, so if you don't remember how to multiply matrices, please look at the notes on that again.
我们可以用，矩阵乘法或矩阵乘积的形式来表述这些式子。
And we can reformulate this in terms of matrix multiplication or matrix product.
如何使用线程池和多线程矩阵乘法的消息队列？
How to use thread pool and message queues in Multithreaded Matrix Multiplication?
我们学过矩阵乘法的，以及用矩阵表示线性方程。
OK, so we've seen how to multiply matrices, and how to write linear systems in matrix form.
下列矩阵乘法将会依照列出来的顺序执行成对的转换。
The following matrix multiplication will perform the pair of transformations in the order listed.
由于公式不涉及矩阵乘法和求逆运算，因而计算量较小。
Since the formulas do not involve the multiplication and inverse of matrix, the calculations are reduced.
在算法中它是通过透视矩阵乘法和透视除法两步完成的。
In this algorithm, which is matrix multiplication and perspective through the perspective divide step completed.
计算最短路径与矩阵乘法和弗洛伊德·沃肖尔算法对以下图。
Compute shortest paths with matrix multiplication and the Floyd-Warshall algorithm for the following graph.
本论文主要介绍了矩阵乘法运算的延伸及矩阵在图形变换中的应用；
This paper mainly introduces the tendency of multiply operation of matrix and its application in graph transformation.
我们将对矩阵乘法的标准技术稍微进行一下修改，这样就可以使用前面介绍的算法了。
We'll make a slight change to the standard technique for multiplying matrices so that the previous algorithm can be applied here.
本文使用NVIDIA的CUDA在GPU上实现了一个高效的矩阵乘法。
In this paper, we implement an efficient matrix multiplication on GPU using NVIDIA's CUDA.
矩阵乘法是科学计算中最基本的操作，高效实现矩阵乘法可以加速许多应用。
Matrix multiplication is a basic operation in scientific computing. Efficient implementation of matrix multiplication can speed up many applications.
利用矩阵的最重要的好处是任何数量的颜色转换，可以使用标准的组成矩阵乘法。
The most important advantage of using matrices is that any number of color transformations can be composed using standard matrix multiplication.
我们提出了“指标分布图”的新概念，从而构造出一个估计任意维数矩阵乘法的新算法。
We propose a new conception: Index Distribution Chart, which makes it possible for us to construct a new fast multiplication algorithm for matrix pairs of arbitrary dimensions.
用变量的线性替换解释矩阵乘法，由此可以简洁而且直观地导出初等矩阵和分块矩阵的乘法原理。
From this point of view, we can concisely and intuitively introduce the multiplication principles of elementary matrix and block matrix.
基于多处理机平台TMS32 0C80 （C80 ） ，提出并行矩阵乘法和并行高斯约当消元法。
On the basis of the multiprocessor platform, TMS320C80's programmable structure, the parallel matrix multiplication and parallel Gauss Jordan elimination algorithms are introduced.
如果我们可以利用GPU进行处理，然后我们可以产生一系列线程，真正咀嚼通过矩阵乘法，我们谈到早期(或任何)。
And if we could use the GPU for processing, then we could spawn a bunch of threads and really chew through the matrix multiplication we talked about earlier (or whatever).
虽然我可以做矩阵乘法并行，我必须要小心，我怎么打破这个问题，我不能利用一切可能的并行，因为我通常的工具工作。
Though I can do matrix multiplication in parallel, I have to be careful about how I break up the problem and I can't exploit all the parallelism possible because of the tools I normally work with.
设计了一种新的分组密码算法，该算法利用矩阵乘法的扩散作用与专门设计的一种矩阵运算的混乱作用实现对信息的加密。
A new block cipher is designed where the diffusion of matrix multiplication is combined with the confusion of specially designed matrix operation to encrypt information.
在意识到矩阵表示将导致物理量不满足乘法交换律之前，海森堡并没有前进太远。
Heisenberg had not proceeded very far with this idea before he noticed that it would lead to his physical quantities not satisfying the commutative law of multiplication.
Scilab提供了简单的矩阵运算（比如乘法），也提供了高级运算库（比如复数多维算法和相关）。
Scilab provides simple matrix operations like multiplication, plus a library of high-level operations like complex multi-dimensional arithmetic and correlation.
使用MPI进行并行编程来实现矩阵与向量的乘法。
Parallel programming using MPI to multiply a matrix by a vector.
假设有一个4x4的矩阵，我们希望将其与另外一个4x1的向量进行乘法操作。
Let's say we have a 4x4 matrix that we want to multiply with a vector (a 4x1 matrix).
在大型线性回归分析中，由于设计矩阵的病态使得经典的最小二乘法失去了优良性。
The superiority of the classical least square method is lost due to the morbid of designed matrix in large-type linear regression analysis.
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