Is the Inner Product of Matrices Preserved under Vector Multiplication?

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In summary, an inner product of matrices is a mathematical operation that takes two matrices and produces a single scalar value by multiplying their elements and adding the products. It is calculated by multiplying the elements of the first matrix with the elements of the second matrix, and then adding all the products together, or by taking the dot product of the two matrices. This operation is not commutative, meaning that changing the order of the matrices will result in a different inner product value. Preserving the inner product of matrices under vector multiplication is important for performing certain calculations and maintaining the relationships and properties of the original matrices. However, there are exceptions to this rule, such as when one of the matrices is a zero matrix, singular, or not compatible for multiplication
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Chris L T521
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Thanks to those who participated in last week's POTW! Here's this week's problem!

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Problem: Let $A$ and $B$ be $n\times n$ matrices with real entries. Show that $\langle A\mathbf{u},B\mathbf{v}\rangle = \langle \mathbf{u}, A^TB\mathbf{v}\rangle$ for any vectors $\mathbf{v},\mathbf{w}\in\mathbb{R}^n$, where $\langle\cdot,\cdot\rangle$ denotes the standard inner product on $\mathbb{R}^n$.

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This week's question was correctly answered by dwsmith, girdav, and Sudharaka. You can find Sudharaka's solution below.

Take any two vectors \(\mathbf{x},\mathbf{y}\in\mathbb{R}^n\) where \(\mathbf{x}=(x_1,x_2,\cdots,x_n)^{T}\) and \(\mathbf{y}=(y_1,y_2,\cdots,y_n)^{T}\). Then the standard inner product of \(\mathbb{R}^n\) is defined by,

\[\langle\mathbf{x},\mathbf{y}\rangle:=\sum_{i=1}^{n}x_{i}y_{i}=x^{T}y\]Hence we have,\[\langle A\mathbf{u},B\mathbf{v}\rangle =(A\mathbf{u})^{T}(B\mathbf{v})=(\mathbf{u}^{T}A^{T})(B\mathbf{v})~~~~~~~~(1)\]Also,\[\langle \mathbf{u}, A^TB\mathbf{v}\rangle=\mathbf{u}^{T}(A^{T}B\mathbf{v})=(\mathbf{u}^{T}A^{T})(B\mathbf{v})~~~~~~~(2)\]By (1) and (2) we have,\[\langle A\mathbf{u},B\mathbf{v}\rangle = \langle \mathbf{u}, A^TB\mathbf{v}\rangle\]
 

FAQ: Is the Inner Product of Matrices Preserved under Vector Multiplication?

What is the definition of an inner product of matrices?

An inner product of matrices is a mathematical operation that takes two matrices and produces a single scalar value. It is calculated by multiplying the elements of the first matrix with the elements of the second matrix, and then adding all the products together.

How is the inner product of matrices calculated?

The inner product of matrices is calculated by multiplying the elements of the first matrix with the elements of the second matrix, and then adding all the products together. This can also be represented as the dot product of the two matrices, where each element in the resulting matrix is the dot product of the corresponding rows and columns in the two matrices.

Is the inner product of matrices commutative?

No, the inner product of matrices is not commutative. This means that changing the order of the matrices in the multiplication will result in a different inner product value. In other words, A*B will not necessarily be equal to B*A.

What is the significance of preserving the inner product of matrices under vector multiplication?

Preserving the inner product of matrices under vector multiplication is important because it allows us to perform certain calculations, such as finding the angle between two vectors or projecting a vector onto another, without having to calculate the inner product again. It also helps maintain the relationships and properties of the original matrices.

Are there any exceptions to the preservation of the inner product of matrices under vector multiplication?

Yes, there are exceptions to this rule. One exception is when one of the matrices is a zero matrix, in which case the inner product will always be zero. Another exception is when one of the matrices is singular (non-invertible), in which case the inner product may not be preserved. Additionally, the inner product may not be preserved if the matrices have different dimensions or are not compatible for multiplication.

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