Scalar product and the Kronecker delta symbol

In summary, the scalar product ##A\centerdot B## is a scalar and can be proven by forming rotated vectors A' and B' using the rotation of A and B. The scalar product of A' and B' can then be expanded and simplified to show that it is equivalent to the sum of the direction cosines of different, orthogonal lines ##x'_i##. This can also be seen by visualizing the multiplication in ##\sum_j \lambda_{ij} \lambda_{kj} =\delta_{jk}## as being between two rows of matrices, while in ##\sum_i \lambda_{ij} \lambda_{ik}## it is between two columns. This relation can also be
  • #1
Karol
1,380
22
From a textbook. proof that the scalar product ##A\centerdot B## is a scalar:
Vectors A' and B' are formed by rotating vectors A and B:
$$A'_i=\sum_j \lambda_{ij} A_j,\; B'_i=\sum_j \lambda_{ij} B_j$$
$$A' \centerdot B'=\sum_i A'_i B'_i =\sum_i \left( \sum_j \lambda_{ij} A_j \right)\left( \sum_k \lambda_{ik} B_k \right)$$
$$=\sum_{i,k} \left( \sum_{i} \lambda_{ij} \lambda_{ik} \right) A_j B_k=\sum_{j} \left( \sum_{k} \delta_{jk} A_j B_k \right) $$
But:
$$\sum_j \lambda_{ij} \lambda_{kj} =\delta_{jk} $$
The order of the indexes in ##\sum_i \lambda_{ij} \lambda_{ik}## is inverse.
$$\lambda_{ij}=\cos(x'_i,x_j)$$
And, for example, for i=2:
$$\lambda^2_{21}+\lambda^2_{22}+\lambda^2_{23}=1$$
The identical index i=2 comes first, while in ##\sum_{i} \lambda_{ij} \lambda_{ik}## the (should be, or not to be) identical indexes j and k are second and the first index, i, changes.
 
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  • #2
The relation is true regardless of whether the first or the second index is summed over.
 
  • #3
Orodruin said:
The relation is true regardless of whether the first or the second index is summed over.
I know, but why?
When the second index is summed over the expression ##\lambda_{ij} \lambda_{kj}## makes sense, the 3 are the direction cosines of different, orthogonal, lines ##x'_i##:
$$\sum_j \lambda_{ij} \lambda_{kj}=\left\{ \begin{array}{l}
\cos^2(x'_1,x_1)+\cos^2(x'_1,x_2)+\cos^2(x'_1,x_3)=1 \\
\cos(x'_1,x_1)\cos(x'_2,x_1)+\cos(x'_1,x_2)\cos(x'_2,x_2)+\cos(x'_1,x_3)\cos(x'_2,x_3)=0 \\
\cos(x'_1,x_1)\cos(x'_3,x_1)+\cos(x'_1,x_2)\cos(x'_3,x_2)+\cos(x'_1,x_3)\cos(x'_3,x_3)=0 \\
\cos(x'_2,x_1)\cos(x'_1,x_1)+\cos(x'_2,x_2)\cos(x'_1,x_2)+\cos(x'_2,x_3)\cos(x'_1,x_3)=0 \\
\cos^2(x'_2,x_1)+\cos^2(x'_2,x_2)+\cos^2(x'_2,x_3)=1 \\
\cos(x'_2,x_1)\cos(x'_3,x_1)+\cos(x'_2,x_2)\cos(x'_3,x_2)+\cos(x'_2,x_3)\cos(x'_3,x_3)=0 \\
\cos(x'_3,x_1)\cos(x'_1,x_1)+\cos(x'_3,x_2)\cos(x'_1,x_2)+\cos(x'_3,x_3)\cos(x'_1,x_3)=0 \\
\cos(x'_3,x_1)\cos(x'_2,x_1)+\cos(x'_3,x_2)\cos(x'_2,x_2)+\cos(x'_3,x_3)\cos(x'_2,x_3)=0 \\
\cos^2(x'_3,x_1)+\cos^2(x'_3,x_2)+\cos^2(x'_3,x_3)=1
\end{array} \right.$$
But in ##\sum_i \lambda_{ij} \lambda_{ik}## the multiplication in each member is between the cosine direction of the same line, which doesn't have a meaning:
$$\sum_i \lambda_{ij} \lambda_{ik} =\left\{ \begin{array}{l}
\cos^2(x'_1,x_1)+\cos^2(x'_2,x_1)+\cos^2(x'_3,x_1)=? \\
\cos(x'_1,x_1)\cos(x'_1,x_2)+\cos(x'_2,x_1)\cos(x'_2,x_2)+\cos(x'_3,x_1)\cos(x'_3,x_2)=? \\
\cos(x'_1,x_1)\cos(x'_1,x_3)+\cos(x'_2,x_1)\cos(x'_2,x_3)+\cos(x'_3,x_1)\cos(x'_3,x_3)=? \\

\cos(x'_1,x_2)\cos(x'_1,x_1)+\cos(x'_2,x_2)\cos(x'_2,x_1)+\cos(x'_3,x_2)\cos(x'_3,x_1)=? \\
\cos^2(x'_1,x_2)+\cos^2(x'_2,x_2)+\cos^2(x'_3,x_2)=? \\
\cos(x'_1,x_2)\cos(x'_1,x_3)+\cos(x'_2,x_2)\cos(x'_2,x_3)+\cos(x'_3,x_2)\cos(x'_3,x_3)=? \\

\cos(x'_1,x_3)\cos(x'_1,x_1)+\cos(x'_2,x_3)\cos(x'_2,x_1)+\cos(x'_3,x_3)\cos(x'_3,x_1)=? \\
\cos(x'_1,x_3)\cos(x'_1,x_2)+\cos(x'_2,x_3)\cos(x'_2,x_2)+\cos(x'_3,x_3)\cos(x'_3,x_2)=? \\
\cos^2(x'_1,x_3)+\cos^2(x'_2,x_3)+\cos^2(x'_3,x_3)\cos(x'_3,x_1)=? \\

\end{array} \right.$$
This problem can be visualized. the multiplication in ##\sum_j \lambda_{ij} \lambda_{kj} =\delta_{jk}## is between 2 rows of the matrices while in ##\sum_i \lambda_{ij} \lambda_{ik}## the multiplication is between 2 columns.
 
  • #4
Are you familiar with the fact that any matrix commutes with its inverse? In this case, the inverse is the transpose (which follows from the relation you are familiar with).

Naturally, this relation also follows from the fact that it does not matter which system you decided to call the primed system and which system you decided to call unprimed, the completeness relation must be true regardless.
Karol said:
which doesn't have a meaning:
This is wrong, it has a meaning. It is just the decomposition of the unprimed basis in the primed eigenvectors instead of vice versa!
 
  • #5
Orodruin said:
This is wrong, it has a meaning. It is just the decomposition of the unprimed basis in the primed eigenvectors instead of vice versa!
Thanks Orodruin
 

1. What is the scalar product?

The scalar product, also known as the dot product, is a mathematical operation that takes two vectors and returns a single scalar value. It is calculated by multiplying the corresponding components of the two vectors and then adding the results together.

2. How is the scalar product represented mathematically?

The scalar product of two vectors a and b is represented as a · b or a · b = |a||b|cosθ, where |a| and |b| are the magnitude of the vectors and θ is the angle between them.

3. What is the significance of the Kronecker delta symbol in the scalar product?

The Kronecker delta symbol, represented as δij, is a mathematical notation used to represent a specific value. In the context of the scalar product, it is used to denote the components of a vector. For example, ai represents the i-th component of vector a.

4. How can the Kronecker delta symbol be used to simplify the calculation of the scalar product?

The Kronecker delta symbol can be used to simplify the calculation of the scalar product when the two vectors have the same magnitude. In this case, the scalar product can be written as a · b = aibi, where i represents the number of dimensions. This reduces the calculation to a simple sum of the products of the corresponding components of the two vectors.

5. In what fields is the scalar product and the Kronecker delta symbol frequently used?

The scalar product and the Kronecker delta symbol are commonly used in fields such as physics, engineering, and mathematics. They are particularly useful in vector calculus, mechanics, and linear algebra. They also have applications in computer graphics, signal processing, and quantum mechanics.

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