Math Methods: help with scalar product properties.

In summary: Overall, it seems that for the given expression to be a valid scalar product, k must be 1. In summary, for the given expression to be a valid scalar product, k must be 1. The scalar product must also satisfy properties such as symmetry and positive-definiteness.
  • #1
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Homework Statement


For what values of k is (scalar product of vectors a and b) = a[itex]_{1}[/itex]b[itex]_{1}[/itex]-a[itex]_{1}[/itex]b[itex]_{2}[/itex]-a[itex]_{2}[/itex]b[itex]_{1}[/itex]+ka[itex]_{2}[/itex]b[itex]_{2}[/itex] a valid scalar product?

The vectors a and b are defined as:

a = a[itex]_{1}[/itex]e[itex]_{1}[/itex] + a[itex]_{2}[/itex]e[itex]_{2}[/itex]

b = b[itex]_{1}[/itex]e[itex]_{1}[/itex] + b[itex]_{2}[/itex]e[itex]_{2}[/itex]

where e[itex]_{1}[/itex] and e[itex]_{2}[/itex] are unit vectors. a and b are also entirely real.


Homework Equations


Well, I know several properties of scalar products that must be true so that seems like the best way to start.

1) The scalar product of a vector (say a or b) with itself must have a real, numerical value corresponding to the dot product of the vector with itself.

2) The scalar product must be linear in each of its two members.

I also know that if the e vectors truly are unit, they will have magnitude of 1 when scalar producted (is that a word?) with themselves. They should also have a scalar product of magnitude 0 when scalar producted with each other if they are orthogonal. However, I have no idea if these unit vectors are necessarily orthonormal by definition or just normal. My reasoning that they aren't necessarily orthonormal is that the vectors that compose a basis of a Hilbert space, much like a set of unit vectors, are not necessarily orthogonal or even normal.

Finally, I know from the Schwarz Inequality that the magnitude of (the scalar product of a and b)[itex]^{2}[/itex] [itex]\leq[/itex](scalar product of a and a)*(scalar product of b and b). However, I am not sure if that helps in this problem.


The Attempt at a Solution


Originally I looked toward property #2 above to solve this problem; that is, the vectors that we are taking a scalar product of need to be linearly independent. However, I cannot see of a way to prove that k must have a specific value based on this fact.

I also found another property in the book that I omitted from the previous section because it is so formatting heavy. For any two vectors (say a and b), if the components of each vector are real then (scalar product of a and b) can be written as:

(scalar product of a and b) = a[itex]_{1}[/itex]b[itex]_{1}[/itex] * (scalar product of e[itex]_{1}[/itex] and e[itex]_{1}[/itex]) + a[itex]_{1}[/itex]b[itex]_{2}[/itex] * (scalar product of e[itex]_{1}[/itex] and e[itex]_{2}[/itex]) + a[itex]_{2}[/itex]b[itex]_{1}[/itex] * (scalar product of e[itex]_{2}[/itex] and e[itex]_{1}[/itex]) + a[itex]_{2}[/itex]b[itex]_{2}[/itex] * (scalar product of e[itex]_{2}[/itex] and e[itex]_{2}[/itex])

I think it must have something to do with this property, as it so closely mimics the form of the problem. So I know that, assuming the unit vectors are normal, their scalar products with themselves are equal to 1. So, matching coefficients, k would just have to also be 1. But this seems far too simple and it fails to explain why the cross scalar products are negative. In fact, the negative, real value of the cross terms seems to tell me that the "unit" vectors are definitely not orthogonal. But now I feel as if I've talked myself in a circle again on what should be a relatively easy problem.

If anyone has any insight on this problem I'd really appreciate it. I've been beating my head against it for quite some time now and it's been bothering me quite a bit. If anyone wishes to know, the problem is from the Arfken Math Methods text. Thanks in advance.

Also, sorry for the shoddy formatting on the scalar products, I could not seem to get the syntax correct for them.
 
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  • #2
Besides linearity, don't you have other properties like symmetry and positive-definiteness that the scalar product has to satisfy?
 

Related to Math Methods: help with scalar product properties.

1. What is the scalar product in math methods?

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 summing the products.

2. What are the properties of the scalar product?

There are several properties of the scalar product, including commutativity, distributivity, and associativity. The commutative property states that the order of the vectors does not affect the result, the distributive property allows for the scalar to be distributed over addition, and the associative property allows for the scalar to be associated with a single vector or with both vectors.

3. How do you calculate the scalar product?

To calculate the scalar product, you first need to ensure that the two vectors have the same number of components. Then, multiply the corresponding components of the two vectors and add the products together. For example, if vector A is (2, 3, 4) and vector B is (5, 6, 7), the scalar product would be 2*5 + 3*6 + 4*7 = 38.

4. What is the geometric interpretation of the scalar product?

The scalar product has a geometric interpretation as well. It represents the projection of one vector onto another, or the length of the shadow of one vector on the other. It can also be used to calculate the angle between two vectors.

5. How is the scalar product used in real life?

The scalar product has many real-life applications, such as in physics, engineering, and computer graphics. It is used to calculate work and energy in physics, to determine the direction and intensity of forces in engineering, and to create 3D models in computer graphics by calculating the angles between vectors.

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