Mixing problem and Eigen vectors relationship.

In summary, the mixing protocol for 5 containers of solvent involves repeatedly dividing up and pouring back the contents of the containers in a pre-assigned way to maintain a constant concentration of added chemicals. The distribution of solvent after each repetition can be determined using matrix multiplication, with the components converging to an eigenvector associated with the eigenvalue 1. This eigenvector represents the stable distribution of solvent after many mixes. The other eigenvalues have absolute values less than one, meaning their corresponding components will decrease with each repetition.
  • #1
dionysian
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1

Homework Statement


A mixing protocol for 5 containers of solvent consists of a repetition of the following procedure. The contents
of each container are removed and divided up into pre-defined fractions. The various fractions are then poured back
into the containers in a pre-assigned way. The idea is to continually mix up the solvents in the ve containers, such
that the concentration of added chemicals can be maintained at a constant level.
Don't worry if you did not catch all the details of the previous paragraph. Let us formulate the problem
mathematically, and hopefully that will clarify things for you. Let pn denote the row vector describing how much
solvent is in each container at the nth repetition of the mixing protocol. The rules for dividing up the contents and
then refilling them tell us that pn = Mpn-1, where

M = [0 0 2/7 1/2 1/9
1/5 0 1/7 1/4 1/9
2/5 1/4 1/7 1/4 1/3
0 1/2 2/7 0 1/3
2/5 1/4 1/7 0 1/9 ]

The matrix M is given to us by the person in charge of the mixing protocol. Assume that at the beginning all the
solvent is inside the rst container, that is, p1 = [1; 0; 0; 0; 0]. How can we determine the distribution of the solvent
after the first application of the mixing protocol? Well, we simply use our math, and compute p2 = Mp1, right?
Using matrix multiplication, we may determine p2, then p3, and so on. Now, do the following

(i) Compute the components of pn against n for the rst 15 mixes and draw a graphics showing them. You
probably will need to use a loop. What do you observe in the picture?
(ii) Compute the eigenvalues of M. Check that one of them is 1, whereas the other four have absolute values
that are less than one. The special eigenvalue 1 has an associated column eigenvector that solves the equation
v = Mv. Indicate how this eigenvector is related to the solution pn after many mixes, and hence deduce the
components of v.

Homework Equations





The Attempt at a Solution




I have done step one and got the proper paint distribution with the given instructions. MY ONLY QUESTION is this: I have no idea how the eigenvector associated with the eigenvalue 1 is related to the solution after many mixes.

To be honest i never really understood what the point of eigen vectors where... i have been told they are important and i have read about them online. This is my understanding of them. An eigen vector is the vector whos direction is not changeing under a transform.

Well, that is great and i see how the eigen vector in a rotation is the axis of rotation but there seems to be a more general idea behiend an eigen vector that eludes me. Can some one point my in the right direction on this? Possibly in the direction that would give me a solution to my problem?
 
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  • #2
If you take the initial vector and decompose it into eigenvectors the component with eigenvalue 1 will remain unchanged. The components with eigenvalues less than one will become less and less as the process is repeated.
 

1. What is the relationship between mixing problems and eigen vectors?

The relationship between mixing problems and eigen vectors is that eigen vectors can be used to solve mixing problems. In a mixing problem, we have a system where different substances are mixed together and we want to determine how the concentrations of each substance change over time. By finding the eigen vectors of the mixing matrix, we can understand the long-term behavior of the system and predict the concentrations of each substance.

2. How are eigen vectors used to solve mixing problems?

Eigen vectors are used in solving mixing problems by representing the long-term behavior of the system. The eigen vectors represent the directions in which the concentrations of each substance will change over time. By finding the eigen vectors of the mixing matrix, we can determine the equilibrium concentrations of each substance and understand the overall behavior of the system.

3. Can you give an example of how eigen vectors are used to solve a mixing problem?

Sure, let's say we have a mixing problem with three substances: A, B, and C. The mixing matrix is represented as [0.5 0.2 0.3; 0.3 0.6 0.1; 0.2 0.2 0.6]. By finding the eigen vectors of this matrix, we can determine that the equilibrium concentrations of A, B, and C are 0.5, 0.3, and 0.2 respectively. We can also understand the long-term behavior of the system by looking at the direction of the eigen vectors.

4. Are there any limitations to using eigen vectors in solving mixing problems?

Yes, there are some limitations to using eigen vectors in solving mixing problems. One limitation is that the mixing matrix must be square, meaning it has the same number of rows and columns. Additionally, the mixing matrix must be invertible, meaning it has a non-zero determinant. If these conditions are not met, then eigen vectors cannot be used to solve the mixing problem.

5. Can eigen vectors be used to solve any type of mixing problem?

No, eigen vectors can only be used to solve linear mixing problems. This means that the concentrations of each substance change at a constant rate and do not depend on the concentrations of other substances. If the mixing problem is non-linear, then eigen vectors cannot be used to solve it.

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