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graphs – Power of adjacency matrix

Let $G$ be a weighted graph with a weight function $w longrightarrow mathbb{R}^{+}$. Let $G’$ denotes the weighted matrix with adjacency matrix

$$A_{G’} = sum_{i=0}^{k} (xA)^{i}$$

where $k$ is integer and $x$ is a variable.

I am not getting what is $A_{G’}$ matrix? Is it contains all walks of length $k$ or is it something else?

How to get non-zero entries of a sparse 3D matrix?

I have a sparse 3D matrix and I want to get the non-zero values. Is there any way to project it into some other space, where it would be easy to get those non-zero entries? Any answers or some source to read from is highly appreciated.

plotting – I want to solve a differential equation in matrix form

I tried something like that. Z is my hamiltonian

n = 5;

σ2 = 0.1;

RR = RandomReal[{-Sqrt[3*σ2], Sqrt[3*σ2]}, n];

Z = Table[
    KroneckerDelta[i - j + 1] + KroneckerDelta[i - j - 1], {i, 1, 
     n}, {j, 1, n}] + DiagonalMatrix[RR];

usol = NDSolveValue[{I D[ψ[x, t], t] == 
    Z.ψ[x, t], ψ[0, t] == 0, ψ[n, t] == 0}, ψ, {t,
    0, 1}]

I solve the problem finding the eigenstates and eigenvalues, then I choose a one site and evolve in time but now I want to solve the differential equation directly to check my results.

linear algebra – A problem about determinant and matrix

Suppose $a_{0},a_{1},a_{2}inmathbb{Q}$, such that the following determinant is zero, i.e.

$
left |begin{array}{cccc}\
a_{0} &a_{1} & a_{2} \
\
a_{2} &a_{0}+a_{1} & a_{1}+a_{2} \
\
a_{1} &a_{2} & a_{0}+a_{1}\
end{array}right|
=0$

Show that $a_{0}=a_{1}=a_{2}=0$

I think it’s equivalent to show that the rank of the matrix is 0, and it’s easy to show the rank cannot be 1.

But I have no idea how to show that the case of rank 2 is impossible. So is there any better idea? Thanks.

linear algebra – Matrix for rotation around a vector without using rodrigues rotation formula

for example how can I write the rotation matrix around the vector $vec v = (1,1,1)$ with the angle 90°.

I searched all the other questions and they all say that rodrigues rotation formula is the way to go but I’m wondering if there’s an easy way for me.

thanks.

linear algebra – How can I find the inverse of a matrix with undetermined variables?

all. I am going to find the inverse of the matrix after the derivatives. But, the system terminates the calculation during the process. So, what is the problem inside? How can I fix it? Many thanks!

The code can be found from the following link.
https://www.wolframcloud.com/download/ellgan101/Published/InverseOfMatrix.nb

If you cannot download the code, please see the attached screen-shot of the code.

Inverse of matrix

matrix – Invert parent transform (doesn’t work for combination of rotation and scale)

My problem

I’m working with Qt3D and my problem is almost exactly like this one:

https://stackoverflow.com/q/60995155/3405291

Understanding the solution

I have a problem understanding the suggested solution. Specifically:

The problem is that the QTransform node does not store the transformation as a general 4x4 matrix. Rather is decomposes the matrix into a 3 transformations that are applied in fixed order:

S – a diagonal scaling matrix

R – the rotation matrix

T – translation

and then applies it in the order T * R * S * X to a point X.

So when the transformation on the parent is M = T * R * S, then the inverse on the child will be M^-1 = S^-1 * R^-1 * T^-1. Setting the inverse on the child QTransform will attempt to decompose it in the same way:

M^-1 = T_i * R_i * S_i = S^-1 * R^-1 * T^-1

That doesn’t work, because particularly S and R don’t commute like this.

I don’t understand the above assertions. Can anyone explain them to me. Just help me realize.

matrices – Applying repeated doubling to an update Matrix

Given a rule to obtain a new $x,y$ positions from an initial $x_0,y_0$

The rule is
$ binom{x’}{y’} = begin{pmatrix}
v_x & -v_y \
v_y & v_x
end{pmatrix}
binom{x}{y}$

$x’ = v_x x – v_y y$

$y’ = v_y x + v_x y$

$f(x,y) = (v_x x – v_y y, v_y x + v_x y)$

Now let’s say I wanted to use this rule to update a position $x_0,y_0$

How can I use repeated doubling to calculate the position after updating it $n$ times without having to manually update the position $n$ times

For instance lets say we started at position $(-10, 0) $ and we wanted to update this position 5 times and our given value for $(u_x,u_y) = (frac{1}{2},frac{1}{2})$

after the first update(n=1) the position would be $(-5,-5)$ and then the 5th update(n=5) the position would be $( frac{5}{4}, frac{5}{4}) $

How could I calculate the position for $( frac{5}{4}, frac{5}{4}) $ without having to calculate n = 2 or n= 3 or n= 4 or simply just using the concept of repeated doubling

linear algebra – Eigenvalues of scaling of matrix

Let $A$ be a (real or complex) square matrix, let $alpha neq 0$.

Is it true that $lambda$ is an eigenvalue of $A$ if and only if and only if $alpha lambda$ is an eigenvalue of $alpha A$?

I think yes, here is why I suppose so: $lambda$ is an eigenvalue of $A ifflambda I- A$ is non injective $iff alpha lambda I – alpha A$ is non injective $iff alpha lambda$ is an eigenvalue of $alpha A$.

Is this correct?

pr.probability – Distribution and Expectation of Inverse of a Random Bernoulli Matrix

This question cropped up as a part of my research. Let us assume a $ntimes n$ random matrix $mathbf{M}$ with elements iid distributed to a Bernoulli distribution that takes values ${0,1}$ with probability $ p = 1/2$.

What I want to know is that what sort of distribution would $mathbf{M}^{-1}$ have? and what could its possible expectation be?

My guess so far has been that the distribution remains the same. I have posted this question on math.stackexchange too.

Tag: matrix