Proving det(A) > 0 for A^3 = A + 1 over R using linear algebra

In summary: All I get is that ##x## must satisfy the equation ##x^3= x+1##. But the original statement was ##x^3 = x+1##. In summary, the conversation discusses the proof that if A is an n x n matrix over R such that A^3 = A + 1, then det(A) > 0. The conversation explores various approaches, including using the decomposition of the expression, considering the real function f(x)=det(A-x*1), and looking at the eigenvalues of A. Ultimately, it is determined that the contradiction arises if det(A) < 0, leading to the conclusion that det(A) > 0.
  • #1
Mr Davis 97
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Homework Statement


If A is an n x n matrix over R such that A^3 = A + 1, prove that det(A) > 0 .

Homework Equations

The Attempt at a Solution


So, what I've done is factor the expression to get A(A+1)(A-1) = 1, then taking the determinant of both sides, I get det(A)det(A+1)det(A-1) = 1. I thought that maybe I could argue that maybe if det(A) < 0, then this would lead to a contradiction, but that doesn't seem to be going anywhere.
Should I try to solve it without the explicit use of determinants?
 
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  • #2
What are the eigenvalues of ##\mathbf A## ?
 
  • #3
StoneTemplePython said:
What are the eigenvalues of ##\mathbf A## ?
Wouldn't any eigenvalue have to satisfy the equation ##\lambda^3 = \lambda + 1##? Whose only real solution is approximately 1.3247?
 
  • #4
Mr Davis 97 said:

Homework Statement


If A is an n x n matrix over R such that A^3 = A + 1, prove that det(A) > 0 .

Homework Equations

The Attempt at a Solution


So, what I've done is factor the expression to get A(A+1)(A-1) = 1, then taking the determinant of both sides, I get det(A)det(A+1)det(A-1) = 1. I thought that maybe I could argue that maybe if det(A) < 0, then this would lead to a contradiction, but that doesn't seem to be going anywhere.
Should I try to solve it without the explicit use of determinants?
The decomposition is a good starting point. It tells us that ##\det A = 0## is impossible, so only ##\det A < 0## or ##\det A > 0## are left.
Now assume ##\det A < 0## and consider the real function ##f(x)=\det (A-x \cdot 1)##. Is it continuous? Do you know something about the values at certain points? What does this mean for the other points?

Edit: I wouldn't consider eigenvalues, because you cannot be sure whether they are real or not. So eventually they don't exist.
 
  • #5
Mr Davis 97 said:
Wouldn't any eigenvalue have to satisfy the equation ##\lambda^3 = \lambda + 1##? Whose only real solution is approximately 1.3247?
Indeed. So any possible determinant is a product comprised of this real root (which is positive) raised to some natural number and the product of some conjugate pairs raised to a natural number...
 
  • #6
fresh_42 said:
The decomposition is a good starting point. It tells us that ##\det A = 0## is impossible, so only ##\det A < 0## or ##\det A > 0## are left.
Now assume ##\det A < 0## and consider the real function ##f(x)=\det (A-x \cdot 1)##. Is it continuous? Do you know something about the values at certain points? What does this mean for the other points?

Edit: I wouldn't consider eigenvalues, because you cannot be sure whether they are real or not. So eventually they don't exist.
What can we do with ##f(x) = \det (A - xI)##? It doesn't seem to be a very malleable expression. The only two things I see is that f(0) < 0, by supposition. How does this let us conclude things about f(1) or f(-1)?
 
  • #7
Mr Davis 97 said:
What can we do with ##f(x) = \det (A - xI)##? It doesn't seem to be a very malleable expression. The only two things I see is that f(0) < 0, by supposition. How does this let us conclude things about f(1) or f(-1)?
Well, you also know ##f(0)\cdot f(-1) \cdot f(1) = 1 > 0## and all are real numbers.

Edit: You can also use @StoneTemplePython's approach with conjugate eigenvalues, if this is easier to you. My suggestion is quasi by foot using the properties of continuous real functions.
 
  • #8
fresh_42 said:
Well, you also know ##f(0)\cdot f(-1) \cdot f(1) = 1 > 0## and all are real numbers.

Edit: You can also use @StoneTemplePython's approach with conjugate eigenvalues, if this is easier to you. My suggestion is quasi by foot using the properties of continuous real functions.
Well if ##f(0) < 0## it would have to be the case that ##f(1) \cdot f(-1) < 0##, which is to say that they have different signs, which would mean that we'd have to have a zero of f(x) on the interval ##[-1,1]##, right? Is this a contradiction since there are no eigenvalues of A on this interval?

Also, ##f(x)## is continuous since it is an nth degree polynomial.
 
  • #9
Mr Davis 97 said:
Well if ##f(0) < 0## it would have to be the case that ##f(1) \cdot f(-1) < 0##, which is to say that they have different signs, which would mean that we'd have to have a zero of f(x) on the interval ##[-1,1]##, right? Is this a contradiction since there are no eigenvalues of A on this interval?

Also, ##f(x)## is continuous since it is an nth degree polynomial.
Yes, that's right. We have an ##x## with ##f(x)=0##. Your conclusion is basically correct. I only would try to demonstrate why this is a contradiction. Where does ##f(x)=0## lead to? Where do the eigenvalues come in here?
 
  • #10
fresh_42 said:
Yes, that's right. We have an ##x## with ##f(x)=0##. Your conclusion is basically correct. I only would try to demonstrate why this is a contradiction. Where does ##f(x)=0## lead to? Where do the eigenvalues come in here?
I'm not completely sure where to go with ##\det (A - xI)= 0##. Of course, the x's for which this is true are the eigenvalues of A, but I don't see how this is related to the interval [-1,1]
 
  • #11
What does it mean, if a linear function has zero determinant?
 
  • #12
fresh_42 said:
What does it mean, if a linear function has zero determinant?
That it's not invertible?
 
  • #13
Yes, and thus it has a non-trivial kernel, i.e. a vector ##v## with ##(A-x\cdot 1)(v)=0##. Now you can use ##A^3=A+1## again and apply it on ##v##.
 
  • #14
fresh_42 said:
Yes, and thus it has a non-trivial kernel, i.e. a vector ##v## with ##(A-x\cdot 1)(v)=0##. Now you can use ##A^3=A+1## again and apply it on ##v##.
All I get is that ##x## must satisfy the equation ##x^3= x+1##
 
  • #15
fresh_42 said:
The decomposition is a good starting point. It tells us that ##\det A = 0## is impossible, so only ##\det A < 0## or ##\det A > 0## are left.
Now assume ##\det A < 0## and consider the real function ##f(x)=\det (A-x \cdot 1)##. Is it continuous? Do you know something about the values at certain points? What does this mean for the other points?

Edit: I wouldn't consider eigenvalues, because you cannot be sure whether they are real or not. So eventually they don't exist.

Eigenvalues work perfectly well here. Since ##A^3 - A - I = 0##, ##p(x) = x^3 - x - 1## is the "minimal polynomial" of ##A##, and that means that its roots are all the eigenvalues of ##A##, not counting multiplicity. One of the eigenvalues is ##\lambda_1 > 0##, as we can see from the fact that ##p(x)## changes sign between ##x = 0## and large positive ##x##. Setting ##p'(x) = 0## implies that there are two stationary points, ##x = \pm 1/\sqrt{3}##, and the second-derivative test shows that ##x = -1/\sqrt{3}## is a (local) maximum, while ##x = +1/\sqrt{3}## is a local minimum. Therefore, there can be only one positive root of ##p(x) = 0##. Also, ##p(-1/\sqrt{3}) < 0##, so ##p(x)## never rises above the 0 for ##x < 0##, hence there are no negative real roots. Therefore, the other two eigenvalues are complex conjugates ##\lambda_2## and ##\lambda_3 = \overline{\lambda}_2##, hence ##\lambda_2 \times \lambda_3 > 0##. The characteristic polynomial ##c(x)## of ##A## must be a multiple of ##p(x)## but with the same roots, hence ##c(x) = (x-\lambda_1)^r (x - \lambda_2)^s (x - \lambda_3)^t##. The matrix ##A## is real, so ##c(x)## is a real polynomial and so its complex roots must come in conjugate pairs. This means that we must have have ##s = t \geq 1##. Thus, we can, indeed, determine the sign of the product of the eigenvalues.
 
Last edited:
  • #16
Mr Davis 97 said:
All I get is that ##x## must satisfy the equation ##x^3= x+1##
Yes, and this is a) the solution and b) as I said, the eigenvalue argument by foot. You get that for some real number ##x_0## between ##x=-1## and ##x=1## that it has to hold ##x_0^3-x_0-1 = 0##. Until here you have used, that the function ##f(x)## is continuous, real-valued, that
Mr Davis 97 said:
Well if ##f(0) < 0## it would have to be the case that ##f(1) \cdot f(-1) < 0##, which is to say that they have different signs
and that ##v \neq 0##.

All which is left is to calculate the real number ##x_0## or at least that it has to be greater than ##1##.
 
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  • #17
fresh_42 said:
Yes, and this is a) the solution and b) as I said, the eigenvalue argument by foot. You get that for some real number ##x_0## between ##x=-1## and ##x=1## that it has to hold ##x_0^3-x_0-1 = 0##. Until here you have used, that the function ##f(x)## is continuous, real-valued, that

and that ##v \neq 0##.

All which is left is to calculate the real number ##x_0##.
To calculate that real number, is it okay and necessary to use software?
 
  • #18
Mr Davis 97 said:
To calculate that real number, is it okay and necessary to use software?
That's not me to decide it. I also looked it up on the Wolfram page. O.k., I had ##A+x\cdot I## and ##x_0 = -1.3...## but this doesn't make any difference. I added (basically) "or at least outside of ##[-1,1]##" to my last post. This way one could do it analytically: calculate inflection points or show that there cannot be a zero between ##-1## and ##1## or even use Cardano's formulas. It would of course make some more work than the software solution, but it's possible.

If we have the polynomial ##p(x)=x^3-x-1## then ##p(-1)=-1## and ##p(1)=-1##. Thus if it crossed the ##x-##axis in between, it would have to have a maximum ##x_m## anywhere in between with ##p(x_m)>0##. But this can easily be calculated by differentiation, solving a quadratic formula and evaluation of ##p(x_m)##. So this is one possible way without software.
 
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1. What is the purpose of proving det(A) > 0 for A^3 = A + 1 over R using linear algebra?

The purpose of this proof is to determine the sign of the determinant of a matrix A, which is a key factor in determining the properties and behavior of the matrix. This proof can also provide insights into the nature of solutions to equations involving this matrix.

2. What is the significance of A^3 = A + 1 over R in this proof?

This equation is a key starting point for the proof, as it establishes a relationship between the matrix A and the real number 1/R. This relationship is important for understanding how the determinant of A can be determined and its sign can be determined.

3. How does linear algebra play a role in proving det(A) > 0 for A^3 = A + 1 over R?

Linear algebra is the branch of mathematics that deals with linear equations and their representations in vector spaces. In this proof, linear algebra techniques are used to manipulate and analyze the matrix A in order to determine the sign of its determinant.

4. What are the key steps in this proof?

The key steps in this proof involve rewriting the equation A^3 = A + 1 over R in terms of the determinant of A, manipulating the equation to isolate the determinant, and then using linear algebra techniques to prove that the determinant is greater than zero.

5. Are there any practical applications of this proof?

Yes, there are practical applications of this proof in various fields such as physics, engineering, and economics. The sign of the determinant of a matrix is used to determine the stability of a system, the nature of solutions to equations, and the behavior of a system over time. This proof can also be used to solve various types of equations involving matrices.

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