3 x 2 + 3 y 2 + 3 z 2 + 2 x y + 4 x z + 6 y z + 3 x + 2 y + z + 1
Find the minimal value of the function f ( x , y , z ) above, where x , y and z are real numbers. Round your answer to three significant figures.
If you come to the conclusion that no such minimum exists, enter 0.666
This is an Algebra problem; Calculus solutions are frowned upon.
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When faced with a quadratic inequality, conversion into a matrix form will (eventually) yield the relevant sum of squares to consider.
My personal preference is to treat this as an inequality in the 4 variables x , y , z , 1 , which allows us to use a 4 × 4 matrix directly.
IE The expression is equal to
( x y z 1 ) ⎝ ⎜ ⎜ ⎛ 3 1 2 2 3 1 3 3 1 2 3 3 2 1 2 3 1 2 1 1 ⎠ ⎟ ⎟ ⎞ ⎝ ⎜ ⎜ ⎛ x y z 1 ⎠ ⎟ ⎟ ⎞
We then need to find the eigenvectors/values of the center matrix.
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A minor of this 4x4 matrix (as highlighted in red below) is equal to 3 ( 3 ⋅ 3 − 3 ⋅ 3 ) − 1 ( 1 ⋅ 3 − 2 ⋅ 3 ) + 2 ( 1 ⋅ 3 − 2 ⋅ 3 ) = − 3 < 0 , which is negative, then by Slyvester's criterion, there is no minimum value for this function. Am I right? Comrade @Otto Bretscher ⎝ ⎜ ⎜ ⎛ 3 1 2 2 3 1 3 3 1 2 3 3 2 1 2 3 1 2 1 1 ⎠ ⎟ ⎟ ⎞
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Yes, exactly, Comrade... that's what I use in my solution. You can invoke Sylvester's criterion or just point out that there is a negative eigenvalue.
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@Otto Bretscher – Please post another type of this question! I wanna practice on it! Thankssssss
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@Pi Han Goh – Just take any quadratic inequality (without restrictions).
@Pi Han Goh – Ok I posted something.... I'm eagerly awaiting your solution, Comrade! ;)
This is a hyperboloid of two sheets. So it has no minimum value. See reference .
There is no equation (only a function), so, where is the hyperboloid? ;)
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Compare 3 x 2 + 3 y 2 + 3 z 2 + 2 x y + 4 x z + 6 y z + 3 x + 2 y + z + 1 with a x 2 + b y 2 + c z 2 + 2 f y z + 2 g z x + 2 h x y + 2 p x + 2 q y + 2 r z + d = 0
find e, E, rank e, rank E, det E, roots of determinant ((a-x,h,g),(h,b-x,f), (g,f,c-x)) = 0
we get rho3 = 3, rho4 = 4, sgn(Delta) = -1, k = 0
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Sure! I was just giving you a hard time since we don't have an equation "=0" in the problem.
So, now that we know that the surface f ( x , y , z ) = 0 forms a hyperboloid... how does that prove that f ( x , y , z ) isn't bounded below?
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@Otto Bretscher – Set f(x,y,z) = that expression + some constant, do the same calculation, that constant cancelled out in calculation.
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@Pi Han Goh – Are you saying that the level surface f ( x , y , z ) = k is a hyperboloid of two sheets for all constants k ?
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@Otto Bretscher – Doesn't it?
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@Pi Han Goh – I don't think so. But it does not really matter. All we need to know is that the surface f ( x , y , z ) = k is non-empty for all k : The range of f is R and there is no minimum.
It's getting late for me... I'll be back tomorrow. To be continued, Comrade!
Here is a more philosophical question: Do we trust your reference, or do we want to prove things ourselves, from first principles? It seems to me that the relevant fact is the following: If the matrix of the quadratic form (the first six terms of f ) has a negative eigenvalue, then f will not be bounded below.
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@Otto Bretscher – I would have proven them all myself and write it up on some wiki page on this site, and I will just reference it when I write a solution.
But given that I'm still a newbie in matrices and quadratic forms, I don't think I will do a good job on it as of now.
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@Pi Han Goh – I wrote a brief solution... please let me know whether it makes any sense, Comrade @Pi Han Goh
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@Otto Bretscher – Haha!? What?! you're the expert here. I'm already lost in your f ( v ) = … .
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@Pi Han Goh – I'm just writing the input vector x , y , z as v , a column vector.
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@Otto Bretscher – No idea. You literally need to spoodfeed me information at this point.
I still got much to learn...
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@Pi Han Goh – Well, write v = ⎣ ⎡ x y z ⎦ ⎤ and compute v T Q v + b T v + c .... you will see that you end up with f ( x , y , z )
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@Otto Bretscher – I know that. I mean,
why you need to write it in that form? or more accurately, how do you know that f(v) = .... ?
Why does det(Q) < 0 implies that it has negative eigenvalues
What is " let x be a unit eigenvector with eigenvalue " for?
etc
I got so many questions here.
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@Pi Han Goh – (a) We write it in this form to make the computation easier (more compact) (b) The determinant is the product of the eigenvalues (c) For λ to be an eigenvalue means that there exist nonzero vectors w such that Q w = λ w ... those are the eigenvectors. We can pick a unit eigenvector to simplify computation.
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@Otto Bretscher – Exercise Question : Find the minimum/maximum value of W ( x , y , z ) = 2 x 2 + 4 y 2 + 6 z 2 + 8 x y + 1 0 x z + 1 2 y z + 1 4 x + 1 6 y + 1 8 z + 2 0 (if it exists).
Your method : To prove that it has no maximum value
Let W ( v ) = v T Q v + b T v + c , where Q = ⎣ ⎡ 2 4 5 4 4 6 5 6 6 ⎦ ⎤ , b T = [ 1 4 1 6 1 8 ] and c = 2 0 .
Because det ( Q ) = 2 ( 4 ⋅ 6 − 6 ⋅ 6 ) − 4 ( 4 ⋅ 6 − 6 ⋅ 5 ) + 5 ( 4 ⋅ 6 − 4 ⋅ 5 ) = 2 0 > 0 , so that Q has a positive eigenvalue; let x be a unit leading eigenvector with eigenvalue λ . Then W ( k x ) = λ k 2 + ( b T x ) k + c is quadratic function of k with a positive leading coefficient, so W ( x , y , z ) fails to be bounded above. In other words, there is no maximum value of W ( x , y , z ) .
My method : To prove that is has no minimum value either.
Let's find the type of second-order algebraic surface , referencing this, we have
e = Q = 2 0 > 0 , Δ E = ⎣ ⎢ ⎢ ⎡ 2 4 5 7 4 4 6 8 5 6 6 9 7 8 9 2 0 ⎦ ⎥ ⎥ ⎤ = 1 4 0 , ρ 3 = rank e = 3 , ρ 4 = rank E = 4
To find the roots of k 1 , k 2 , k 3 of ⎣ ⎡ 2 − x 4 5 4 4 − x 6 5 6 6 − x ⎦ ⎤ = 0 so that we can determine the value of k such that k : = { 1 if the signs of non-zero k ’s are the same 0 otherwise .
and the roots to the equation below satisfy the condition − x 3 + 1 2 x 2 + 3 3 x + 2 0 = 0 . By Descartes' rule of signs , there is exactly one positive root to this cubic equation only (or just use WolframAlpha ). So k = 0 . Comparing it with value in the Mathworld link as given above , we have
ρ 3 = 3 , ρ 4 = 4 , Δ E = 1 4 0 > 0 ⇒ sgn Δ ( E ) ≡ + , k = 0
to get a hyperboloid of one sheet. So both the domain and range of W ( x , y , z ) is R 3 . Thus, there is no maximum nor minimum value for this function.
Follow-up questions :
( 1 ) Is my method correct too? (This is a rhetorical question)
( 2 ) Using your method, how do you prove that W ( x , y , z ) has no minimum value either?
( 3 ) Is it possible to use partial derivatives to show that W ( x , y , z ) has no minimum nor maximum value too? Because I know how to solve it for 2-dimensional functions , but I don't know any method for 3-dimensional functions or higher. If I recall correctly, there is something to do with Hessian matrix , but I'm not sure how to proceed. Thoughts? (Don't ask me to read that wikipedia page, it explains nothing to me)
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@Pi Han Goh – Just a few quick remarks; I have limited time now (but will have more time later tonight).
I think the main point to make is that all the three methods ("yours", "mine", and the Hessian) are essentially the same: In each case we care about the eigenvalues of the same matrix (your values k 1 , k 2 , k 3 ). The Hessian is twice the matrix of the quadratic form. If that matrix has a negative eigenvalue, then the function fails to be bounded below, as I show in my solution.
The maximum of the function is not an issue. Just let y = z = 0 and you are left with 2 x 2 + 1 4 x + 2 0 , which fails to be bounded above.
Please let me know where you want to go from here.
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@Otto Bretscher – Uggghhhh! I should have thought about y = z = 0 .
I̶'̶v̶e̶ ̶s̶p̶e̶n̶t̶ ̶w̶a̶y̶ ̶t̶o̶o̶ ̶m̶u̶c̶h̶ ̶t̶i̶m̶e̶ ̶t̶h̶i̶n̶k̶i̶n̶g̶ ̶a̶b̶o̶u̶t̶ ̶w̶r̶i̶t̶i̶n̶g̶ ̶u̶p̶ ̶a̶ ̶g̶o̶o̶d̶ ̶s̶o̶l̶u̶t̶i̶o̶n̶ ̶a̶n̶d̶ ̶y̶o̶u̶ ̶b̶e̶a̶t̶ ̶m̶e̶ ̶s̶o̶ ̶e̶a̶s̶i̶l̶y̶.̶
Y U SO SMART?!!?!?
If that matrix has a negative eigenvalue, then the function fails to be bounded below.
How do you prove this statement? Is the converse and/or inverse of this statement true as well?
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@Pi Han Goh – The proof of "If that matrix has a negative eigenvalue, then the function fails to be bounded below" is in my solution: "Let x be a unit eigenvector with negative eigenvalue λ . Then f ( k x ) = λ k 2 + ( b T x ) k + c , a quadratic function in k with negative leading coefficient, so that f fails to be bounded below.
When all eigenvalues are positive, then we do indeed have a minimum, but when the smallest eigenvalue is 0 then it could go either way.
Given your youthful age, you are doing very well understanding this stuff, and you are making enormous progress. It took me a while to get it (more or less), and I'm still learning. Writing my linear algebra text made me really think about these things.
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Let x be a unit eigenvector with negative eigenvalue λ .
Then f ( k x ) = λ k 2 + ( b T x ) k + c , a quadratic function in k with negative leading coefficient, so that f fails to be bounded below.
How does the first paragraph implies the second paragraph? I'm dumbfounded here...
Ditto this statement as well:
When all eigenvalues are positive, then we do indeed have a minimum, but when the smallest eigenvalue is 0 then it could go either way.
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@Pi Han Goh – Here is one way to think about it: After a change of coordinates, we can write the function as λ 1 ( u − a ) 2 + λ 2 ( v − b ) 2 + λ 3 ( w − c ) 2 + d , as long as none of the eigenvalues is 0. If all the eigenvalues are positive, then d is the minimal value. But if one of them is negative, say λ 1 , then we can make v = b and w = c to see that the function fails to be bounded below.
@Pi Han Goh – Another important point that makes life easier: We don't actually have to find the characteristic polynomial and the eigenvalues, thanks to Sylvester's Criterion : If there is a negative principal minor, then there will be a negative eigenvalue. In our case we can use the 2 x 2 minor in the top left, which is -8. We can also see this directly: If we let y = − x and z = 0 , the function becomes − 2 x 2 + (lower terms), which fails to be bounded below.
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@Otto Bretscher – Great! I learned something new todayyyyyy Thankyouuuuu
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Let's write the function in standard form as f ( v ) = v T Q v + b T v + c , where Q = ⎣ ⎡ 3 1 2 1 3 3 2 3 3 ⎦ ⎤ , b T = [ 3 2 1 ] , and c = 1 . Now det ( Q ) = − 3 < 0 , so that Q has a negative eigenvalue λ ; let x be a unit eigenvector with eigenvalue λ . Then f ( k x ) = λ k 2 + ( b T x ) k + c , a quadratic function in k with negative leading coefficient, so that f fails to be bounded below. The answer is 0 . 6 6 6 .