Is the Proof for the Nearest Point in a Cone Valid?

In summary, the conversation discusses a problem with a proof given in a book about a cone defined as K = {v | v = -∑iλi gi, where λi ≥ 0, ∀i}. The objective is to prove that for a point u in K, if d = u - f, then g_i^Td ≤ 0, ∀i. The proof given is by contradiction, but the validity of the proof is questioned. The conversation also brings up the issue of uniqueness of u and finding points close to zero. The expert suggests working through an example to better understand the argument and points out that the definition of K allows for points satisfying g_i^Td = t_i < 0 to exist.
  • #1
kaosAD
33
0
I encountered a problem in a book with a proof given. But I am a bit skeptic about it. I hope someone can help shed some light.

Let [tex]\{g_{i}\}[/tex] be a set of vectors and imagine a cone defined as [tex]K = \left\{v \,\bigg|\, v =-\sum_{i}\lambda_{i}g_{i}, \textup{ where }\lambda_{i}\geq 0 \ , \forall i \right\}[/tex].

Let [tex]f \notin K[/tex] and let [tex]u \in K[/tex] be the closest point to [tex]f[/tex]. Obviously [tex]u[/tex] is the projected point of [tex]f[/tex] onto [tex]K[/tex]. The objective is to prove that if [tex] d = u - f [/tex], then [tex]g_{i}^\top d \leq 0, \, \forall i[/tex]. (Note that [tex]d \neq 0[/tex].)

The proof given is by contradiction: Suppose that is not true, that is, [tex]\hat{g}_{i}^\top d = s_{i}[/tex] for some scalar [tex]s_{i} > 0, \, \forall i[/tex], where [tex]\hat{g}_{i}= g_{i}/\|g_{i}\|[/tex]. It is not difficult to see that [tex](u-s_{i}\hat{g}_{i}) \in K, \, \forall i[/tex], i.e., it remains in the cone even by small or large perturbation on the vector [tex]u[/tex]. Now, we shall show the perturbed point has smaller distance. Indeed this is the case since for any [tex]i[/tex],

[tex]
\begin{align*}
\|(u-s_{i}\hat{g}_{i})-f \|^{2} &= \|(u-f)-s_{i}\hat{g}_{i}\|^{2}= \|(u-f)\|^{2}-2 s_{i}\hat{g}_{i}^\top (u-f)+s_{i}^{2}\|\hat{g}_{i}\|^{2} \\
&= \|d\|-2s_{i}\hat{g}_{i}^\top d+s_{i}^{2} \\
&= \|d\|-2s_{i}^{2}+s_{i}^{2} \\
&= \|d\|-s_{i}^{2}\leq \|d\|,
\end{align*}
[/tex]

which contradicts with the assumption that [tex]u[/tex] is the nearest point in [tex]K[/tex] to [tex]f[/tex] -- done!.

All looks good, however if I let [tex]\hat{g}_{i}^\top d = t_{i}[/tex] for which the scalar [tex]t_{i}< 0,\, \forall i[/tex] but sufficiently close to 0 such that [tex](u-t_{i}\hat{g}_{i}) \in K[/tex] for any [tex]i[/tex], then using the same derivation I arrive at [tex]\|(u-t_{i}\hat{g}_{i})-f \|^{2}= \|d\|-t_{i}^{2}\leq \|d\|[/tex] too! This means it can contradict even for the case [tex]g_{i}^\top d < 0[/tex]. I now question the validity of this proof. I welcome your comment.
 
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  • #2
Firstly, the negation of (for all i) is (there exists an i).

Secondly nothing states that the condition of g_i^Td<=0 for all i implies that this determines u uniquely. Thus given such a u with this condition, there may be points closer and lying in the cone. And if there isn't a closer point you won't be able to find things sufficiently close to zero.
 
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  • #3
matt grime said:
Firstly, the negation of (for all i) is (there exists an i).

You mean in the definition of K? But you can't change that.

matt grime said:
Secondly nothing states that the condition of g_i^Td<=0 for all i implies that this determines u uniquely. Thus given such a u with this condition, there may be points closer and lying in the cone. And if there isn't a closer point you won't be able to find things sufficiently close to zero.

Yes, I agree with you that nothing states about the implication but
since K is a cone which is closed and convex, [tex]u \in K[/tex] exists and must be a unique point.
 
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  • #4
No, I do not mean the definition of K. You are doing a proof by contradiction, so what is the negation of the statement you're trying to prove? It is not what you wrote.

And you still haven't justified that in your 'second argument' that you can actually choose things as you claim you can. Just write down a simple example and work out where you go wrong. (For example there is nothing to stop you picking 1-d things, for example the cone {x : x=>1, x in R} and f=0, u=1)
 
  • #5
Right you have the point there:there might not be any point [tex](u - t_i \hat{g}_i) \in K[/tex] such that it satisfies [tex]\hat{g}^\top d = t_i < 0[/tex]. This means the book cannot also claim that the point [tex](u - s_i \hat{g}_i) \in K[/tex] satisfyng [tex]\hat{g}^\top d = s_i > 0[/tex] always exist.
 
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  • #6
It can because of the definition of K. (I admit I've not thought to carefully about this or your attempted counter example, but it is clear that what the line of argument is approximately: that all things are greater than or equal to zero, so if something is not strictly negative, then it is positive, and, say, 1/2 of a positive number is positive again, so in K as well. You really ought to work through an example to see what is going on. It is quite simple, I believe: if z is in K, then so is z+r_ig_i for any positive z_i by the definition of K.)
 
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FAQ: Is the Proof for the Nearest Point in a Cone Valid?

1. What is "Proof Using Shortest Distance"?

"Proof Using Shortest Distance" is a method used in geometry to prove the congruence of two triangles by showing that their corresponding sides and angles are equal.

2. How does "Proof Using Shortest Distance" work?

The method involves drawing perpendicular lines from the vertices of one triangle to the corresponding sides of the other triangle. By showing that these lines are equal in length, and that the angles formed by these lines are also equal, we can prove that the two triangles are congruent.

3. What is the advantage of using "Proof Using Shortest Distance"?

One advantage of this method is that it is relatively simple and straightforward, making it easier to understand and apply compared to other methods of proof.

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No, "Proof Using Shortest Distance" can only be used to prove congruence of triangles that have at least one pair of corresponding sides that are equal in length.

5. Are there any limitations to using "Proof Using Shortest Distance"?

Yes, this method can only be used to prove congruence of triangles in two-dimensional space. It cannot be used for proving congruence in three-dimensional space or for other geometric figures.

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