Proof of Congruence Transformation

In summary, for a complex matrix T and dimension nxm, if A of dimension nxn is positive semidefinite, then T*AT >= 0. However, the converse is not true in general. A sufficient condition for the converse to hold is for T to be surjective, i.e. have full row rank. This means that for every n x 1 vector y, there exists an m x 1 vector x such that y = Tx. The next question is whether this condition is also necessary for the converse to hold.
  • #1
azizz
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Consider the following statement

Let T (size: nxm) be a complex matrix. Then if A of dimension nxn is positive semidefinite then T*AT >= 0.

Now I was wondering if the converse is true aswel? In my math book they used the converse statement to proof something, but is it possible to say that if T*AT >= 0 (positive semidefinite) then A>= 0?

Note: I used the symbol * to indicate the Hermittian.

Someone got some tips for me?
 
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  • #2
It's not true in general. Suppose, for example, that T is n x 1. So it's a particular column vector; let's call it [tex]x_0[/tex] to emphasize that fact.

Then in that case, T*AT >= 0 means simply that

[tex]x_0^* A x_0 \geq 0[/tex] (scalar inequality)

for that specific [tex]x_0[/tex] (or any scalar multiple of [tex]x_0[/tex]). But in order to conclude that A is positive semidefinite, you would need

[tex]x^* A x \geq 0[/tex]

for EVERY column vector x.

[edit]: A sufficient condition for the conclusion you desire would be that T must be surjective. Do you see why?
 
  • #3
Thanks for the fast replay. Let me see if i got.

A>=0 implies T*AT>=0 if T has full column rank. That means that for every column (i) of T we have Ti*ATi>=0.

If T did not have full column rank then there are linear dependent columns for which it does not hold that Ti*ATi>=0.
 
  • #4
azizz said:
Thanks for the fast replay. Let me see if i got.

A>=0 implies T*AT>=0 if T has full column rank. That means that for every column (i) of T we have Ti*ATi>=0.

If T did not have full column rank then there are linear dependent columns for which it does not hold that Ti*ATi>=0.

No, that's not right. A >= 0 implies T*AT >= 0 for ANY complex n x m matrix T.

Why is this? Because

x*(T*AT)x = (Tx)*A(Tx) = y*Ay >= 0 because A is positive semidefinite

(here I have defined y = Tx for added clarity)

You asked about the converse: when does T*AT >= 0 imply A >= 0?

A sufficient condition is for T to be surjective, i.e., for T to have full ROW rank. (Not full column rank! The n x 1 example I gave in the last post has full column rank, but the desired result does not hold.)

Why is it sufficient for T to be surjective? Let y be any n x 1 vector. We want

y*Ay >= 0.

Since T is surjective, there exists a (m x 1) vector x such that y = Tx.

Then:

y*Ay = (Tx)*A(Tx) = x*(T*AT)x* >= 0 because T*AT is positive semidefinite. QED.

The next natural question is: is it also NECESSARY for T to be surjective? I'll let you think about that one.
 

FAQ: Proof of Congruence Transformation

What is a congruence transformation?

A congruence transformation is a type of transformation that preserves the shape and size of a geometric figure. It involves moving, rotating, or reflecting the figure in a way that does not change its measurements or angles.

How do you prove congruence transformation?

Congruence transformation can be proved using various methods, such as using congruence postulates or theorems. The most common method is using side-angle-side (SAS) or side-side-side (SSS) congruence criteria, where you compare the lengths of corresponding sides and the measures of corresponding angles of two figures.

What is the difference between congruence transformation and similarity transformation?

The main difference between congruence and similarity transformation is that congruence preserves both shape and size, while similarity only preserves shape. In a congruence transformation, the figures are exactly the same, while in a similarity transformation, the figures are proportional but not necessarily equal.

Can a figure have multiple congruence transformations?

Yes, a figure can have multiple congruence transformations. This is because there are different types of transformations that can result in congruent figures, such as translations, rotations, and reflections. As long as the measurements and angles of the figures remain unchanged, they can be considered congruent.

How is congruence transformation used in real-life applications?

Congruence transformation has various real-life applications, such as in architecture, engineering, and design. For example, architects use congruence transformation to create symmetrical and proportional buildings, while engineers use it to create identical and precise structures. Congruence transformation is also used in computer graphics and animation to create realistic and accurate images.

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