Singular Matrices: Disadvantages, Practical Use & Mass Quantization

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In summary, the conversation discusses the concept of a metric in mathematics, specifically in relation to matrices and singular matrices. A metric is a map that associates each pair of points in a space to a real number. It must satisfy three rules: the distance between two points is zero if they are the same point, the distance is the same regardless of the order of the points, and the distance between two points directly is shorter than going through any intermediate points. The conversation also touches on the use and definition of a metric in different dimensional spaces, as well as its relevance in physics, such as in the metric tensor in general relativity.
  • #1
Antonio Lao
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I am getting all singular matrices in my research on the quantization of one-dimensional space.

I think this is the proper place for me to ask all mathematicians the disadvantage of singular matrix.

I know that singular matrix does not have an inverse since its determinant is zero and hence a metric cannot be defined.

What is the parctical use of a metric besides giving a "distance?"

Zero metric can also mean zero mass in physics. But if all masses are zeros, where do the experimentally determined masses come from?
 
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  • #2
Matrices do have a metric (many in fact) irrespective of whether or not they are singular, the only time d(M,N) is zero if d is a matrix metric is if M=N


metrics yield topologies, but as a metric is a (generalized) notion of 'distance', I'm not sure I understand what you're asking.

singular of a matrix means that the codimension of the image is not zero, that do you for another interpretation? or the dimension of the kernel is non-zero.

anyway, just becuase the determinant is not a metric does not imply there is no metric in the space of matrices. There is an impotant one, and it implies that if M is an invertible matrix, and H is a matrix such that d(M,M+H) < 1/d(M,0) that M+H is invertible too.
 
  • #3
matt grime,

Thanks for helping me again with my math deficit.

Are the following square symmetric matrices singular and do they have metric, inverse, etc.?

[tex]H^{+} = \left(\begin{array}{cc}+1 & -1\\-1 & +1\end{array}\right)[/tex]

and

[tex]H^{-} = \left(\begin{array}{cc}-1 & +1\\+1 & -1\end{array}\right)[/tex]

Isn't nonzero determinant a necessary condition for having a metric?

Please clarify further?

Antonio
 
  • #4
both those are singular matrices yes, but there are still metrics on (2x2) matrices and the metric is not zero on them.

let [a,b,c,d] denote the matrix

|a b|
|c d|

then one norm is

n([a,b,c,d]) = sqrt(|a|^2 + |b|^2 + |c|^2 + |b|^2)

another is the usual operator norm:

n(M) = sup ||M(x)||

where sup is taken over all vectors x of norm 1.

A metric can be defined from a norm:

d(A,B) = n(A-B)


neither matrix has an inverse, but that isn't important really.

nxm matrices are just nxm dimensional real space, where there are norms too.


det isn't a metric - det can be -ve, for instance.

perhaps you should say what you think a metric is?
 
  • #5
matt grime,

Thanks for your continued elucidations.

I am still having difficulty understanding your math notations and their meanings.

The truth is I don't know what a metric is. At first I thought metric defines a "distance" in some space.

This might be a stupid question, can there be a metric in one-dimensional space, in two-dim space, in 3-dim space?

The important question for me is how do we define a metric in one-dim space using a matrix?

Antonio
 
  • #6
If X is some space, a metric is a map [tex]d: X \times X \rightarrow R [/tex] that associates each pair of points in X to a real number, that satisfies

[tex] d(x,x) = 0 [/tex]

[tex]d(x,y) = d(y,x)[/tex]; and

[tex]d(x,z) \le d(x,y) + d(y,z) [/tex]

As long as they obey these laws, metrics don't have to be nice, and there are metric spaces that don't have anything to do with euclid.
 
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  • #7
selfAdjoint,

Your notations are a lot better than matt's but still I'm having troubles with your math logic.

Is d:X x X -->R a one-dim metric? What is the operator x? Is it multiplication?

How to write the map for a 2-dim metric, or a 3-dim metric?

There is a metric in GR where the g11 is 1, g22=-1, g33=-1, g44=-1 and all the other elements are zeros. What is the meaning or purpose of this metric in GR? (another stupid question).

Antonio
 
  • #8
the small x there means cartesian product.

simply what is being got at is that the distance notion requires two inputs - the initial point and the final point; the dsitance is from a point to another point.

I'm sorry that you find my notations unclear, but they are very standard.you ought to know what a norm is and what sup means; they are easily learned ideas; wolfram can help, usually.a metric satisfies 3 rules:

two points are zero distance apart iff they are the same point

the distance from a to b is the same as the distance from b to a,

the distance from a to b directly is shorter than going via nay intermediate point.
How does one define a metric in 1d using a matirx? dunno, because the only matrices from R to R are 1x1, ie R again, and the only singular 1x1 matix is 0... and I've no idea what you are getting at.

i though you wanted a metric on some rxp matrices. of which there are infinitely many.

what do you mean by an n-dim metric? metrics do not have a dimension.i really don't understand what's wrong with my notation; in fact I'm more than slightly offended by the implication. perhaps you should learn what the meanings of the terms you presumne to use are first?
 
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  • #9
Originally posted by Antonio Lao
selfAdjoint,

Your notations are a lot better than matt's but still I'm having troubles with your math logic.

Is d:X x X -->R a one-dim metric? What is the operator x? Is it multiplication?

How to write the map for a 2-dim metric, or a 3-dim metric?

There is a metric in GR where the g11 is 1, g22=-1, g33=-1, g44=-1 and all the other elements are zeros. What is the meaning or purpose of this metric in GR? (another stupid question).

Antonio

[tex]g^{\mu \nu}[/tex] is the metric tensor. The metric between two points [tex]u_{\mu} [/tex] and [tex] v_{\nu}[/tex] is [tex]g^{\mu \nu}u_{\mu}v_{\nu}[/tex] where the Einstein summation condition is observed, i.e. you add up all the terms for [tex]\mu, \nu = 0, 1, 2, 3 [/tex]. This gives you a unique real number. You can verify for your self with examples that this definition of a metric - a real number associated with each pair of points - satisfies the three metric axioms I stated.
 
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  • #10
matt and selfAdjoint,

Thanks to both of you for the valuable helps. These will help me save time but still I have to go on WolframResearch - Eric Weisstein's world of Math and Physics.

At least now I know what to look for. Thanks again.

Antonio
 
  • #11
matt grime,

you quote
_________________

i really don't understand what's wrong with my notation; in fact I'm more than slightly offended by the implication. perhaps you should learn what the meanings of the terms you presumne to use are first?
__________________

There is nothing wrong with your notations, I just can't understand them. The faults is mine. I am trying to learn the meanings and the notations simultaneously and to me it does present a problem. If I stumble on Egyptians hieroglyphics, I would have the same problem. I know there is nothing wrong with the hieroglyphics, the message is there but it is in codes.

Antonio
 
  • #12
ok,my reply was far too nasty in tone anyway and i was going to take it down, or at least edit it.
 
  • #13
matt grime,

It's too late for that, the whole world (the entire universe) saw it.

Antonio
 

What are singular matrices?

A singular matrix is a square matrix that does not have an inverse. This means that it cannot be multiplied by another matrix to produce the identity matrix, which is the matrix equivalent of the number 1.

What are the disadvantages of singular matrices?

The main disadvantage of singular matrices is that they cannot be inverted, which limits their use in certain mathematical operations. They also pose challenges in solving systems of linear equations and can result in errors in calculations.

How are singular matrices used in practical applications?

Singular matrices are commonly used in engineering, physics, and other fields for modeling systems with constraints. They are also used in image processing, computer graphics, and data compression.

What is mass quantization?

Mass quantization is a process of discretizing continuous data into discrete values. It is commonly used in signal processing and data compression, where the continuous data is represented by a finite number of bits or symbols.

How does mass quantization relate to singular matrices?

In some cases, mass quantization can result in matrices becoming singular, which means they do not have an inverse. This can affect the accuracy of the quantized data and the results of any calculations or operations performed on the data.

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