Min number dependent columns in a matrix

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Discussion Overview

The discussion revolves around determining the minimum number of dependent columns in a binary matrix, particularly in the context of matrices structured as (I | A), where I is an identity matrix and A is a submatrix. The conversation explores the complexity of this problem, potential methods for analysis, and its implications in coding theory.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant inquires about an easy method to determine the minimum number of dependent columns in a binary matrix, suggesting that the solution may depend on the structure of matrix A.
  • Another participant suggests row reduction as a method, but this is challenged by a subsequent post indicating that row reduction only provides the maximum number of independent rows, not dependent columns.
  • A participant provides examples of matrices with the same rank but different minimum numbers of rows needed to form a dependent set, illustrating the complexity of the problem.
  • One participant expresses the belief that finding the minimum number of dependent columns is a hard problem and relates it to finding the maximum number of zeroes in a nontrivial null vector.
  • Another participant mentions that this problem is an active area of research, with probabilistic algorithms being developed to address it for certain classes of matrices.
  • Discussion includes the relationship between the problem and linear coding theory, specifically mentioning the minimum distance for linear codes associated with parity check matrices.
  • Concerns are raised about the practical difficulty of solving this problem by hand, particularly for larger matrices.

Areas of Agreement / Disagreement

Participants generally agree that determining the minimum number of dependent columns is a complex problem, with no consensus on a straightforward solution. Multiple competing views on potential methods and the nature of the problem are present throughout the discussion.

Contextual Notes

Participants note that the problem's complexity increases with larger matrices, and there are unresolved aspects regarding the effectiveness of various proposed methods. The discussion also highlights the dependence on specific matrix structures and the challenges in applying theoretical approaches to practical scenarios.

gonzo
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Is there some easy way to determine the minimum number of dependent columns in a matrix? Assume the matrix entries are binary for convenience.

Let's say I have an m x n, m>n matrix in the form (I | A) So that I is nxn, and A is m-n x n. This obviously depends on A.

So is there some easy method for figuring this out by maniuplating A, or is there just a lot of trial and error?
 
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Row reduction. Look it up.
 
Doesn't work. That only gives you the maximum number of independent rows, which is totally different.
 
Just to demonstrate more clearly why rank probably won't be too useful... consider the following matrices are all rank 3:

[tex]\left(\begin{array}{cccc}<br /> 1 & -1 & 0 & 0 \\<br /> 0 & 1 & -1 & 0 \\<br /> 0 & 0 & 1 & -1 \\<br /> -1 & 0 & 0 & 1<br /> \end{array}\right)[/tex]

[tex]\left(\begin{array}{cccc}<br /> 1 & 0 & 0 & 0 \\<br /> 0 & 1 & 0 & 0 \\<br /> 0 & 0 & 1 & 0 \\<br /> 0 & 0 & 1 & 0<br /> \end{array}\right)[/tex]

[tex]\left(\begin{array}{cccc}<br /> 1 & 0 & 0 & 0 \\<br /> 0 & 1 & 0 & 0 \\<br /> 0 & 0 & 1 & 1 \\<br /> 0 & 0 & 0 & 0<br /> \end{array}\right)[/tex]

but the minimum number of rows you need to form a dependent set is 4, 2, and 1 respectively. (4, 1, and 2 if you want a dependent set of columns)
 
Oh sorry, I wasn't even paying attention.
 
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Yes, I've convinced myself that this is a hard problem in general.

Notice, gonzo, that the problem of finding "the minimum number of dependent columns" is the same as the problem of finding "the most zeroes a nontrivial (right) null vector can have".

If you really want the exact answer, then only approach I can think of at the moment is to compute the intersection of the null space with the various subspaces that have exactly 1 nonzero coordinate, and then with those that have exactly 2 nonzero coordinates, and so forth.
 
Yeah, it seems likely to be an very hard problem that gets out of hand fast with big matrices.

I've done a bit more research on this and it seems an active and important area of research, with some people coming up with probablistic algorithms to try and reduce the time for certain classes of matrices.

I feel much better about having trouble with this initially.

By the way, for anyone interested, this number is the minimum distance for the linear code that this matrix is a parity check matrix for.
 
Ah, that I know is hard! Being a parity check matrix makes the problem a little easier, though: it makes it possible to exhaust through all the null vectors, and allows you to represent the problem as a graph.
 
Yeah, but even for 15 bits, trying to do it by hand on a test for example can take a lot of time. Like being given the parity check matrix for a [15,5] code and tryingto determine the distance. I had initially hoped for something easier then just trying every combination from the bottom up. Wishful thinking.
 

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