Why Does a Singular Matrix Imply Infinite or Zero Solutions?

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Discussion Overview

The discussion revolves around the implications of a singular matrix in the context of the equation Ax = B, where A is a square matrix and x and B are vectors. Participants explore the conditions under which a singular matrix leads to either infinite solutions or no solutions, seeking a deeper understanding of the underlying principles and proofs.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • One participant asks for clarification on why a singular matrix implies either infinite solutions or zero solutions, expressing a desire for a proof.
  • Another participant explains that if A is singular, it does not have an inverse, suggesting that this characteristic affects the nature of the solutions.
  • A different participant elaborates on the concept of linear transformations, stating that a singular matrix maps vectors into a subspace, and if the target vector y is not in that subspace, there is no solution. If y is in the subspace, there are infinitely many solutions due to the presence of a non-zero null space.
  • A later reply expresses appreciation for the clarity of the explanation provided, indicating that the concepts discussed are understood.

Areas of Agreement / Disagreement

Participants generally agree on the implications of singular matrices regarding the nature of solutions, but the discussion remains exploratory without a formal proof being presented. There are no explicit disagreements noted, but the initial request for proof indicates some uncertainty in fully grasping the concepts.

Contextual Notes

The discussion touches on the dimension theorem and the relationship between the rank and nullity of a matrix, but does not resolve the mathematical steps or provide a formal proof of the claims made.

member 428835
hey guys


given [itex]Ax=B[/itex] where A is a square matrix and x and B are vectors, can anyone tell me why a singular matrix (that is, the determinant = 0) implies one of two situations: infinite solutions or zero solutions? a proof would be nice. i read through pauls notes but there was no proof.

thanks all!
 
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If ##\mathbf{A}\vec{x}=\vec{y}## then ##\mathbf{A}^{-1}\vec{y}=\vec{x}## provided the inverse exists.

If the matrix ##\mathbf{A}## is singular, it does not have an inverse.
Another name for it is "degenerate".

What does that tell you about the solutions?
(Think about it in terms of solving simultaneous equations.)
 
An n by n square matrix represents a linear transformation, A, from Rn to Rn. If it is "non-singular", then it maps all of Rn to all of Rn. That is, it is a "one to one" mapping- given any y in Rn there exist a unique x in Rn such that Ax= y.

But we can show that, for any linear transformation, A, from one vector space, U, to another, V, the "image" of A, that is, the set of all vectors y, of the form y= Ax for some x, is a subspace of V and that the "null space" of A, the set of all vectors, x, in U such that Ax= 0, is a subspace of U. Further, we have the "dimension theorem". If "m" is dimension of the image of A (called the "rank" of A) and "n" is the dimension of the nullspace of A (called the "nullity" of A) then m+ n is equal to the dimension of V. In particuar, if U and V have the same dimension, n, and the rank of A is m with m< n, then the nullity of A= m-n> 0.

It is further true that if A(u)= v and u' is in the nullspace of A then A(u+ u')= A(u)+ A(u')= v+ 0= v.

The result of all of that is this: If A is a singular linear transformation from vector space U to vector space V, then it maps U into some subspace of V. If y is NOT in that subspace then there is NO x such that Ax= y. If y is in that subspace then there exist x such that Ax= y but also, for any v in the nullity of A (which has non-zero dimension and so contains an infinite number of vectors) A(x+ v)= y also so there exist an infinite number of such vectors.
 
thanks this makes tons of sense!
 

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