Generalized solutions for the smallest Euclidean norm

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SUMMARY

The discussion centers on finding the generalized solution for the equation Ax=y, where A is a 3x4 matrix with a rank of 3 and y is a 3x1 vector. The solution is expressed as X = x + alpha.n, with X = [-1;0;0;1] + alpha [1;-2;1;0], indicating a non-unique solution due to the nullity of 1. The goal is to determine the value of alpha that minimizes the Euclidean norm of the generalized solution.

PREREQUISITES
  • Linear algebra concepts, specifically matrix rank and nullity
  • Understanding of generalized solutions in the context of linear equations
  • Familiarity with Euclidean norms and optimization techniques
  • Basic knowledge of eigenvectors and their properties
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  • Research methods for minimizing the Euclidean norm of a vector
  • Learn about the properties of eigenvectors in linear algebra
  • Explore techniques for solving underdetermined systems of equations
  • Study the implications of matrix rank and nullity on solution sets
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Mathematicians, engineers, and students studying linear algebra, particularly those interested in optimization problems and generalized solutions in systems of equations.

crazygrey
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Hi folks,

I have to find the generalized solution for the following Ax=y :

[1 2 3 4;0 -1 -2 2;0 0 0 1]x=[3;2;1]

The rank of A is 3 so there is one nullity so the generalized solution is:

X= x+alpha.n (where alpha is a constant , and n represents the nullity)

I found the solution to be:

X= [-1;0;0;1]+ alpha [1;-2;1;0] which is a non-unique solution.

I need to find (alpha) so that the generalized solution, i.e, the eigenvector has the smallest Euclidean norm

Thanks
 
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