The Relationship Between Rank and Elements in a Jacobian Matrix

In summary, the conversation discusses the matrix of partial derivatives ##\displaystyle{\frac{\partial y^{j}}{\partial y^{i}}}##, which is a ##p \times p## matrix with a rank less than ##p##. The question is whether a given element of this matrix, such as ##\displaystyle{\frac{\partial y^{1}}{\partial u^{2}}}##, can be written in the form ##\displaystyle{\frac{\partial y^{1}}{\partial u^{2}}=A_{1k_{1}}M_{k_{1}2}}##, where ##A## is a ##p \times p## matrix with a rank less than ##p## and ##M
  • #1
spaghetti3451
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Let the matrix of partial derivatives ##\displaystyle{\frac{\partial y^{j}}{\partial y^{i}}}## be a ##p \times p## matrix, but let the rank of this matrix be less than ##p##.

Does this mean that some given element of this matrix, say ##\displaystyle{\frac{\partial y^{1}}{\partial u^{2}}}##, can be written as

##\displaystyle{\frac{\partial y^{1}}{\partial u^{2}}=A_{1k_{1}}M_{k_{1}2}}##,

where ##A## is a ##p\times p## matrix of rank less than ##p## and ##M## is an arbitrary matrix?
 
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  • #2
The answer to that is trivially yes. Given the element ##\frac{\partial y^1}{\partial u^2}## we define the matrix ##A## to have all zero entries except for
##A_{1k_1}=\frac{\partial y^1}{\partial u^2}## and the matrix ##M## is all zeros except that ##M_{k_12}=1##.

It sounds like you were trying to ask something different and less trivial, but it's not clear what that is.
 
  • #3
Indeed, I am trying to ask something different and less trivial.

Consider the following expression:

##\displaystyle{\alpha_{j_{1}\dots j_{p}}\frac{\partial y^{j_{1}}}{\partial u^{i_{1}}} \dots \frac{\partial y^{j_{p}}}{\partial u^{i_{p}}}du^{i_{1}}\wedge \dots \wedge du^{i_{p}}}##.

My goal is to show that this is zero, if the matrix of partial derivatives is of rank less than the dimension ##p## of the matrix. My approach is to try and rewrite every factor of partial derivatives in the form

##\displaystyle{\frac{\partial y^{j_{1}}}{\partial u^{i_{2}}}=A_{j_{1}k_{1}}M_{k_{1}i_{2}}}##

and play around with indices.
 

What is the rank of the Jacobian matrix?

The rank of the Jacobian matrix is the number of linearly independent rows or columns in the matrix. It is used to determine the dimension of the tangent space at a point in a multivariable function.

How is the rank of the Jacobian matrix calculated?

The rank can be calculated by finding the determinant of the matrix and counting the number of non-zero values. Alternatively, it can also be found by performing row or column operations to reduce the matrix to its echelon form and counting the number of non-zero rows or columns.

What does a high rank of the Jacobian matrix indicate?

A high rank of the Jacobian matrix indicates that the multivariable function is well-behaved and has a unique solution at a given point. It also means that the derivatives of the function with respect to all variables are linearly independent.

What does a low rank of the Jacobian matrix indicate?

A low rank of the Jacobian matrix indicates that the multivariable function is not well-behaved and may have multiple solutions or no solution at a given point. It also means that some of the derivatives of the function with respect to variables are linearly dependent on each other.

Why is the rank of the Jacobian matrix important in mathematics and science?

The rank of the Jacobian matrix is important because it provides information about the behavior and properties of multivariable functions. It is used in various fields such as physics, engineering, and economics to analyze and optimize systems and processes. It also plays a crucial role in solving systems of differential equations and in determining critical points in optimization problems.

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