Order of rows in the Jacobian arbitrary?

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In summary, the conversation discusses the use of Jacobians in changing variables for integrals. The main concern is the sign of the Jacobian determinant and how it affects the orientation of the integration. It is noted that the Jacobian determinant can change sign over a region and may need to be reversed in certain cases. However, this is not a common occurrence unless working with more advanced problems or real-world applications.
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geoduck
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I just wanted to verify which order you put the rows of the Jacobian. If your initial variables are (x,y) and change to new variables u(x,y)=f(x,y) and v(x,y)=g(x,y), then you'll get a Jacobian. If this Jacobian is negative, would you change your definition to u(x,y)=g(x,y) and v(x,y)=f(x,y), since dxdy is assumed positive, so dudv needs to be positive?

So can you always get away with putting absolute values on the Jacobian?

My worry is that region of integration might be negative, but the measure positive, when making a change of variables.

An example is [itex]\int_{-5}^{6} f(x)dx [/itex] with [itex]u(x)=-x [/itex].

Then [itex]\int_{-5}^{6} f(x)dx=\int_{5}^{-6}f(-u) (-du) [/itex].

So it would have been a mistake to make the Jacobian positive:

[itex]\int_{-5}^{6} f(x)dx \neq \int_{5}^{-6}f(-u) (|-1|du) [/itex]

Can I say that in all integrals, we automatically put the lowest limit on the bottom, and the highest limit on the top, and make sure all Jacobians are positive? Would that work every time?
 
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The Jacobian determinant contains information about the local region; as it is a function of the local coordinates, it _may_ change sign over a region.

This is discussed here: https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant#Jacobian_determinant

So if the Jacobian determinant is negative everywhere for your region of integration, then the orientation has been reversed ... so you would then reverse the integration order.

If it is positive everywhere, no change required.

If it is ever zero, your coordinate space has collapsed, and you will need to reconsider how to proceed ... thus you would not ordinarily expect to find a Jacobian determinant that changes sign during an integration unless:

(a) you are doing problems from a text on real analysis or differential geometry ...
(b) you are working a "real" problem, from the real world

or

(c) your professor likes trick questions!
 

1. What is the Jacobian matrix and why is it important in scientific research?

The Jacobian matrix is a mathematical tool used to represent the relationship between a set of input variables and a set of output variables. It is particularly important in scientific research because it allows for the analysis of complex systems and the prediction of how changes in one variable may affect the others.

2. How is the order of rows in the Jacobian determined?

The order of rows in the Jacobian is typically determined by the order in which the variables are listed. For example, if the first row represents the derivatives of the output variables with respect to the first input variable, then the second row would represent the derivatives with respect to the second input variable, and so on.

3. Can the order of rows in the Jacobian be changed?

Yes, the order of rows in the Jacobian can be changed. However, it is important to note that this may alter the overall structure and interpretation of the matrix, so it should be done carefully and with consideration of the specific research question at hand.

4. What is the significance of an arbitrary Jacobian matrix?

An arbitrary Jacobian matrix is one where the order of rows and columns is not predetermined and can be chosen at will. This can be useful in certain research contexts where the relationship between input and output variables may not be easily defined or known beforehand.

5. Are there any limitations to using an arbitrary Jacobian matrix?

Yes, there are limitations to using an arbitrary Jacobian matrix. Since the order of rows and columns is not predetermined, it may be difficult to interpret and make meaningful conclusions from the data. Additionally, it may not accurately represent the true relationship between variables, leading to inaccurate predictions and conclusions.

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