How can one prove that the maximum entropy occurs

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Homework Help Overview

The discussion revolves around proving that maximum entropy occurs when the probabilities of all microstates are equal. The original poster presents the entropy formula and seeks clarification on the conditions for maximum entropy.

Discussion Character

  • Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants suggest using Lagrange multipliers to enforce the constraint that the sum of probabilities equals one. They discuss taking partial derivatives with respect to the probabilities and the multiplier to derive conditions for maximum entropy.

Discussion Status

The discussion is active, with multiple participants contributing similar approaches involving Lagrange multipliers. There is a recognition of shared reasoning among participants, but no explicit consensus has been reached regarding the proof.

Contextual Notes

Participants are working under the constraint that the sum of probabilities must equal one, and they are exploring the implications of this constraint in the context of maximizing entropy.

ehrenfest
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Homework Statement


I am calculating entropy using the formula:

[tex]S=-\sum_i P_i \ln{P_i}[/tex]

where the sum is over all of the microstates of my system and P_i is the probability to find a particle in microstate i.

How can one prove that the maximum entropy occurs when P_i is the same for all i?

Homework Equations


The Attempt at a Solution

 
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Enforce [itex]\sum_i P_i=1[/itex] with a Lagrange multiplier; that is, extremize
[tex]-\sum_i P_i\ln P_i + k\bigl(\sum_i P_i-1\bigr)[/tex]
with respect to both [itex]P_i[/itex] and [itex]k[/itex].
 
The sum of the P_i=1, that's a constraint. Add a lagrange multiplier to enforce the constraint, like alpha*(sum(P_i)-1). Now take the partial derivatives wrt all variables and set them equal to zero. The partial wrt alpha gives you sum(P_i)-1=0. The partial wrt to P_i gives you an expression for P_i in terms of alpha. alpha is a constant, so all P_i are equal.
 
Avodyne said:
Enforce [itex]\sum_i P_i=1[/itex] with a Lagrange multiplier; that is, extremize
[tex]-\sum_i P_i\ln P_i + k\bigl(\sum_i P_i-1\bigr)[/tex]
with respect to both [itex]P_i[/itex] and [itex]k[/itex].

Great minds think alike. But some are faster.
 

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