TBan-I: Difference between revisions

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<center><math> n(x) = \#\{ i \mid E_{\min} < E_i < E_{\min}+x \} </math></center> that is, the number of random variables lying above the minimum <math>E_{\min}</math> but less than <math>E_{\min}+x</math>. This is itself a random variable. We are interested in its mean value: <center><math> \overline{n(x)} = \sum_{k=0}^{M-1} k \, \text{Prob}[n(x)=k] </math></center>
<center><math> n(x) = \#\{ i \mid E_{\min} < E_i < E_{\min}+x \} </math></center> that is, the number of random variables lying above the minimum <math>E_{\min}</math> but less than <math>E_{\min}+x</math>. This is itself a random variable. We are interested in its mean value: <center><math> \overline{n(x)} = \sum_{k=0}^{M-1} k \, \text{Prob}[n(x)=k] </math></center>


''' The Final goal''' is to show that for large 'M', when the extremes are described by the Gumbel distribution :
''' The Final goal''' is to show that, for large 'M' (when the extremes are described by the Gumbel distribution), you have:
<center><math> \overline{n(x)}  = e^{x/b_M}-1 </math></center>
<center><math> \overline{n(x)}  = e^{x/b_M}-1 </math></center>


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where <math>Q_{M-1}(E) = [1-P(E)]^{M-1}</math>.
where <math>Q_{M-1}(E) = [1-P(E)]^{M-1}</math>.
'''Step 2: the Gumbel limit ''' So far, no approximations have been made. To proceed, we use <math> Q_{M-1}(E)\approx Q_M(E)</math> and its  asymptotics Gumbel form:
'''Step 2: the Gumbel limit ''' So far, no approximations have been made. To proceed, we use <math> Q_{M-1}(E)\approx Q_M(E)</math> and its  asymptotics Gumbel form:

Revision as of 14:12, 31 August 2025

Nei seguente esercizio useremo le notazioni della statistica dei valori estremi usate nel corso.

exercise 1: La distribuzione di Gumbel

esercizio 2: The weakest link

Exercise 3: number of states above the minimum

Definition of n(x):Given a realization of the random energies E1,E2,,EM, define

n(x)=#{iEmin<Ei<Emin+x}

that is, the number of random variables lying above the minimum

Emin

but less than

Emin+x

. This is itself a random variable. We are interested in its mean value:

n(x)=k=0M1kProb[n(x)=k]

The Final goal is to show that, for large 'M' (when the extremes are described by the Gumbel distribution), you have:

n(x)=ex/bM1

Step 1: Exact manipulations: You start from the exact expression for the probability of k states in the interval:

Prob[n(x)=k]=M(M1k)dEp(E)[P(E+x)P(E)]k[1P(E+x)]Mk1

To compute n(x), you must sum over k. Use the identity

k=0M1k(M1k)(AB)kBM1k=(AB)ddAk=0M1(M1k)(AB)kBM1k=(M1)(AB)AM2

to arrive at the form:

n(x)=M(M1)dEp(E)[P(E+x)P(E)](1P(E))M2=MdE[P(E+x)P(E)]dQM1(E)dE

where QM1(E)=[1P(E)]M1.

Step 2: the Gumbel limit So far, no approximations have been made. To proceed, we use QM1(E)QM(E) and its asymptotics Gumbel form: