T-2: Difference between revisions

From Disordered Systems Wiki
Jump to navigation Jump to search
 
(291 intermediate revisions by the same user not shown)
Line 1: Line 1:
In this set of problems, we use the replica method to study the equilibrium properties of a prototypical toy model of glasses, the spherical <math>p</math>-spin model.  
<strong>Goal: </strong> deriving the equilibrium phase diagram of the Random Energy Model (REM). Notion such as: freezing transition, entropy crisis, condensation, overlap distribution. <br>
 
<strong> Techniques: </strong> saddle point approximation, thermodynamics, probability theory.
 
<br>


=== Problem 1: getting acquainted with the model ===


In the spherical <math>p</math>-spin model the configurations<math> \vec{S}=(S_1, \cdots, S_N) </math>
== Problems==
=== Problem 2: the REM: freezing transition, condensation & glassiness ===
In this Problem we compute the equilibrium phase diagram of the model, and in particular the quenched free energy density <math>f(\beta):=f_\infty(\beta) </math> which controls the scaling of the typical value of the partition function, <math>Z_N(\beta) \sim e^{-N \beta \, f(\beta) +o(N) } </math>. We show that the free energy density equals to


<center><math>
<math display="block">
  \mathcal{N}(E) = e^{N \Sigma\left( \frac{E}{N}\right) + o(N)}, \quad \quad \Sigma(\epsilon)
f(\beta) =  
=
\begin{cases}
\begin{cases}
\log 2- \epsilon^2 \quad &\text{ if } |\epsilon| \leq \sqrt{\log 2} \\
&- \left( T \log 2 + \frac{1}{2 T}\right) \quad \text{if} \quad T \geq T_f\\
0 \quad &\text{ if } |\epsilon| >\sqrt{\log 2}
& - \sqrt{2 \,\log 2} \quad \text{if} \quad T <T_f
\end{cases}
\end{cases} \quad \quad T_f= \frac{1}{ \sqrt{2 \, \log 2}}.
</math></center>
</math>


The function  <math> \Sigma(\epsilon) </math> is the entropy of the model, and it is sketched in Fig. X. The point where the entropy vanishes, <math> \epsilon=- \sqrt{\log 2} </math>, is the energy density of the ground state, consistently with what we obtained in the lecture. The entropy is maximal at <math> \epsilon=0 </math>: the highest number of configurations have vanishing energy density.  
At <math> T_f </math> a "freezing transition" occurs: in the whole low-temperature phase, the free-energy is “frozen” at the value that it has at the critical temperature <math>T= T_f </math>. <br>


We also characterize the overlap distribution of the model: the overlap between two configurations <math> \vec{\sigma}^\alpha, \vec{\sigma}^\gamma </math> is
<math>q(\vec{\sigma}^\alpha, \vec{\sigma}^\gamma)= \frac{1}{N} \sum_{i=1}^N \sigma_i^\alpha \, \sigma_i^\beta</math>, and its distribution is
<math display="block">
P_{N, \beta}(q)= \sum_{\alpha, \gamma} \frac{e^{-\beta E(\vec{\sigma}^\alpha)}}{Z_N(\beta)}\frac{e^{-\beta E(\vec{\sigma}^\gamma)}}{Z_N(\beta)}\delta(q-q(\vec{\sigma}^\alpha, \vec{\sigma}^\gamma)).
</math>
<br>


<ol>
<ol>
<li> <em> Averages: the annealed entropy.</em> We begin by computing the “annealed" entropy <math> \Sigma^A </math>, which is the function that controls the behaviour of the average number of configurations at a given energy, <math> \overline{\mathcal{N}(E)}= \text{exp}\left(N \Sigma^A\left( \frac{E}{N} \right)+ o(N)\right) </math>. Compute this function using the representation <math> \mathcal{N}(E)dE= \sum_{\alpha=1}^{2^N} \chi_\alpha(E) dE \;</math>  [with <math> \chi_\alpha(E)=1</math> if  <math> E_\alpha \in [E, E+dE]</math> and  <math> \chi_\alpha(E)=0</math> otherwise], together with the distribution  <math> p(E)</math> of the energies of the REM configurations. When does <math> \Sigma^A </math> coincide with the entropy defined above (which we define as the “quenched” entropy in the following)?</li>
<li><em> The freezing transition.</em>  
The partition function the REM reads
<math>
Z_N(\beta) = \sum_{\alpha=1}^{2^N} e^{-\beta E_\alpha}= \int dE \, \mathcal{N}_N(E) e^{-\beta E}.
</math>  
Using the behaviour of the typical value of <math> \mathcal{N}_N </math> determined in Problem 1, derive the free energy of the model (hint: perform a saddle point calculation). What is the order of this thermodynamic transition?  
</li>
</ol>
</ol>
<br>
<br>


<ol start="2">
<ol start="2">
<li><em> Self-averaging.</em> For  <math> |\epsilon| \leq  \sqrt{\log 2} </math> the quantity <math> \mathcal{N} </math> is self-averaging: its distribution concentrates around the average value <math> \overline{\mathcal{N}} </math> when  <math> N \to \infty </math>. Show that this is the case by computing the second moment  <math> \overline{\mathcal{N}^2} </math> and using the central limit theorem. Show that  this is no longer true in the region where the annealed entropy is negative: why does one expect fluctuations to be relevant in this region?</li>
<li><em> Fluctuations, and back to average vs typical.</em> Similarly to what we did for the entropy, one can define an annealed free energy <math> f_{\text{a}}(\beta) </math> from <math> \mathbb{E}({Z_N(\beta)})=e^{- N \beta f_{\text{a}}(\beta) + o(N)} </math>: show that in the whole low-temperature phase this is smaller than the quenched free energy obtained above. Putting all the results together, justify why the average of the partition function in the low-T phase is "dominated by rare events".
</li>
</ol>
</ol>
<br>
<br>


<ol start="3">
<ol start="3">
<li> <em> Rare events vs typical values.</em> For  <math> |\epsilon| > \sqrt{\log 2} </math> the annealed entropy is negative: the average number of configurations with those energy densities is exponentially small in <math> N </math>. This implies that the probability to get configurations with those energy is exponentially small in <math> N </math>: these configurations are rare. Do you have an idea of how to show this, using the expression for  <math> \overline{\mathcal{N}}</math>? What is the typical value of <math> \mathcal{N} </math> in this region? Justify why the point where the entropy vanishes coincides with the ground state energy of the model.</li>
<li> <em> Entropy crisis.</em> What happens to the entropy of the model when the critical temperature is reached, and in the low temperature phase? What does this imply for the partition function <math> Z_N</math>?
</li>
</ol>
</ol>
<br>


<ol start="4"><li><em> Overlap distribution and glassiness.</em>
Justify why in the REM the overlap when  <math> N \to \infty</math> typically can take only values zero and one, leading to
<math display="block">
P_\beta(q)= \lim_{N \to \infty} \mathbb{E}(P_{N, \beta}(q))= \mathbb{E}(I_2) \, \delta(q-1)+ (1-\mathbb{E}(I_2)) \, \delta(q), \quad \quad I_2= \lim_{N \to \infty} \frac{\sum_\alpha z_\alpha^2}{\left(\sum_\alpha z_\alpha\right)^2}, \quad \quad z_\alpha=e^{-\beta E_\alpha}
</math>
Why <math> I_2 </math> can be interpreted as a probability? Using probability arguments, one can compute
<math display="block">
\mathbb{E}({I_2})
=\begin{cases}
0 \quad &\text{if} \quad T>T_f\\
1-\frac{T}{T_f} \quad &\text{if} \quad T \leq T_f
\end{cases}
</math>
Interpret this result; in particular, why is this consistent with the entropy crisis?
</li>
</ol>


'''Comment:''' this analysis of the landscape suggests that in the large  <math> N </math> limit, the fluctuations due to the randomness become relevant when one looks at the bottom of their energy landscape, close to the ground state energy density. We show below that this intuition is correct, and corresponds to the fact that the partition function <math> Z </math> has an interesting behaviour at low temperature.


=== Problem 2: the annealed free energy ===
<!--<em> Hint: </em> For <math> T<T_f </math>, use that <math> \overline{I_2}=\frac{T_f}{T} \frac{d}{d\mu}\log  \int_{0}^\infty (1-e^{-u -\mu u^2})u^{-\frac{T}{T_f}-1} du\Big|_{\mu=0}</math></li> </ol>-->


We now compute the equilibrium phase diagram of the model, and in particular the quenched free energy density <math>f </math> which controls the scaling of the typical value of the partition function, <math>Z \sim e^{-N \beta \, f +o(N) } </math>. We show that the free energy equals to
== Comments==
<center><math>
f =  
\begin{cases}
&- \left( T \log 2 + \frac{1}{4 T}\right) \quad \text{if} \quad T \geq T_c\\
& - \sqrt{\log 2} \quad \text{if} \quad T <T_c
\end{cases} \quad \quad T_c= \frac{1}{2 \sqrt{\log 2}}.
</math></center>
At <math> T_c </math> a transition occurs, often called freezing transition: in the whole low-temperature phase, the free-energy is “frozen” at the value that it has at the critical temperature  <math>T= T_c </math>.


<ol>
<ol>
<li><em> The thermodynamical transition and the freezing.</em>  
<li><em> Glassiness.</em> The low-T phase of the REM is a frozen phase, characterized by the fact that the free energy is temperature independent, and that the typical value of the partition function is very different from the average value. In fact, the low-T phase is also <em> a glass phase, </em> characterized by a non-trivial overlap distribution  <math> P_\beta(q)</math> and by  <math>q_{EA}=1.</math> These quantities can be considered as <em> order parameters </em> of the glass phase.<br></li>
The partition function the REM reads
<math>
Z = \sum_{\alpha=1}^{2^N} e^{-\beta E_\alpha}= \int dE \, \mathcal{N}(E) e^{-\beta E}.
</math>  
Using the behaviour of the typical value of <math> \mathcal{N} </math> determined in Problem 1, derive the free energy of the model (hint: perform a saddle point calculation). What is the order of this thermodynamic transition?
</ol>
</li>
<br>


<ol start="2">
<li><em> Replicas.</em> In the REM, <math> P_\beta(q)</math> can be computed explicitly using probability arguments; in more complicated models that require the machinery of replicas to compute the equilibrium properties, <math> P_\beta(q)</math> will emerge very naturally from such theory. We go back to this concepts in the next sets of problems, where we will also show that the non-zero order parameter <math> P_\beta(q)</math> is associated to a particular form of symmetry breaking, the so called replica symmetry.</li>
<li> <em> Entropy.</em> What happens to the entropy of the model when the critical temperature is reached, and in the low temperature phase? What does this imply for the partition function <math> Z</math>?</li>
</ol>
</ol>
<br>


<ol start="3">
<li><em> Fluctuations, and back to average vs typical.</em> Similarly to what we did for the entropy, one can define an annealed free energy <math> f^A </math> from <math> \overline{Z}=e^{- N \beta f^A + o(N)} </math>: show that in the whole low-temperature phase this is smaller than the quenched free energy obtained above. Putting all the results together, justify why the average of the partition function in the low-T phase is "dominated by rare events".
</ol></li>


<!--[[File:Suscept-experiment.png|thumb|left|x140px| Experimental measurements of the magnetic Field Cooled (top) and Zero-Field Cooled  (bottom) susceptibility in a CuMn spin glass. Figure taken from C. Djurberg, K. Jonason, P. Nordblad, https://arxiv.org/abs/cond-mat/9810314]]
<br>
<br>
<li><em> The physics: susceptibility and experiments.</em> The magnetic susceptibility of the REM can be computed adding a field to the energy function:  <math> E(\vec{\sigma}^\alpha) \to E_\alpha + H \sum_i \sigma^\alpha_i</math>. Redoing the thermodynamics calculation in presence of the field (do its!!) one can show that in the REM
<center><math>
\chi(\beta) = \frac{\partial m(\beta, H) }{\partial H}\Big|_{H=0}=
\begin{cases}
&\frac{\partial \tanh(\beta H) }{\partial H}\Big|_{H=0}= \beta \quad \text{if} \quad T \geq T_f\\
& \frac{\partial \tanh(\beta_f H) }{\partial H}\Big|_{H=0}= \beta_f \quad \text{if} \quad T <T_f
\end{cases}.
</math></center>
where <math>m(\beta, H)</math> is the equilibrium magnetization. Therefore, the susceptibility does not diverge at the transition, as it happens in standard ferromagnets, but becomes flat (recall that what diverges at the spin-glass transition are non-linear susceptibilities, as discussed in Lecture 1 by Alberto). A similar behavior is found in measurements of the Field-Cooled susceptibility in spin glass systems, and it is captured by the Parisi's solution of the SK model with replicas, which therefore captures the physics probed in experiments.
</li>
</ol>-->


'''Comment:''' the low-T phase of the REM is a frozen phase, characterized by the fact that the free energy is temperature independent, and that the typical value of the partition function is very different from the average value. In fact, the low-T phase is also <math> a glass phase </math> in the sense discussed in the lecture. It is characterized by the fact that Replica Symmetry is broken, as one sees explicitly by re-deriving the free energy through the replica method. We go back to this in the next lectures/TDs.
== Check out: key concepts and exercises ==


=== Problem 3: the quenched free energy ===
Freezing transition, typical vs average, domination by rare events, entropy crisis, condensation and extreme events, overlap distribution.


In this final exercise, we show how the freezing transition can be understood in terms of extreme valued statistics (discussed in the lecture) and localization. We consider the energies of the configurations and define <math> E_\alpha= - N \sqrt{\log 2} + \delta E_\alpha </math>, so that
After this lecture, you have all the tools to solve <code>Exercise 5 </code> and <code>Exercise 6 </code>.
<center><math>
{Z} = e^{ \beta N \sqrt{\log 2} }\sum_{\alpha=1}^{2^N} e^{-\beta \delta E_\alpha}= e^{ \beta N \sqrt{\log 2} }\sum_{\alpha=1}^{2^N} z_\alpha
</math></center>
We show that <math> Z </math> is a sum of random variables that become heavy tailed for <math> T < T_c </math>, implying that the central limit theorem is violated and this sum is dominated by few terms, the largest ones. This can be interpreted as the occurrence of localization.


We go back to the overlap distribution for the REM in <code>Exercise 13 </code>.


<ol>
== To know more ==
<li> <em> Heavy tails and concentration.</em> Compute the distribution of the variables <math> \delta E_\alpha </math> and show that for <math> (\delta E)^2/N \ll 1 </math> this is an exponential. Using this, compute the distribution of the  <math> z_\alpha </math> and show that it is a power law,
* Derrida. Random-energy model: limit of a family of disordered models [https://hal.science/hal-03285940v1/document]
<center><math>
* Derrida and Toulouse. Sample to sample fluctuations in the random energy model [https://hal.science/jpa-00232503/document]
p(z)= \frac{c}{z^{1+\mu}} \quad \quad \mu= \frac{2 \sqrt{\log 2}}{\beta}
<!--* Derrida. Random-energy model: An exactly solvable model of disordered systems [http://www.lps.ens.fr/~derrida/PAPIERS/1981/prb81.pdf]-->
</math></center>
When <math> T < T_c </math>, one has  <math> \mu<1 </math>: the distribution of  <math> z </math> becomes heavy tailed. What does this imply for the sum  <math> Z </math>? Is this consistent with the behaviour of the partition function and of the entropy discussed in Problem 2? Why can one talk about a localization or condensation transition?</li>
</ol>
<br>
 
<ol start="2">
<li><em> Inverse participation ratio.</em> The low temperature behaviour of the partition function an be characterized in terms of a standard measure of localization (or condensation), the Inverse Participation Ratio (IPR) defined as:
<center><math>
IPR= \frac{\sum_{\alpha=1}^{2^N} z_\alpha^2}{[\sum_{\alpha=1}^{2^N} z_\alpha]^2}= \sum_{\alpha=1}^{2^N} \omega_\alpha^2 \quad \quad \omega_\alpha=\frac{ z_\alpha}{\sum_{\alpha=1}^{2^N} z_\alpha}.
</math></center>
When <math> z </math> is power law distributed with exponent <math> \mu </math>,  one can show (HOMEWORK!) that
<center><math>
IPR= \frac{\Gamma(2-\mu)}{\Gamma(\mu) \Gamma(1-\mu)}.
</math></center>
Discuss how this quantity changes across the transition at <math> \mu=1 </math>, and how this fits with what you expect in general in a localized phase. 
</li>
</ol>
<br>

Latest revision as of 20:01, 15 February 2026

Goal: deriving the equilibrium phase diagram of the Random Energy Model (REM). Notion such as: freezing transition, entropy crisis, condensation, overlap distribution.
Techniques: saddle point approximation, thermodynamics, probability theory.


Problems

Problem 2: the REM: freezing transition, condensation & glassiness

In this Problem we compute the equilibrium phase diagram of the model, and in particular the quenched free energy density f(β):=f(β) which controls the scaling of the typical value of the partition function, ZN(β)eNβf(β)+o(N). We show that the free energy density equals to

f(β)={(Tlog2+12T)ifTTf2log2ifT<TfTf=12log2.

At Tf a "freezing transition" occurs: in the whole low-temperature phase, the free-energy is “frozen” at the value that it has at the critical temperature T=Tf.

We also characterize the overlap distribution of the model: the overlap between two configurations σα,σγ is q(σα,σγ)=1Ni=1Nσiασiβ, and its distribution is PN,β(q)=α,γeβE(σα)ZN(β)eβE(σγ)ZN(β)δ(qq(σα,σγ)).

  1. The freezing transition. The partition function the REM reads ZN(β)=α=12NeβEα=dE𝒩N(E)eβE. Using the behaviour of the typical value of 𝒩N determined in Problem 1, derive the free energy of the model (hint: perform a saddle point calculation). What is the order of this thermodynamic transition?


  1. Fluctuations, and back to average vs typical. Similarly to what we did for the entropy, one can define an annealed free energy fa(β) from 𝔼(ZN(β))=eNβfa(β)+o(N): show that in the whole low-temperature phase this is smaller than the quenched free energy obtained above. Putting all the results together, justify why the average of the partition function in the low-T phase is "dominated by rare events".


  1. Entropy crisis. What happens to the entropy of the model when the critical temperature is reached, and in the low temperature phase? What does this imply for the partition function ZN?


  1. Overlap distribution and glassiness. Justify why in the REM the overlap when N typically can take only values zero and one, leading to Pβ(q)=limN𝔼(PN,β(q))=𝔼(I2)δ(q1)+(1𝔼(I2))δ(q),I2=limNαzα2(αzα)2,zα=eβEα Why I2 can be interpreted as a probability? Using probability arguments, one can compute 𝔼(I2)={0ifT>Tf1TTfifTTf Interpret this result; in particular, why is this consistent with the entropy crisis?


Comments

  1. Glassiness. The low-T phase of the REM is a frozen phase, characterized by the fact that the free energy is temperature independent, and that the typical value of the partition function is very different from the average value. In fact, the low-T phase is also a glass phase, characterized by a non-trivial overlap distribution Pβ(q) and by qEA=1. These quantities can be considered as order parameters of the glass phase.
  2. Replicas. In the REM, Pβ(q) can be computed explicitly using probability arguments; in more complicated models that require the machinery of replicas to compute the equilibrium properties, Pβ(q) will emerge very naturally from such theory. We go back to this concepts in the next sets of problems, where we will also show that the non-zero order parameter Pβ(q) is associated to a particular form of symmetry breaking, the so called replica symmetry.


Check out: key concepts and exercises

Freezing transition, typical vs average, domination by rare events, entropy crisis, condensation and extreme events, overlap distribution.

After this lecture, you have all the tools to solve Exercise 5 and Exercise 6 .

We go back to the overlap distribution for the REM in Exercise 13 .

To know more

  • Derrida. Random-energy model: limit of a family of disordered models [1]
  • Derrida and Toulouse. Sample to sample fluctuations in the random energy model [2]