Tsallis entropy

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In physics, the Tsallis entropy is a generalization of the standard Boltzmann–Gibbs entropy. It is proportional to the expectation of the q-logarithm of a distribution.

History

The concept was introduced in 1988 by Constantino Tsallis[1] as a basis for generalizing the standard statistical mechanics and is identical in form to Havrda–Charvát structural α-entropy,[2] introduced in 1967 within information theory.

Definition

Given a discrete set of probabilities [math]\displaystyle{ \{p_i\} }[/math] with the condition [math]\displaystyle{ \sum_i p_i=1 }[/math], and [math]\displaystyle{ q }[/math] any real number, the Tsallis entropy is defined as

[math]\displaystyle{ S_q({p_i}) = k \cdot \frac{1}{q-1} \left( 1 - \sum_i p_i^q \right), }[/math]

where [math]\displaystyle{ q }[/math] is a real parameter sometimes called entropic-index and [math]\displaystyle{ k }[/math] a positive constant.

In the limit as [math]\displaystyle{ q \to 1 }[/math], the usual Boltzmann–Gibbs entropy is recovered, namely

[math]\displaystyle{ S_\text{BG} = S_1(p) = -k \sum_i p_i \ln p_i , }[/math]

where one identifies [math]\displaystyle{ k }[/math] with the Boltzmann constant [math]\displaystyle{ k_B }[/math].

For continuous probability distributions, we define the entropy as

[math]\displaystyle{ S_q[p] = {1 \over q - 1} \left( 1 - \int (p(x))^q\, dx \right), }[/math]

where [math]\displaystyle{ p(x) }[/math] is a probability density function.

Cross-entropy

The cross-entropy pendant is the expectation of the negative q-logarithm with respect to a second distribution, [math]\displaystyle{ r }[/math]. So [math]\displaystyle{ \tfrac{1}{q-1}(1 - {\textstyle \sum_i} p_i^q\cdot \tfrac{r_i}{p_i}) }[/math].

Using [math]\displaystyle{ t = q - 1 }[/math], this may be written [math]\displaystyle{ (1 - E_r[p^t])/t }[/math]. For smaller [math]\displaystyle{ t }[/math], values [math]\displaystyle{ p_i^t }[/math] all tend towards [math]\displaystyle{ 1 }[/math].

The limit [math]\displaystyle{ q\to 1 }[/math] computes the negative of the slope of [math]\displaystyle{ E_r[p^t] }[/math] at [math]\displaystyle{ t=0 }[/math] and one recovers [math]\displaystyle{ -{\textstyle \sum_i} r_i \ln p_i }[/math]. So for fixed small [math]\displaystyle{ t }[/math], raising this expectation relates to log-likelihood maximalization.

Properties

Identities

A logarithm can be expressed in terms of a slope through [math]\displaystyle{ \tfrac{d}{dx} p^x = p^{x} \ln p }[/math] resulting in the following formula for the standard entropy:

[math]\displaystyle{ S = -\lim_{x\rightarrow 1}\tfrac{d}{dx} \sum_i p_i^x = -{\textstyle \sum_i} p_i \ln p_i }[/math]

Likewise, the discrete Tsallis entropy satisfies

[math]\displaystyle{ S_q = -\lim_{x\rightarrow 1}D_q \sum_i p_i^x }[/math]

where Dq is the q-derivative with respect to x.

Non-additivity

Given two independent systems A and B, for which the joint probability density satisfies

[math]\displaystyle{ p(A, B) = p(A) p(B),\, }[/math]

the Tsallis entropy of this system satisfies

[math]\displaystyle{ S_q(A,B) = S_q(A) + S_q(B) + (1-q)S_q(A) S_q(B).\, }[/math]

From this result, it is evident that the parameter [math]\displaystyle{ |1-q| }[/math] is a measure of the departure from additivity. In the limit when q = 1,

[math]\displaystyle{ S(A,B) = S(A) + S(B),\, }[/math]

which is what is expected for an additive system. This property is sometimes referred to as "pseudo-additivity".

Exponential families

Many common distributions like the normal distribution belongs to the statistical exponential families. Tsallis entropy for an exponential family can be written [3] as

[math]\displaystyle{ H^T_q(p_F(x;\theta)) = \frac{1}{1-q} \left((e^{F(q\theta)-q F(\theta)}) E_p[e^{(q-1)k(x)}]-1 \right) }[/math]

where F is log-normalizer and k the term indicating the carrier measure. For multivariate normal, term k is zero, and therefore the Tsallis entropy is in closed-form.

Applications

The Tsallis Entropy has been used along with the Principle of maximum entropy to derive the Tsallis distribution.

In scientific literature, the physical relevance of the Tsallis entropy has been debated.[4][5][6] However, from the years 2000 on, an increasingly wide spectrum of natural, artificial and social complex systems have been identified which confirm the predictions and consequences that are derived from this nonadditive entropy, such as nonextensive statistical mechanics,[7] which generalizes the Boltzmann–Gibbs theory.

Among the various experimental verifications and applications presently available in the literature, the following ones deserve a special mention:

  1. The distribution characterizing the motion of cold atoms in dissipative optical lattices predicted in 2003[8] and observed in 2006.[9]
  2. The fluctuations of the magnetic field in the solar wind enabled the calculation of the q-triplet (or Tsallis triplet).[10]
  3. The velocity distributions in a driven dissipative dusty plasma.[11]
  4. Spin glass relaxation.[12]
  5. Trapped ion interacting with a classical buffer gas.[13]
  6. High energy collisional experiments at LHC/CERN (CMS, ATLAS and ALICE detectors)[14][15] and RHIC/Brookhaven (STAR and PHENIX detectors).[16]

Among the various available theoretical results which clarify the physical conditions under which Tsallis entropy and associated statistics apply, the following ones can be selected:

  1. Anomalous diffusion.[17][18]
  2. Uniqueness theorem.[19]
  3. Sensitivity to initial conditions and entropy production at the edge of chaos.[20][21]
  4. Probability sets that make the nonadditive Tsallis entropy to be extensive in the thermodynamical sense.[22]
  5. Strongly quantum entangled systems and thermodynamics.[23]
  6. Thermostatistics of overdamped motion of interacting particles.[24][25]
  7. Nonlinear generalizations of the Schroedinger, Klein–Gordon and Dirac equations.[26]
  8. Blackhole entropy calculation.[27]

For further details a bibliography is available at http://tsallis.cat.cbpf.br/biblio.htm

Generalized entropies

Several interesting physical systems[28] abide by entropic functionals that are more general than the standard Tsallis entropy. Therefore, several physically meaningful generalizations have been introduced. The two most general of these are notably: Superstatistics, introduced by C. Beck and E. G. D. Cohen in 2003[29] and Spectral Statistics, introduced by G. A. Tsekouras and Constantino Tsallis in 2005.[30] Both these entropic forms have Tsallis and Boltzmann–Gibbs statistics as special cases; Spectral Statistics has been proven to at least contain Superstatistics and it has been conjectured to also cover some additional cases.[citation needed]

See also

References

  1. ↑ Tsallis, C. (1988). "Possible generalization of Boltzmann-Gibbs statistics". Journal of Statistical Physics 52 (1–2): 479–487. doi:10.1007/BF01016429. Bibcode: 1988JSP....52..479T. 
  2. ↑ Havrda, J.; Charvát, F. (1967). "Quantification method of classification processes. Concept of structural α-entropy". Kybernetika 3 (1): 30–35. http://dml.cz/bitstream/handle/10338.dmlcz/125526/Kybernetika_03-1967-1_3.pdf. 
  3. ↑ Nielsen, F.; Nock, R. (2012). "A closed-form expression for the Sharma–Mittal entropy of exponential families". Journal of Physics A: Mathematical and Theoretical 45 (3): 032003. doi:10.1088/1751-8113/45/3/032003. Bibcode: 2012JPhA...45c2003N. 
  4. ↑ Cho, A. (2002). "A Fresh Take on Disorder, Or Disorderly Science?". Science 297 (5585): 1268–1269. doi:10.1126/science.297.5585.1268. PMID 12193769. 
  5. ↑ Abe, S.; Rajagopal, A.K. (2003). "Revisiting Disorder and Tsallis Statistics". Science 300 (5617): 249–251. doi:10.1126/science.300.5617.249d. PMID 12690173. 
  6. ↑ Pressé, S.; Ghosh, K.; Lee, J.; Dill, K. (2013). "Nonadditive Entropies Yield Probability Distributions with Biases not Warranted by the Data". Phys. Rev. Lett. 111 (18): 180604. doi:10.1103/PhysRevLett.111.180604. PMID 24237501. Bibcode: 2013PhRvL.111r0604P. 
  7. ↑ Tsallis, Constantino (2009). Introduction to nonextensive statistical mechanics : approaching a complex world (Online-Ausg. ed.). New York: Springer. ISBN 978-0-387-85358-1. 
  8. ↑ Lutz, E. (2003). "Anomalous diffusion and Tsallis statistics in an optical lattice". Physical Review A 67 (5): 051402. doi:10.1103/PhysRevA.67.051402. Bibcode: 2003PhRvA..67e1402L. 
  9. ↑ Douglas, P.; Bergamini, S.; Renzoni, F. (2006). "Tunable Tsallis Distributions in Dissipative Optical Lattices". Physical Review Letters 96 (11): 110601. doi:10.1103/PhysRevLett.96.110601. PMID 16605807. Bibcode: 2006PhRvL..96k0601D. http://discovery.ucl.ac.uk/142750/1/142750.pdf. 
  10. ↑ Burlaga, L. F.; - Viñas, A. F. (2005). "Triangle for the entropic index q of non-extensive statistical mechanics observed by Voyager 1 in the distant heliosphere". Physica A: Statistical Mechanics and Its Applications 356 (2–4): 375. doi:10.1016/j.physa.2005.06.065. Bibcode: 2005PhyA..356..375B. 
  11. ↑ Liu, B.; Goree, J. (2008). "Superdiffusion and Non-Gaussian Statistics in a Driven-Dissipative 2D Dusty Plasma". Physical Review Letters 100 (5): 055003. doi:10.1103/PhysRevLett.100.055003. PMID 18352381. Bibcode: 2008PhRvL.100e5003L. 
  12. ↑ Pickup, R.; Cywinski, R.; Pappas, C.; Farago, B.; Fouquet, P. (2009). "Generalized Spin-Glass Relaxation". Physical Review Letters 102 (9): 097202. doi:10.1103/PhysRevLett.102.097202. PMID 19392558. Bibcode: 2009PhRvL.102i7202P. 
  13. ↑ Devoe, R. (2009). "Power-Law Distributions for a Trapped Ion Interacting with a Classical Buffer Gas". Physical Review Letters 102 (6): 063001. doi:10.1103/PhysRevLett.102.063001. PMID 19257583. Bibcode: 2009PhRvL.102f3001D. 
  14. ↑ Khachatryan, V.; Sirunyan, A.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J.; Fabjan, C. et al. (2010). "Transverse-Momentum and Pseudorapidity Distributions of Charged Hadrons in pp Collisions at √s=7 TeV". Physical Review Letters 105 (2): 022002. doi:10.1103/PhysRevLett.105.022002. PMID 20867699. Bibcode: 2010PhRvL.105b2002K. 
  15. ↑ Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Bergauer, T.; Dragicevic, M.; Erö, J. et al. (2011). "Charged particle transverse momentum spectra in pp collisions at $ √s= 0.9 and 7 TeV". Journal of High Energy Physics 2011 (8): 86. doi:10.1007/JHEP08(2011)086. Bibcode: 2011JHEP...08..086C. 
  16. ↑ Adare, A.; Afanasiev, S.; Aidala, C.; Ajitanand, N.; Akiba, Y.; Al-Bataineh, H.; Alexander, J.; Aoki, K. et al. (2011). "Measurement of neutral mesons in p+p collisions at √s=200 GeV and scaling properties of hadron production". Physical Review D 83 (5): 052004. doi:10.1103/PhysRevD.83.052004. Bibcode: 2011PhRvD..83e2004A. 
  17. ↑ Plastino, A. R.; Plastino, A. (1995). "Non-extensive statistical mechanics and generalized Fokker-Planck equation". Physica A: Statistical Mechanics and Its Applications 222 (1–4): 347–354. doi:10.1016/0378-4371(95)00211-1. Bibcode: 1995PhyA..222..347P. 
  18. ↑ Tsallis, C.; Bukman, D. (1996). "Anomalous diffusion in the presence of external forces: Exact time-dependent solutions and their thermostatistical basis". Physical Review E 54 (3): R2197–R2200. doi:10.1103/PhysRevE.54.R2197. PMID 9965440. Bibcode: 1996PhRvE..54.2197T. 
  19. ↑ Abe, S. (2000). "Axioms and uniqueness theorem for Tsallis entropy". Physics Letters A 271 (1–2): 74–79. doi:10.1016/S0375-9601(00)00337-6. Bibcode: 2000PhLA..271...74A. 
  20. ↑ Lyra, M.; Tsallis, C. (1998). "Nonextensivity and Multifractality in Low-Dimensional Dissipative Systems". Physical Review Letters 80 (1): 53–56. doi:10.1103/PhysRevLett.80.53. Bibcode: 1998PhRvL..80...53L. 
  21. ↑ Baldovin, F.; Robledo, A. (2004). "Nonextensive Pesin identity: Exact renormalization group analytical results for the dynamics at the edge of chaos of the logistic map". Physical Review E 69 (4): 045202. doi:10.1103/PhysRevE.69.045202. PMID 15169059. Bibcode: 2004PhRvE..69d5202B. 
  22. ↑ Tsallis, C.; Gell-Mann, M.; Sato, Y. (2005). "Asymptotically scale-invariant occupancy of phase space makes the entropy Sq extensive". Proceedings of the National Academy of Sciences 102 (43): 15377–82. doi:10.1073/pnas.0503807102. PMID 16230624. Bibcode: 2005PNAS..10215377T. 
  23. ↑ Caruso, F.; Tsallis, C. (2008). "Nonadditive entropy reconciles the area law in quantum systems with classical thermodynamics". Physical Review E 78 (2): 021102. doi:10.1103/PhysRevE.78.021102. PMID 18850781. Bibcode: 2008PhRvE..78b1102C. 
  24. ↑ Andrade, J.; Da Silva, G.; Moreira, A.; Nobre, F.; Curado, E. (2010). "Thermostatistics of Overdamped Motion of Interacting Particles". Physical Review Letters 105 (26): 260601. doi:10.1103/PhysRevLett.105.260601. PMID 21231636. Bibcode: 2010PhRvL.105z0601A. 
  25. ↑ Ribeiro, M.; Nobre, F.; Curado, E. M. (2012). "Time evolution of interacting vortices under overdamped motion". Physical Review E 85 (2): 021146. doi:10.1103/PhysRevE.85.021146. PMID 22463191. Bibcode: 2012PhRvE..85b1146R. https://hal.archives-ouvertes.fr/hal-02881463/file/PRE2012.pdf. 
  26. ↑ Nobre, F.; Rego-Monteiro, M.; Tsallis, C. (2011). "Nonlinear Relativistic and Quantum Equations with a Common Type of Solution". Physical Review Letters 106 (14): 140601. doi:10.1103/PhysRevLett.106.140601. PMID 21561176. Bibcode: 2011PhRvL.106n0601N. 
  27. ↑ Majhi, Abhishek (2017). "Non-extensive statistical mechanics and black hole entropy from quantum geometry". Physics Letters B 775: 32–36. doi:10.1016/j.physletb.2017.10.043. Bibcode: 2017PhLB..775...32M. 
  28. ↑ "Superstatistics in nanoscale electrochemical systems". Proceedings of the National Academy of Sciences 108 (49): 19535–19539. 2011. doi:10.1073/pnas.1109844108. PMID 22106266. Bibcode: 2011PNAS..10819535G. 
  29. ↑ "Superstatistics". Physica A: Statistical Mechanics and Its Applications 322: 267–275. 2003. doi:10.1016/S0378-4371(03)00019-0. Bibcode: 2003PhyA..322..267B. 
  30. ↑ "Generalized entropy arising from a distribution of q indices". Physical Review E 71 (4): 046144. 2005. doi:10.1103/PhysRevE.71.046144. PMID 15903763. Bibcode: 2005PhRvE..71d6144T. 

Further reading

External links



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