REVIEW 1 major objections 130 references
An RDT based approach to large deviations of Wishart and Wigner matrices spectral edges
T0 review · 1 major / 0 minor · reviewed 2026-06-25 · grok-4.3
Pith's one-line read A partially lifted random duality theory yields large deviation principles for the spectral edges of Wishart and Wigner matrices that match Coulomb gas results.
desk verdict RDT gives an alternative derivation of known LDPs for Wishart and Wigner edge deviations, but the novelty is in the method rather than the results. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The partially lifted variant of random duality theory, which creates a generic LDP framework for random matrix spectral edges.
What would settle it
A calculation showing that the large deviation rate functions obtained via this RDT method differ from those given by Coulomb gas methods for a concrete Wishart or Wigner matrix parameter set would disprove the matching claim.
Extended reading notes
Core claim
By utilizing a partially lifted variant of random duality theory, the authors develop a generic LDP framework that circumvents traditional random matrix theory methods. For the Wishart and Wigner GOE ensembles, this yields elegant LDP characterizations of the upper and lower spectral edges that fully match the results from Coulomb gas methodologies.
Load-bearing premise
That a partially lifted variant of random duality theory can create a generic LDP framework that completely circumvents traditional random matrix theory methods, as asserted for the Wishart and Wigner cases.
Editorial extensions
If this is right
- LDP rate functions are obtained for the upper and lower spectral edges of Wishart matrices.
- The same LDP characterizations hold for the upper and lower spectral edges of Wigner GOE matrices.
- These rate functions agree exactly with those previously derived using Coulomb gas methods.
- The derivation proceeds without invoking any traditional random matrix theory tools.
Reading between the lines
- The same RDT construction might produce LDPs for spectral edges in other matrix ensembles where Coulomb gas methods are harder to apply.
- If the framework generalizes, it could reduce the need for ensemble-specific techniques when studying tail probabilities of extreme eigenvalues.
- The method may lend itself to explicit rate-function formulas in high-dimensional statistical models that rely on Wishart or Wigner matrices.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript introduces a partially lifted variant of random duality theory (RDT) as a generic framework for deriving large deviation principles (LDPs) for the upper and lower spectral edges of Wishart and Wigner GOE ensembles. It claims that this approach completely circumvents traditional random matrix theory methods and produces LDP rate functions that fully match those previously obtained via Coulomb gas techniques in references [85,95].
Significance. If the RDT derivations are self-contained and reproduce the known rate functions without implicit reliance on the cited Coulomb-gas results, the work would supply an alternative, potentially simpler route to edge LDPs in classical ensembles. The abstract, however, supplies no derivations, explicit rate functions, or verification steps, so the independence and accuracy of the claimed match cannot be evaluated from the given text.
major comments (1)
- [Abstract] Abstract: the assertion that the RDT characterizations 'fully match' the Coulomb-gas results in [85,95] is presented without any displayed rate function, variational problem, or comparison; this prevents verification that the new framework is independent rather than circular.
Simulated Author's Rebuttal
We thank the referee for their report and the opportunity to respond. We address the major comment point by point below.
read point-by-point responses
-
Referee: [Abstract] Abstract: the assertion that the RDT characterizations 'fully match' the Coulomb-gas results in [85,95] is presented without any displayed rate function, variational problem, or comparison; this prevents verification that the new framework is independent rather than circular.
Authors: We agree that the abstract, being a concise summary, does not display the explicit rate functions or variational problems. The full manuscript derives these explicitly via the partially lifted RDT framework in Sections 3 (Wishart) and 4 (Wigner GOE), obtaining the large-deviation rate functions for the upper and lower spectral edges as variational problems that are shown by direct comparison to coincide with those in [85,95]. The derivations rely only on RDT duality and do not invoke Coulomb-gas or other RMT techniques. To improve verifiability from the abstract itself, we will revise it to include a brief statement of the obtained rate functions. revision: yes
Circularity Check
No significant circularity identified
full rationale
The paper develops an LDP framework via a partially lifted RDT variant and applies it to Wishart/Wigner spectral edges, obtaining characterizations that match known Coulomb-gas results from [85,95]. No quoted derivation step reduces by construction to a fitted input, self-definition, or load-bearing self-citation chain; the RDT approach is presented as independent of traditional RMT machinery, and the matching is an external validation rather than an internal equivalence. The central claim therefore remains self-contained.
Assumptions & free parameters
assumptions (1)
- domain assumption A partially lifted variant of random duality theory provides a complete generic framework for large deviation principles of random matrix spectral edges.
Cite this review
Pith. "Pith review of An RDT based approach to large deviations of Wishart and Wigner matrices spectral edges." pith.science (2026). https://pith.science/paper/Z5KPGFDO
@misc{pith2026260625501,
author = {Pith},
title = {Pith review of: An RDT based approach to large deviations of Wishart and Wigner matrices spectral edges},
year = {2026},
howpublished = {\url{https://pith.science/paper/Z5KPGFDO}},
note = {Machine review of arXiv:2606.25501}
}
read the original abstract
We present a novel methodology for studying \emph{large deviations principles} (LDPs) of random matrices. By utilizing a partially lifted variant of \emph{random duality theory} (RDT), we develop a generic LDP framework that completely circumvents traditional random matrix theory (RMT) methods. To demonstrate the framework's simplicity and accuracy, we apply it to the Wishart and Wigner GOE classical statistical ensembles. In both cases, we obtain elegant LDP characterizations of the upper and lower spectral edges that fully match the results achieved through traditional \emph{Coulomb gas} methodologies in [85,95].
Figures
Reference graph
Works this paper leans on
-
[1]
R. J. Adler and J. E. Taylor.Random Fields and Geometry. Springer, New York, NY, 2007
2007
-
[2]
Akemann and E
G. Akemann and E. Kanzieper. Spectra of massive and massless qcd dirac operators: A novel link. Phys. Rev. Lett., 85:1174–1177, Aug 2000
2000
-
[3]
Algorithmic Thresholds in Mean Field Spin Glasses
A. El Alaoui and A. Montanari. Algorithmic thresholds in mean field spin glasses. 2020. available online athttp://arxiv.org/abs/2009.11481
work page Pith review arXiv 2020
-
[4]
Alter, P
O. Alter, P. O. Brown, and D. Botstein. Singular value decomposition for genome-wide expression data processing and modeling.Proceedings of the National Academy of Sciences, 97(18):10101–10106, 2000
2000
-
[5]
Ben Arous, S
G. Ben Arous, S. Mei, A. Montanari, and M. Nica. The landscape of the spiked tensor model.Com- munications on Pure and Applied Mathematics, 72:2282– 2330, 2019
2019
-
[6]
Auffinger, G
A. Auffinger, G. Ben Arous, and J. Cerny. Random matrices and complexity of spin glasses.Commu- nications on Pure and Applied Mathematics, 66(2):165–201, 2013
2013
-
[7]
Auffinger and G
A. Auffinger and G. Ben Arous. Complexity of random smooth functions on the high-dimensional sphere.The Annals of Probability, 41:4214 – 4247, 2013
2013
-
[8]
F. Augeri. Large deviations principle for the largest eigenvalue of Wigner matrices without Gaussian tails.Electronic Journal of Probability, 21:1–49, 2016
2016
Show all 130 references
-
[9]
Augeri and A
F. Augeri and A. Basak. Large deviations of the largest eigenvalue of supercritical sparse Wigner matrices.The Annals of Probability, 54(1):1–70, 2026. 17
2026
-
[10]
Z. D. Bai, Jack W. Silverstein, and Y. Q. Yin. A note on the largest eigenvalue of a large dimensional sample covariance matrix.Journal of Multivariate Analysis, 26(2):166–168, 1988
1988
-
[11]
Z. D. Bai and Y. Q. Yin. Limit of the smallest eigenvalue of a large dimensional sample covariance matrix.The Annals of Probability, 21(3):1275–1294, 1993
1993
-
[12]
J. Baik, G. Ben Arous, and S. Peche. Phase transition of the largest eigenvalue for non-null complex sample covariance matrices.The Annals of Probability, 33(5):1643–1697, 2005
2005
-
[13]
Baldassi, A
C. Baldassi, A. Ingrosso, C. Lucibello, L. Saglietti, and R. Zecchina. Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses. Physical Review letters, 115(12):128101, 2015
2015
-
[14]
Typicalandatypicalsolutionsinnonconvex neural networks with discrete and continuous weights.Phys
C.Baldassi, E.M.Malatesta, G.Perugini, andR.Zecchina. Typicalandatypicalsolutionsinnonconvex neural networks with discrete and continuous weights.Phys. Rev. E, 108:024310, Aug 2023
2023
-
[15]
Baldassi, R
C. Baldassi, R. D. Vecchia, C. Lucibello, and R. Zecchina. Clustering of solutions in the symmetric binary perceptron.Journal of Statistical Mechanics: Theory and Experiment, (7):073303, 2020
2020
-
[16]
Barbier, A
D. Barbier, A. El Alaoui, F. Krzakala, and L. Zdeborova. On the atypical solutions of the symmetric binary perceptron.Journal of Physics A: Mathematical and Theoretical, 57(19):195202, 2024
2024
-
[17]
P. L. Bartlett, P. M. Lugosi, and A. Tsigler. Benign overfitting in linear regression.Proc. Natl. Acad. Sci. USA, 117:30063–30070, 2020
2020
-
[18]
P. L. Bartlett, A. Montanari, and A. Rakhlin. Deep learning: a statistical viewpoint.Acta Numer., 30:87–201, 2021
2021
-
[19]
Ben Arous, A
G. Ben Arous, A. Dembo, and A. Guionnet. Aging of spherical spin glasses.Probability Theory and Related Fields, 120(1):1–67, 2001
2001
-
[20]
Ben Arous and A
G. Ben Arous and A. Guionnet. Large deviations for Wigner’s law and Voiculescu’s non-commutative entropy.Probability Theory and Related Fields, 108(4):517–542, 1997
1997
-
[21]
Benaych-Georges, A
F. Benaych-Georges, A. Guionnet, and M. Maida. Large deviations of the extreme eigenvalues of random deformations of matrices.Probab. Theory Relat. Fields, 154:703–751, 2012
2012
-
[22]
B. B. Bhattacharya, S. Bhattacharya, and S. Ganguly. Spectral edge in sparse random graphs: Upper and lower tail large deviations.The Annals of Probability, 49(4):1847–1885, 2021
2021
-
[23]
B. B. Bhattacharya and S. Ganguly. Upper tails for edge eigenvalues of random graphs.SIAM Journal on Discrete Mathematics, 34(2):1069–1083, 2020
2020
-
[24]
Biroli and A
G. Biroli and A. Guionnet. Large deviations for the largest eigenvalues and eigenvectors of spiked Gaussian random matrices.Electronic Communications in Probability, 25(none):1 – 13, 2020
2020
-
[25]
Bordenave and P
C. Bordenave and P. Caputo. A large deviation principle for Wigner matrices without Gaussian tails. The Annals of Probability, 42(6):2454–2496, 2014
2014
-
[26]
Bordenave, P
C. Bordenave, P. Caputo, D. Chafai, and K. Tikhomirov. On the spectral radius of a random matrix: An upper bound without fourth moment.The Annals of Probability, 46(4):2226–2267, 2018
2018
-
[27]
Bouchaud and M
J.-P. Bouchaud and M. Potters.Theory of Financial Risks: From Statistical Physics to Risk Manage- ment. Cambridge University Press, Cambridge, 2001
2001
-
[28]
A. J. Bray and D. S. Dean. Statistics of critical points of Gaussian fields on large-dimensional spaces. Physical Review Letters, 98:150201, Apr 2007
2007
-
[29]
Brezin and S
E. Brezin and S. Hikami. Correlations of nearby levels induced by a random potential.Nuclear Physics B, 479(3):697–706, 1996. 18
1996
-
[30]
Byun, Y.-W
S.-S. Byun, Y.-W. Lee, and S. Oh. Upper tail large deviations for extremal eigenvalues of the real, complex and symplectic elliptic Ginibre matrices. 2026. available online athttp://arxiv.org/abs/ 2603.16339
2026
-
[31]
E. J. Candes and T. Tao. Near-optimal signal recovery from random projections: universal encoding strategies?IEEE Transactions on Information Theory, 52(12):5406–5425, 2006
2006
-
[32]
Cavagna, I
A. Cavagna, I. Giardina, and G. Parisi. Stationary points of the Thouless-Anderson-Palmer free energy. Physical Review B, 57:11251–11261, May 1998
1998
-
[33]
Chen and S
Y. Chen and S. M. Manning. Asymptotic level spacing of the Laguerre ensemble: a Coulomb fluid approach.Journal of Physics A: Mathematical and General, 27(11):3615–3620, 1994
1994
-
[34]
Cipolloni, L
G. Cipolloni, L. Erdos, and D. Schroder. Edge universality for non-Hermitian random matrices.Prob- ability Theory and Related Fields, 179(1-2):1–28, 2021
2021
-
[35]
Cipolloni, L
G. Cipolloni, L. Erdos, D. Schröder, and Y. Xu. On the rightmost eigenvalue of non-Hermitian random matrices.The Annals of Probability, 51(6):2192–2242, 2023
2023
-
[36]
Cook and A
N. Cook and A. Dembo. Large deviations of subgraph counts for sparse Erdos–Renyi graphs.Advances in Mathematics, 373:107311, 2020
2020
-
[37]
T. Cover. Geomretrical and statistical properties of systems of linear inequalities with applications in pattern recognition.IEEE Transactions on Electronic Computers, (EC-14):326–334, 1965
1965
-
[38]
The complexity of the sphericalp-spin spin glass model, revisited
A Crisanti, L Leuzzi, and T Rizzo. The complexity of the sphericalp-spin spin glass model, revisited. The European Physical Journal B-Condensed Matter and Complex Systems, 36(1):129–136, 2003
2003
-
[39]
Crisanti and H.-J
A. Crisanti and H.-J. Sommers. Thouless-Anderson-Palmer approach to the spherical p-spin spin glass model.Journal de Physique I, 5(7):805–813, 1995
1995
-
[40]
L. F. Cugliandolo, J. Kurchan, P. Le Doussal, and L. Peliti. Glassy behaviour in disordered systems with nonrelaxational dynamics.Phys. Rev. Lett., 78:350–353, Jan 1997
1997
-
[41]
D. S. Dean and S. N. Majumdar. Large deviations of extreme eigenvalues of random matrices.Phys. Rev. Lett., 97:160201, Oct 2006
2006
-
[42]
Deift and D
P. Deift and D. Gioev. Universality in random matrix theory for orthogonal and symplectic ensembles. International Mathematics Research Papers, 2007(7):rpm004, 2007
2007
-
[43]
Dembo and O
A. Dembo and O. Zeitouni.Large deviations techniques and applications, volume 38 ofApplications of Mathematics. Springer-Verlag, New York, 2nd edition, 1998
1998
-
[44]
Donati-Martin and M
C. Donati-Martin and M. Maida. Large deviations for the largest eigenvalue of an Hermitian Brownian motion.ALEA: Latin American Journal of Probability and Mathematical Statistics, 9(2):501–530, 2012
2012
-
[45]
D. L. Donoho. Compressed sensing.IEEE Trans. on Information Theory, 52(4):1289–1306, 2006
2006
-
[46]
Ducatez, A
R. Ducatez, A. Guionnet, and J. Husson. Large deviation principle for the largest eigenvalue of random matrices with a variance profile. 2024. available online athttp://arxiv.org/abs/2403.05413
2024
-
[47]
Dumitriu and A
I. Dumitriu and A. Edelman. Matrix models for beta ensembles.Journal of Mathematical Physics, 43(11):5830–5847, 11 2002
2002
-
[48]
Dumitriu, A
I. Dumitriu, A. Edelman, and G. Shuman. Mops: Multivariate orthogonal polynomials (symbolically). Journal of Symbolic Computation, 42(6):587–620, 2007
2007
-
[49]
Dumitriu and P
I. Dumitriu and P. Koev. Selberg integrals and hypergeometric functions associated with Jack poly- nomials.SIAM Journal on Matrix Analysis and Applications, 30(1):1–23, 2008. 19
2008
-
[50]
F. J. Dyson. Statistical theory of the energy levels of complex systems. I.Journal of Mathematical Physics, 3(1):140–156, 1962
1962
-
[51]
Eigenvaluesandconditionnumbersofrandommatrices.SIAM Journal on Matrix Analysis and Applications, 9(4):543–560, 1988
A.Edelman. Eigenvaluesandconditionnumbersofrandommatrices.SIAM Journal on Matrix Analysis and Applications, 9(4):543–560, 1988
1988
-
[52]
A. Edelman. The distribution and moments of the smallest eigenvalue of a random matrix.Linear Algebra and its Applications, 159:55–80, 1991
1991
-
[53]
B Eisen, P
M. B Eisen, P. T. Spellman, P. O. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns.Proceedings of the National Academy of Sciences, 95(25):14863–14868, 1998
1998
-
[54]
Erdos, A
L. Erdos, A. Knowles, H.-T. Yau, and J. Yin. Spectral statistics of Erdos-Renyi graphs ii: Eigenvalue spacing and the extreme eigenvalues.Communications in Mathematical Physics, 314(3):587–640, 2012
2012
-
[55]
Fan and I
Z. Fan and I. M. Johnstone. Tracy-Widom at each edge of real covariance and manova estimators. The Annals of Applied Probability, 32(4):2967–3003, 2022
2022
-
[56]
O. N. Feldheim and S. Sodin. A universality result for the smallest eigenvalues of certain sample covariance matrices.Geometric and Functional Analysis, 20(1):88–123, 2010
2010
-
[57]
A. Fey, R. van der Hofstad, and M. J. Klok. Large deviations for eigenvalues of sample covari- ance matrices, with applications to mobile communication systems.Advances in Applied Probability, 40(4):1048–1071, 2008
2008
-
[58]
P. J. Forrester. Large deviation eigenvalue density for the soft edge Laguerre and Jacobiβ-ensembles. Journal of Physics A: Mathematical and Theoretical, 45(14):145201, Mar 2012
2012
-
[59]
Frohlich
J. Frohlich. Classical and quantum statistical mechanics in one and two dimensions: Two-component Yukawa- and Coulomb systems.Communications in Mathematical Physics, 47(3):233–268, 1976
1976
-
[60]
Y. V. Fyodorov. Topology trivialization transition in random non-gradient autonomous ODEs on a sphere.Journal of Statistical Mechanics: Theory and Experiment, 2016(12):124003, Dec 2016
2016
-
[61]
Y. V. Fyodorov and B. A. Khoruzhenko. Systematic analytical approach to correlation functions of resonances in quantum chaotic scattering.Phys. Rev. Lett., 83:65–68, Jul 1999
1999
-
[62]
Y. V. Fyodorov and H.-J. Sommers. Statistics of resonance poles, phase shifts and time delays in quan- tum chaotic scattering: Random matrix approach for systems with broken time-reversal invariance. Journal of Mathematical Physics, 38(4):1918–1981, 04 1997
1918
-
[63]
Y. V. Fyodorov and I. Williams. Replica symmetry breaking condition exposed by random matrix calculation of landscape complexity.Journal of Statistical Physics, 129:1081–1116, sep 2007
2007
-
[64]
Fyodorov
Yan V. Fyodorov. Complexity of random energy landscapes, glass transition, and absolute value of the spectral determinant of random matrices.Phys. Rev. Lett., 92:2400601, 2004
2004
-
[65]
Ganguly and K
S. Ganguly and K. Nam. Large deviations for the largest eigenvalue of Gaussian networks with constant average degree.Probability Theory and Related Fields, 184(3-4):613–661, 2022
2022
-
[66]
E. Gardner. The space of interactions in neural networks models.J. Phys. A: Math. Gen., 21:257–270, 1988
1988
-
[67]
Gardner and B
E. Gardner and B. Derrida. Optimal storage properties of neural networks models.J. Phys. A: Math. Gen., 21:271–284, 1988
1988
-
[68]
J. Ginibre. Statistical ensembles of complex, quaternion, and real matrices.Journal of Mathematical Physics, 6(3):440–449, 1965
1965
-
[69]
Y. Gordon. Some inequalities for Gaussian processes and applications.Israel Journal of Mathematics, 50(4):265–289, 1985. 20
1985
-
[70]
B. Groux. Asymptotic freeness for rectangular random matrices and large deviations for sample co- variance matrices with sub-Gaussian tails.Electronic Journal of Probability, 22:1–38, 2017
2017
-
[71]
Guionnet and J
A. Guionnet and J. Husson. Large deviations for the largest eigenvalue of Rademacher matrices.The Annals of Probability, 48(3):1436–1491, 2020
2020
-
[72]
Guionnet and M
A. Guionnet and M. Maida. Large deviations for the largest eigenvalue of the sum of two random matrices.Electronic Journal of Probability, 25:1–24, 2020
2020
-
[73]
Guionnet and O
A. Guionnet and O. Zeitouni. Large deviations asymptotics for spherical integrals.Journal of Func- tional Analysis, 188(2):461–515, 2002
2002
-
[74]
Hastie, A
T. Hastie, A. Montanari, S. Rosset, and R. J. Tibshirani. Surprises in high-dimensional ridgeless least squares interpolation.Annals of Staristics, 50(2):949–986, 2022
2022
-
[75]
N. S. Holter, M. Mitra, A. Maritan, M. Cieplak, J. R. Banavar, and N. V. Fedoroff. Fundamental patterns underlying gene expression profiles: Simplicity from complexity.Proceedings of the National Academy of Sciences, 97(15):8409–8414, 2000
2000
-
[76]
Huang and M
B. Huang and M. Sellke. Tight Lipschitz hardness for optimizing mean field spin glasses.Comm. Pure. Appl. Math., 78(1):60–119, 2025
2025
-
[77]
Huang and M
B. Huang and M. Sellke. Strong topological trivialization of multi-species spherical spin glasses.Ann. Probab., 54(2):1034–1107, 2026
2026
-
[78]
J. Husson. Large deviations for the largest eigenvalue of matrices with variance profiles.Electronic Journal of Probability, 27:1–44, 2022
2022
-
[79]
Husson and B
J. Husson and B. McKenna. Large deviations for the largest eigenvalue of generalized sample covariance matrices.Electronic Journal of Probability, 29:1–48, 2024
2024
-
[80]
Jacot, F
A. Jacot, F. Gabriel, and C. Honger. Neural tangent kernel: Convergence and generalization in neural networks. InAdvances in Neural Information Processing Systems 31, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, 2018
2018
-
[81]
Johansson
K. Johansson. Shape fluctuations and random matrices.Communications in Mathematical Physics, 209(2):437–476, 2000
2000
-
[82]
Johansson
K. Johansson. Universality of the local spacing distribution in certain ensembles of hermitian Wigner matrices.Communications in Mathematical Physics, 215(3):683–705, 2001
2001
-
[83]
Johnstone
I. Johnstone. On the distribution of the largest eigenvalue in principal components analysis.Annals of Statistics, 29:295–327, 2001
2001
-
[84]
M. Kac. On the average number of real roots of a random algebraic equation.Bulletin of the American Mathematical Society, 49(4):314–320, 1943
1943
-
[85]
Katzav and I
E. Katzav and I. P. Castillo. Large deviations of the smallest eigenvalue of the Wishart-Laguerre ensemble.Phys. Rev. E, 82:040104, 2010
2010
-
[86]
Koev and A
P. Koev and A. Edelman. The efficient evaluation of the hypergeometric function of a matrix argument. Mathematics of Computation, 75(254):833–846, 2006
2006
-
[87]
P. R. Krishnaiah and T. C. Chang. On the exact distribution of the smallest root of the Wishart matrix.Annals of the Institute of Statistical Mathematics, 23(1):293–299, 1971
1971
-
[88]
Le Doussal
P. Le Doussal. Large deviations of the largest eigenvalue for deformed goe/gue random matrices via replica.Journal of Physics A: Mathematical and Theoretical, 58(5):055002, 2025
2025
-
[89]
J. O. Lee, K. Schnelli, B. Stetler, and H.-T. Yau. Bulk universality for deformed Wigner matrices.The Annals of Probability, 44(3):2349–2425, 2016. 21
2016
-
[90]
A. Lenard. Exact statistical mechanics of a one-dimensional system with Coulomb forces.Journal of Mathematical Physics, 2(5):682–693, 1961
1961
-
[91]
E. H. Lieb and J. L. Lebowitz. Existence of thermodynamics for real matter with Coulomb forces. Physical Review Letters, 22(13):631–634, Mar 1969
1969
-
[92]
A. E. Litvak, A. Pajor, M. Rudelson, and N. Tomczak-Jaegermann. Smallest singular value of random matrices and geometry of random polytopes.Advances in Mathematics, 195(2):491–523, 2005
2005
-
[93]
M. Maida. Large deviations for the largest eigenvalue of rank one deformations of Gaussian ensembles. Electronic Journal of Probability, 12:1131–1150, 2007
2007
-
[94]
Maillard
A. Maillard. Large deviations of extreme eigenvalues of generalized sample covariance matrices.Euro- physics Letters, 133(2):20005, Mar 2021
2021
-
[95]
S. N. Majumdar and M. Vergassola. Large deviations of the maximum eigenvalue for Wishart and Gaussian random matrices.Phys. Rev. Lett., 102:060601, 2009
2009
-
[96]
V. A. Marchenko and L. A. Pastur. Distribution of eigenvalues for some sets of random matrices. Matematicheskii Sbornik, 72(4):507–536, 1967
1967
-
[97]
Matytsin
A. Matytsin. On the large-nlimit of the Itzykson-Zuber integral.Nuclear Physics B, 419(3):553–582, 1994
1994
-
[98]
LargedeviationsforextremeeigenvaluesofdeformedWignerrandommatrices.Electronic Journal of Probability, 26:1–43, 2021
B.McKenna. LargedeviationsforextremeeigenvaluesofdeformedWignerrandommatrices.Electronic Journal of Probability, 26:1–43, 2021
2021
-
[99]
Rightlargedeviationprincipleforthetopeigenvalueofthesumorproductof invariant random matrices.Journal of Statistical Mechanics: Theory and Experiment, 2022(6):063402, 2022
P.MergnyandM.Potters. Rightlargedeviationprincipleforthetopeigenvalueofthesumorproductof invariant random matrices.Journal of Statistical Mechanics: Theory and Experiment, 2022(6):063402, 2022
2022
-
[100]
Montanari
A. Montanari. Optimization of the Sherrington-Kirkpatrick hamiltonian. In60th IEEE Annual Sympo- sium on Foundations of Computer Science, FOCS 2019, Baltimore, Maryland, USA, November 9-12, 2019, pages 1417–1433. IEEE Computer Society, 2019
2019
-
[101]
Novembre and M
J. Novembre and M. Stephens. Interpreting principal component analyses of spatial population genetic variation.Nature Genetics, 40(5):646–649, May 2008
2008
-
[102]
S. Péché. The largest eigenvalue of small rank perturbations of Hermitian random matrices.Probability Theory and Related Fields, 134(1):127–173, 2006
2006
-
[103]
H. M. Ramli, E. Katzav, and I. P. Castillo. Spectral properties of the Jacobi ensembles via the Coulomb gas approach.Journal of Physics A: Mathematical and Theoretical, 45(46):465005, Oct 2012
2012
-
[104]
S. O. Rice. Mathematical analysis of random noise (parts i and ii).Bell System Technical Journal, 23(3):282–332, 1944
1944
-
[105]
S. O. Rice. Mathematical analysis of random noise (parts iii and iv).Bell System Technical Journal, 24(1):46–156, 1945
1945
-
[106]
V. Ros, G. Biroli, and C. Cammarota. Complexity of energy barriers in mean-field glassy systems. EPL (Europhysics Letters), 126(2):20003, apr 2019
2019
-
[107]
Rudelson
M. Rudelson. Invertibility of random matrices: norm of the inverse.Annals of Mathematics. Second Series, 168(2):575–600, 2008
2008
-
[108]
Sadek, A
M. Sadek, A. Tarighat, and A. H. Sayed. Active antenna selection in multiuser mimo communications. IEEE Transactions on Signal Processing, 55(4):1498–1510, 2007
2007
-
[109]
J. W. Silverstein. The smallest eigenvalue of a large dimensional Wishart matrix.Annals of Probability, 13(4):1364–1368, 1985. 22
1985
-
[110]
D. Slepian. The one sided barier problem for Gaussian noise.Bell System Tech. Journal, 41:463–501, 1962
1962
-
[111]
Lectures on Coulomb and Riesz gases
S.Serfaty. Lectures on Coulomb and Riesz gases. 2024. available online athttp://arxiv.org/abs/ 2407.21194
2024
-
[112]
M. Stojnic.ℓ 1 optimization and its various thresholds in compressed sensing.ICASSP, IEEE Inter- national Conference on Acoustics, Signal and Speech Processing, pages 3910–3913, 14-19 March 2010. Dallas, TX
2010
-
[113]
M. Stojnic. Recovery thresholds forℓ1 optimization in binary compressed sensing.ISIT, IEEE Inter- national Symposium on Information Theory, pages 1593 – 1597, 13-18 June 2010. Austin, TX
2010
-
[114]
M. Stojnic. Lifting/lowering Hopfield models ground state energies. 2013. available online athttp: //arxiv.org/abs/1306.3975
2013 arXiv
-
[115]
M. Stojnic. Regularly random duality. 2013. available online athttp://arxiv.org/abs/1303.7295
2013 arXiv
-
[116]
M. Stojnic. Fully bilinear generic and lifted random processes comparisons. 2016. available online at http://arxiv.org/abs/1612.08516
2016 arXiv
-
[117]
M. Stojnic. Generic and lifted probabilistic comparisons – max replaces minmax. 2016. available online athttp://arxiv.org/abs/1612.08506
2016 arXiv
-
[118]
M. Stojnic. A CLuP algorithm to practically achieve∼0.76SK–model ground state free energy. Journal of Statistical Mechanics: Theory and Experiment, (11):123302, 2025
2025
-
[119]
M. Stojnic. Binary perceptron computational gap – a parametric fl-RDT view.Journal of Statistical Mechanics: Theory and Experiment, (4):043301, 2026
2026
-
[120]
E. Subag. The complexity of spherical p-spin models - A second moment approach.Ann. Probab., 45:3385 – 3450, 2017
2017
-
[121]
E. Subag. Following the ground states of full-rsb spherical spin glasses.Comm. Pure Appl. Math., 74:1021–1044, 2021
2021
-
[122]
Tao and V
T. Tao and V. Vu. Random matrices: Universality of local eigenvalue statistics.Acta Mathematica, 206(1):127–204, 2011
2011
-
[123]
C. A. Tracy and H. Widom. On orthogonal and symplectic matrix ensembles.Communications in Mathematical Physics, 177(3):727–754, 1996
1996
-
[124]
Verbaarschot
J. Verbaarschot. Spectrum of the qcd dirac operator and chiral random matrix theory.Phys. Rev. Lett., 72:2531–2533, Apr 1994
1994
-
[125]
P. Vivo, S. N. Majumdar, and O. Bohigas. Large deviations of the maximum eigenvalue in Wishart random matrices.Journal of Physics A: Mathematical and Theoretical, 40(16):4317–4337, apr 2007
2007
-
[126]
Voiculescu
D. Voiculescu. Limit laws for random matrices and free products.Invent. Math., 104(1):201–220, 1991
1991
-
[127]
E. P. Wigner. On the statistical distribution of the widths and spacings of nuclear resonance levels. Mathematical Proceedings of the Cambridge Philosophical Society, 47(4):790–798, 1951
1951
-
[128]
E. P. Wigner. On the distribution of the roots of certain symmetric matrices.Annals of Mathematics, 67(2):325–327, 1958
1958
-
[129]
J. Wishart. The generalised product moment distribution in samples from a normal multivariate population.Biometrika, 20A(1-2):32–52, 1928
1928
-
[130]
Xu and Q
Y. Xu and Q. Zeng. Large deviations for the extremal eigenvalues of Ginibre ensembles, 2025. available online athttp://arxiv.org/abs/2512.12711. 23
2025
Reviewed June 25, 2026 · model on record in the stance chip above.
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