SlideShare ist ein Scribd-Unternehmen logo
1 von 1
Downloaden Sie, um offline zu lesen
Eigenvalues of sums from sums of eigenvalues: how accurate is free probability in calculating the density of states in disordered systems?
 Jiahao Chen, Eric Hontz, Jeremy Moix, Matthew Welborn, and Troy Van Voorhis                                                                                                                    Alberto Suárez                                                                                    Ramis Movassagh and Alan Edelman
                            Department of Chemistry                                                                                                                                  Departamento de Ingeniería Informática                                                                           Department of Mathematics
                     Massachusetts Institute of Technology                                                                                                                             Universidad Autónoma de Madrid                                                                             Massachusetts Institute of Technology

Why are disordered systems interesting?                                                                                          Application to disordered one-dimensional tight binding systems                                                     Explaining the different behaviors of different partitionings
 1. Unique physics, e.g.        2. Many applications                    3. A challenge to model!                                     How well can we approximate the density of states in one-dimensional electronic systems? [4]                           Our new result is to provide a quantative error analysis of the approximations from free probability.
                                                                                                                                     Consider two possible partitionings of the Hamiltonian:                                                                This involves combining two known facts:
    state localization             bulk heterojunction materials          sampling in configuration space
    anomalous diffusion            disordered metals                      diagonalize lots of Hamiltonians                                                                                                                                                  1. The difference between two probability distributions can be quantified by asymptotic moment expansions
    ergodicity breaking            defects in nanostructures                                                                      random                                                                                                                       which generalize Edgeworth or Gram-Charlier series. [5, 6]

                                                                                                                                  constant

                                                                                                                                                                                                                                                                The moment expansion is completely parameterized by the cumulants of the two distributions.
                                                                                                                                                                                                                                                            Our new result is to provide a quantative error analysis of the approximations from free probability.
                                                                                                                                                                                                                                                            This involves combining two results:
                                             electronic structure
 crystal         atomic coordinates                                         observable
                                                  dynamics                                                                                                                                                                                                  2. Free probability implies a particular rule for calculating joint moments of the probability distribution:


disordered system                                                                                                                                                                                                                                           This gives us a way to calculate moments of the distribution produced from the free convolution by
                                                                                                                                                                                                                                                            calculating all the joint moments arising from the expansion of the moments of the sum:



                                                                                                                                                                                                                                                            The noncommutative expansion of the trace is equivalent to the combinatorics of necklaces. [7]
                                                                                                                                     For each piece, the eigenvalues can be calculated easily.                                                              We can then find an n such that the leading order discrepancy between the exact and free distributions is
                                                                                           ensemble-averaged
                                                                                           observable
                                                                                                                                     How well does the free convolution approximate the density of states?
                  ...




                                                                                                                                     Numerical convolution, Gaussian noise
sampling in configuration space
                                                                                                                                   Scheme I             low noise                                             high noise
   Random matrix theory can help us characterize the ensemble of random Hamiltonians and develop accurate                          Scheme II
   approximations to their eigenvalue spectra.                                                                                     exact                                                                                                                                                            Scheme 1                    Scheme II
   The basic idea: take a Hamiltonian matrix with some (or all) random entries, break it up into pieces whose
   eigenvalues can be easily calculated, then “add” then back together again.
                                                                                                                                                                                                                                                     It turns out that Scheme I in the infinite limit reduces to the coherent potential approximation, a self-consistent
 Eigenvalues of sums of matrices                                                                                                                                                   moderate noise                                                    mean-field theory. [8] Our result provides an explanation for why the CPA works so well.
   In general, eigenvalues of matrix sums are not sums of eigenvalues!
                                                                                                                                                                                                                                                  References
                                                                                                                                                                                                                                                     [1] D. Voiculescu, Invent. Math. 104, 201 (1991).
                                                                                                                                     Scheme I shows universally good agreement with the exact density of states, whereas Scheme II worsens           [2] A. Nica and R. Speicher, Lectures on the Combinatorics of Free Probability, London Math. Soc. Lecture Note Ser. (2006).
                                                                                                                                     in the high noise regime.                                                                                       [3] D. Voiculescu, in Operator algebras and their connections with topology and ergodic theory, Lecture Notes in Mathematics, Vol. 1132,
   However, we can neglect precise information about the bases of the matrices by approximating them with random                                                                                                                                         (Springer, 1985) pp. 556–588.
   permutations or random rotations. This seems very drastic, but it is sometimes exact!                                                                                                                                                             [4] D. J. Thouless, Phys. Rep. 13, 93 (1974).
                                                   random permutation                              random rotation                   How does Scheme I compare to perturbation theory?                                                               [5] A. Stuart and J. K. Ord, Kendall’s advanced theory of statistics. (Edward Arnold, London, 1994).
                                                                                                                                                                                                                                                     [6] D. Wallace, Ann. Math. Stat. 29, 635 (1958).
                                                                                                                                     Analytic convolution, semicircular noise                                                                        [7] J. Sawada, SIAM J. Comput. 31, 259 (2001).
                                                                                                                                                                                                                                                     [9] P. Neu and R. Speicher, Z. Phys. B 95, 101 (1994); J. Phys. A 79, L79 (1995); J. Stat. Phys. 80, 1279 (1995).
   Exact if A and B commute, i.e. if relative orientations       Exact if A and B are free, i.e .their eigenvectors are in
                                                                                                                                 Scheme I
   of the eigenvectors are perfectly parallel.                   generic position, i.e. relative orientations are so random
                                                                 that they are effectively uniformly distributed over all
                                                                                                                                 A perturbs B                                                                                                     With thanks to
                                                                                                                                 B perturbs A
                                                                 possible rotations (Q is uniform with Haar measure) [1,2].
                                                                                                                                 exact                                                                                                                                         E.H.        J.C.                T.V.
   In the limit of infinitely large matrices, the density of states of A + B can be found by:
                                                                                                                                                                                                                                                                                                                      M.W.    A.E.     R.M.     A.S.      J.M.
   Convolution of the eigenvalue densities of A and B            Free convolution of the eigenvalue densities of A and B [2,3]

                                                                                                                                                                                                                                                                                                                               useful discussions with

                                                                                                                                                                                                                                                                                                                                   Sebastiaan Vlaming (MIT, Chemistry)
                                                                                                                                                                                                                                                                                                                                   Jonathan Novak (MIT, Mathematics)
                                                                                                                                                                                                                                                                                                                                   N Raj Rao (Michigan, Mathematics)

                                                                                                                                     Unlike perturbation theory, where there is asymmetric treatment of A and B, Scheme I provides an excellent
                                                                                                                                     approximation universally regardless of the strength of noise. But why?


                                                                                                                                                                                                                                                  Funding
                                                                                                                                                                                                                                                     NSF SOLAR 1035400 ( J.C., E.H., M.W., T.V., R.M., A.E.), CHE1112825 ( J.M.), DMS 1016125 (A.E.)
   Gives us ways to calculate eigenvalue spectra without ever diagonalizing a matrix!                                                                                                                                                                DARPA Grant No. N99001-10-1-4063 ( J.M.)
                                                                                                                                                                                                                                                     Dirección General de Investigación, Project TIN2010-21575-C02-02 (A.S.)

Weitere ähnliche Inhalte

Andere mochten auch

Genomics data analysis in Julia
Genomics data analysis in JuliaGenomics data analysis in Julia
Genomics data analysis in JuliaJiahao Chen
 
Understanding ECG signals in the MIMIC II database
Understanding ECG signals in the MIMIC II databaseUnderstanding ECG signals in the MIMIC II database
Understanding ECG signals in the MIMIC II databaseJiahao Chen
 
Resolving the dissociation catastrophe in fluctuating-charge models
Resolving the dissociation catastrophe in fluctuating-charge modelsResolving the dissociation catastrophe in fluctuating-charge models
Resolving the dissociation catastrophe in fluctuating-charge modelsJiahao Chen
 
Excitation Energy Transfer In Photosynthetic Membranes
Excitation Energy Transfer In Photosynthetic MembranesExcitation Energy Transfer In Photosynthetic Membranes
Excitation Energy Transfer In Photosynthetic MembranesJiahao Chen
 
A Julia package for iterative SVDs with applications to genomics data analysis
A Julia package for iterative SVDs with applications to genomics data analysisA Julia package for iterative SVDs with applications to genomics data analysis
A Julia package for iterative SVDs with applications to genomics data analysisJiahao Chen
 
An introduction to Julia
An introduction to JuliaAn introduction to Julia
An introduction to JuliaJiahao Chen
 
What's next in Julia
What's next in JuliaWhat's next in Julia
What's next in JuliaJiahao Chen
 
Theory and application of fluctuating-charge models
Theory and application of fluctuating-charge modelsTheory and application of fluctuating-charge models
Theory and application of fluctuating-charge modelsJiahao Chen
 
Python as number crunching code glue
Python as number crunching code gluePython as number crunching code glue
Python as number crunching code glueJiahao Chen
 

Andere mochten auch (9)

Genomics data analysis in Julia
Genomics data analysis in JuliaGenomics data analysis in Julia
Genomics data analysis in Julia
 
Understanding ECG signals in the MIMIC II database
Understanding ECG signals in the MIMIC II databaseUnderstanding ECG signals in the MIMIC II database
Understanding ECG signals in the MIMIC II database
 
Resolving the dissociation catastrophe in fluctuating-charge models
Resolving the dissociation catastrophe in fluctuating-charge modelsResolving the dissociation catastrophe in fluctuating-charge models
Resolving the dissociation catastrophe in fluctuating-charge models
 
Excitation Energy Transfer In Photosynthetic Membranes
Excitation Energy Transfer In Photosynthetic MembranesExcitation Energy Transfer In Photosynthetic Membranes
Excitation Energy Transfer In Photosynthetic Membranes
 
A Julia package for iterative SVDs with applications to genomics data analysis
A Julia package for iterative SVDs with applications to genomics data analysisA Julia package for iterative SVDs with applications to genomics data analysis
A Julia package for iterative SVDs with applications to genomics data analysis
 
An introduction to Julia
An introduction to JuliaAn introduction to Julia
An introduction to Julia
 
What's next in Julia
What's next in JuliaWhat's next in Julia
What's next in Julia
 
Theory and application of fluctuating-charge models
Theory and application of fluctuating-charge modelsTheory and application of fluctuating-charge models
Theory and application of fluctuating-charge models
 
Python as number crunching code glue
Python as number crunching code gluePython as number crunching code glue
Python as number crunching code glue
 

Kürzlich hochgeladen

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MIND CTI
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Victor Rentea
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxRustici Software
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...apidays
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdfSandro Moreira
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businesspanagenda
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Bhuvaneswari Subramani
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAndrey Devyatkin
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...apidays
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusZilliz
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native ApplicationsWSO2
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingEdi Saputra
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistandanishmna97
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobeapidays
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Victor Rentea
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Angeliki Cooney
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 

Kürzlich hochgeladen (20)

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​Elevate Developer Efficiency & build GenAI Application with Amazon Q​
Elevate Developer Efficiency & build GenAI Application with Amazon Q​
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Architecting Cloud Native Applications
Architecting Cloud Native ApplicationsArchitecting Cloud Native Applications
Architecting Cloud Native Applications
 
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost SavingRepurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
 
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In PakistanCNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 

Eigenvalues of sums from sums of eigenvalues: how accurate is free probability in calculating the density of states in disordered systems?

  • 1. Eigenvalues of sums from sums of eigenvalues: how accurate is free probability in calculating the density of states in disordered systems? Jiahao Chen, Eric Hontz, Jeremy Moix, Matthew Welborn, and Troy Van Voorhis Alberto Suárez Ramis Movassagh and Alan Edelman Department of Chemistry Departamento de Ingeniería Informática Department of Mathematics Massachusetts Institute of Technology Universidad Autónoma de Madrid Massachusetts Institute of Technology Why are disordered systems interesting? Application to disordered one-dimensional tight binding systems Explaining the different behaviors of different partitionings 1. Unique physics, e.g. 2. Many applications 3. A challenge to model! How well can we approximate the density of states in one-dimensional electronic systems? [4] Our new result is to provide a quantative error analysis of the approximations from free probability. Consider two possible partitionings of the Hamiltonian: This involves combining two known facts: state localization bulk heterojunction materials sampling in configuration space anomalous diffusion disordered metals diagonalize lots of Hamiltonians 1. The difference between two probability distributions can be quantified by asymptotic moment expansions ergodicity breaking defects in nanostructures random which generalize Edgeworth or Gram-Charlier series. [5, 6] constant The moment expansion is completely parameterized by the cumulants of the two distributions. Our new result is to provide a quantative error analysis of the approximations from free probability. This involves combining two results: electronic structure crystal atomic coordinates observable dynamics 2. Free probability implies a particular rule for calculating joint moments of the probability distribution: disordered system This gives us a way to calculate moments of the distribution produced from the free convolution by calculating all the joint moments arising from the expansion of the moments of the sum: The noncommutative expansion of the trace is equivalent to the combinatorics of necklaces. [7] For each piece, the eigenvalues can be calculated easily. We can then find an n such that the leading order discrepancy between the exact and free distributions is ensemble-averaged observable How well does the free convolution approximate the density of states? ... Numerical convolution, Gaussian noise sampling in configuration space Scheme I low noise high noise Random matrix theory can help us characterize the ensemble of random Hamiltonians and develop accurate Scheme II approximations to their eigenvalue spectra. exact Scheme 1 Scheme II The basic idea: take a Hamiltonian matrix with some (or all) random entries, break it up into pieces whose eigenvalues can be easily calculated, then “add” then back together again. It turns out that Scheme I in the infinite limit reduces to the coherent potential approximation, a self-consistent Eigenvalues of sums of matrices moderate noise mean-field theory. [8] Our result provides an explanation for why the CPA works so well. In general, eigenvalues of matrix sums are not sums of eigenvalues! References [1] D. Voiculescu, Invent. Math. 104, 201 (1991). Scheme I shows universally good agreement with the exact density of states, whereas Scheme II worsens [2] A. Nica and R. Speicher, Lectures on the Combinatorics of Free Probability, London Math. Soc. Lecture Note Ser. (2006). in the high noise regime. [3] D. Voiculescu, in Operator algebras and their connections with topology and ergodic theory, Lecture Notes in Mathematics, Vol. 1132, However, we can neglect precise information about the bases of the matrices by approximating them with random (Springer, 1985) pp. 556–588. permutations or random rotations. This seems very drastic, but it is sometimes exact! [4] D. J. Thouless, Phys. Rep. 13, 93 (1974). random permutation random rotation How does Scheme I compare to perturbation theory? [5] A. Stuart and J. K. Ord, Kendall’s advanced theory of statistics. (Edward Arnold, London, 1994). [6] D. Wallace, Ann. Math. Stat. 29, 635 (1958). Analytic convolution, semicircular noise [7] J. Sawada, SIAM J. Comput. 31, 259 (2001). [9] P. Neu and R. Speicher, Z. Phys. B 95, 101 (1994); J. Phys. A 79, L79 (1995); J. Stat. Phys. 80, 1279 (1995). Exact if A and B commute, i.e. if relative orientations Exact if A and B are free, i.e .their eigenvectors are in Scheme I of the eigenvectors are perfectly parallel. generic position, i.e. relative orientations are so random that they are effectively uniformly distributed over all A perturbs B With thanks to B perturbs A possible rotations (Q is uniform with Haar measure) [1,2]. exact E.H. J.C. T.V. In the limit of infinitely large matrices, the density of states of A + B can be found by: M.W. A.E. R.M. A.S. J.M. Convolution of the eigenvalue densities of A and B Free convolution of the eigenvalue densities of A and B [2,3] useful discussions with Sebastiaan Vlaming (MIT, Chemistry) Jonathan Novak (MIT, Mathematics) N Raj Rao (Michigan, Mathematics) Unlike perturbation theory, where there is asymmetric treatment of A and B, Scheme I provides an excellent approximation universally regardless of the strength of noise. But why? Funding NSF SOLAR 1035400 ( J.C., E.H., M.W., T.V., R.M., A.E.), CHE1112825 ( J.M.), DMS 1016125 (A.E.) Gives us ways to calculate eigenvalue spectra without ever diagonalizing a matrix! DARPA Grant No. N99001-10-1-4063 ( J.M.) Dirección General de Investigación, Project TIN2010-21575-C02-02 (A.S.)