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1 f d 2016 10 2017 2 2 2 High Performance Comp. 3 - PowerPoint PPT Presentation

1 f d 2016 10 2017 2 2 2 High Performance Comp. 3 JO, Ohzeki, Shinaoka, Yoshimi, arXiv:1702.03056 Shinaoka, JO, Ohzeki, Yoshimi, arXiv:1702.03054 4 INTRODUCTION: Two Problems in Quantum Many-body


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  2. • f d – – • – 2016 10  – 2017 2 2 2

  3. High Performance Comp. 3

  4. JO, Ohzeki, Shinaoka, Yoshimi, arXiv:1702.03056 Shinaoka, JO, Ohzeki, Yoshimi, arXiv:1702.03054 4

  5. INTRODUCTION: Two Problems in Quantum Many-body Computations 5

  6. c.f. 6

  7. フーリエ 解析接続 変換 扱いにくい 扱いやすい (ダイアグラム展開 量子モンテカルロ法) 7

  8. The standard method: Pade Vidberg, Serene, 1977 8-fold degenerate impurity Anderson model 8 lines should coincide CT-QMC data 8

  9. → Lehmann ρ G difficulty : K (ill-conditioned matrix) (NaN)  9

  10. Maximum entropy method M. Jarrell, J. E. Gubernatis, Phys. Rep. 269 , 133 (1996) m : “default model” = m Stochastic method A. W. Sandvik, PRB 57 , 10287 (1998) S. Fuchs, T. Pruschke, and M. Jarrell, PRE 81 , 056701 (2010) K. S. D. Beach, arXiv:cond-mat/0403055 A. W. Sandvik, PRE 94 , 063308 (2016) Growing attempts K. S. D. Beach, R. J. Gooding, and F. Marsiglio, PRB 61 , 5147 (2000) A. Dirks et al ., Phys. Rev. E 87, 023305 (2013). F. Bao et al. , PRB 94 , 125149 (2016) O. Goulko et al ., PRB 95 , 014102 (2017). G. Bertaina, D. Galli, and E. Vitali, arXiv:1611.08502. L.-F. Arsenault et al ., arXiv:1612.04895. 10

  11. PROBLEM I 11

  12. fermionic bosonic More complicated object 12

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  14. URu 2 Si 2 Wiebe et al. 2007 テンソル積 + + ... + + + ... 14

  15. PROBLEM II 15

  16. We will show… • Two problems are “ two sides of the same coin ” • Solution to – Problem I (analytical continuation) – Problem II (two-particle objects) 16

  17. SOLUTION to problem I Sparse-Modeling (SpM) Analytical Continuation JO, Ohzeki, Shinaoka, Yoshimi, arXiv:1702.03056 17

  18. (SpM) 劣決定系 スパース性 x N 18

  19. スパース性 = 19

  20. Q. ρ A. (ill-conditioned matrix) ∵ K ρ ’ G ’ 20

  21. L1 スパース性 L1 LASSO (Least Absolute Shrinkage of Selection Operators) R. Tibshirani, J. R. Stat. Soc. B 58, 267 (1996) ( ) F ADMM (alternating direction method of multipliers) Boyd et al., Foundations and Trends in Machine Learning 3 , 1 (2011) 21

  22. (1) (2) M=4000 QMC 22

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  26. λ For a given λ automatic! 26

  27. SpM ✓ 1. 2. L1 – • (ill-conditioned ) • – • Our code will be available soon on GitHub 27

  28. SOLUTION to problem II Intermediate Representation (IR) Shinaoka, JO, Ohzeki, Yoshimi, arXiv:1702.03054 28

  29. M=4000 わずか 7 成分! 29

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  31. SVD 31

  32. Λ→ 0 で ルジャンドル多項式 に一致! 32

  33. わずか 5, 6 要素で十分! c.f. Boehnke et al. 2011 33

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  35. SVD (Boehnke et al. 2011) (Legendre) Legendre 35

  36. The solution has been there Lehmann (basic equation) SVD c.f. SV truncation Creffield et al. 1995 Bryan method in MaxEnt Bryan 1990 36

  37. A “new” rep Everything in IR basis! - QMC measurement - Perturbative expansion - Bethe-Salpeter equation … 37

  38. Two problems in quantum many-body calculations • I. II. Our solution • SVD – – L1 スパースモデリングで本質を見抜く 38

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