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Efficient Algorithms for High-Dimensional Eigenvalue

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Efficient Algorithms for High-Dimensional Eigenvalue Efficient Algorithms for High-dimensional Eigenvalue Problems by Zhe Wang Department of Mathematics Duke University Date: Approved: Jianfeng Lu, Advisor Xiuyuan Cheng Jonathon Mattingly Weitao Yang Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics in the Graduate School of Duke University 2020 ABSTRACT Efficient Algorithms for High-dimensional Eigenvalue Problems by Zhe Wang Department of Mathematics Duke University Date: Approved: Jianfeng Lu, Advisor Xiuyuan Cheng Jonathon Mattingly Weitao Yang An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics in the Graduate School of Duke University 2020 Copyright © 2020 by Zhe Wang All rights reserved Abstract The eigenvalue problem is a traditional mathematical problem and has a wide appli- cations. Although there are many algorithms and theories, it is still challenging to solve the leading eigenvalue problem of extreme high dimension. Full configuration interaction (FCI) problem in quantum chemistry is such a problem. This thesis tries to understand some existing algorithms of FCI problem and propose new efficient algorithms for the high-dimensional eigenvalue problem. In more details, we first es- tablish a general framework of inexact power iteration and establish the convergence theorem of full configuration interaction quantum Monte Carlo (FCIQMC) and fast randomized iteration (FRI). Second, we reformulate the leading eigenvalue problem as an optimization problem, then compare the show the convergence of several coor- dinate descent methods (CDM) to solve the leading eigenvalue problem. Third, we propose a new efficient algorithm named Coordinate descent FCI (CDFCI) based on coordinate descent methods to solve the FCI problem, which produces some state-of- the-art results. Finally, we conduct various numerical experiments to fully test the algorithms. iv Contents Abstract iv List of Figures viii List of Tables ix Acknowledgements xi 1 Introduction 1 1.1 Leading eigenvalue problem . .1 1.2 Full Configuration Interaction . .2 1.3 Contributions . .6 1.4 Notations and Organization . .9 2 Inexact Power Iteration 12 2.1 Introduction . 12 2.2 General convergence analysis of inexact power iteration . 15 2.3 Algorithms . 23 2.3.1 Full configuration-interaction quantum Monte Carlo . 24 2.3.2 Fast Randomized Iteration . 36 3 Coordinate Descent Methods 45 3.1 Introduction . 45 3.2 Landscape Analysis . 51 3.3 Coordinate descent method with conservative stepsize . 54 3.3.1 CD-Cyc-Grad and SCD-Cyc-Grad . 54 3.3.2 Global convergence of gradient based coordinate-wise descent method . 55 v 3.4 Greedy coordinate descent method . 56 3.4.1 GCD-Grad-LS and GCD-LS-LS . 57 3.4.2 Escapable saddle points using exact line search . 59 3.5 Stochastic coordinate descent methods . 62 3.5.1 SCD-Grad-vecLS and SCD-Grad-LS . 63 3.5.2 Local convergence of stochastic coordinate descent method . 67 4 Coordinate Descent Full Configuration Interaction 76 4.1 Introduction . 76 4.2 Coordinate descent FCI . 77 4.2.1 Algorithm . 79 4.3 Implementation and complexity . 85 5 Numerical Results 89 5.1 Comparison of FCIQMC and FRI . 89 5.1.1 Hubbard Model . 90 5.1.2 Molecules . 96 5.2 Comparison of CDMs . 98 5.2.1 Dense random matrices . 101 5.2.2 Hubbard models . 105 5.3 Benchmark of CDFCI . 110 5.3.1 Numerical results of H2O, C2, and N2 .............. 110 5.3.2 Binding curve of N2 ........................ 120 5.3.3 All electron chromium dimer calculation . 121 5.3.4 N2 Binding Curve Data . 123 vi 6 Conclusion 125 A Proofs of Theorems 129 A.1 Proof of Theorem 3.3.1 . 129 A.2 Proof of local convergence of SCD methods . 134 Bibliography 137 vii List of Figures 1.1 Correlation between the updating frequencies and magnitudes of the ground state wavefunction by CDFCI. 11 3.1 Convergence behavior of greedy coordinate descent methods vs stochas- tic coordinate descent methods. 63 3.2 Convergence behavior of SCD-Grad-Grad method with different choice of t. The axis of iterations on (a) is twice as long as that in (b). 73 5.1 Convergence of the projected energy with respect to time for System 1, a 4 × 4 Hubbard model for comparison between FCIQMC and FRI. 92 5.2 Convergence of error and ground state of 4 × 4 Hubbard model for comparison between FCIQMC and FRI. 95 5.3 Convergence of the projected energy with respect to time for Ne in aug-cc-pVDZ basis for comparison between FCIQMC and FRI . 97 5.4 Convergence of the projected energy with respect to time for H2O in cc-pVDZ basis for comparison of FCIQMC and FRI . 98 5.5 Convergence behavior of CDMs for the 4 × 4 Hubbard model with 10 electrons. k = 1 for all methods except PM. 108 5.6 Convergence of ground state energy of H2O against time to benchmark CDFCI. Each point or curve represents one test as in Table 5.11. 114 5.7 Convergence of ground state energy of C2 against time to benchmark CDFCI. Each point or curve represents one test as in Table 5.12. 115 5.8 Convergence of ground state energy of N2 against time to benchmark CDFCI. Each point or curve represents one test as in Table 5.13. 118 5.9 Error of energy and the number of nonzeros entries in the vector z after convergence for different compression tolerance " in CDFCI. 119 5.10 Binding curve and the corresponding Hartree-Fock values and number of nonzeros in x of N2 under cc-pVDZ basis computed by CDFCI. 121 viii List of Tables 1.1 Common notations. 10 3.1 Short names in name convention of CDMs. 48 5.1 Test systems for FCIQMC and FRI . 89 5.2 Parameters and numerical results for System 1, a 4×4 Hubbard Model for comparison between FCIQMC and FRI. 92 5.3 Comparison of FCIQMC and FRI for Ne in aug-cc-pVDZ basis . 97 5.4 Comparison of FCIQMC and FRI for H2O in cc-pVDZ basis . 99 5.5 Performance of various CDMs for A108 ................. 101 5.6 Performance of various CDMs for A101 ................. 103 5.7 Performance of various CDMs for A108 + 1000I ............. 104 5.8 Properties of the 2D Hubbard Hamiltonian for numerical comparison of CDMs . 106 5.9 Performance of various CDMs for 4 × 4 Hubbard model with 6 electrons106 5.10 Properties of test molecule systems for CDFCI. HF energy and GS energy denote Hartree-Fock energy and ground state energy respectively.112 5.11 Convergence of ground state energy of H2O to benchmark CDFCI. 113 5.12 Convergence of ground state energy of C2 to benchmark CDFCI. 116 5.13 Convergence of ground state energy of N2 to benchmark CDFCI. 117 5.14 Nitrogen molecule ground state energy using CDFCI, DMRG (max M = 4000) and couple cluster theories. Slant digits indicate inaccurate digits.122 5.15 Energy of Cr2 by CDFCI, DMRG, HCI and coupled cluster theories. CDFCI produces the state-of-the-art variational energy. 122 ix 5.16 Energy of nitrogen dimer with different bond lengthsThe energy refers to variational ground state energy calculated by CDFCI with " = 10−6. 124 x Acknowledgements First, I would like to express my deep and sincere gratitude to my advisor Professor Jianfeng Lu. As my mentor and advisor, Professor Lu has been guiding me in both research and life since I first stepped on the campus of Duke University in 2015 fall. Professor Lu is an extraordinary and profound researcher. He points the direction of my research and his guidance keeps motivating and inspiring me. Discussion with Professor Lu is always enjoyable and fruitful. Professor Lu is also a considerate and reliable friend. Besides academic support, he supports and understands me in many other aspects as well, for example, job searching. I receive full respect and freedom during my PhD. Without Professor Lu, my PhD time would never be so meaningful and colorful. Second, I would thank my committee members, Professor Jonathan Mattingly, Professor Weitao Yang and Professor Xiuyuan Cheng. The discussions with them help me overcome many difficulties and give me new research directions. Third, I want to say thank you to Dr. Yingzhou Li. He can not help me more and he is one of my best friends. I am also grateful to Prof. George Booth, Prof. Jonathan Weare, Prof. Stephen Wright, Prof. Lexing Ying, Prof. Wotao Yin, Prof. Ali Alavi and Dr. Qiming Sun for useful discussion of research with me and my advisor. I would also like to thank my friends Honglue Shi, Zehua Chen, Hanqing Liu and Xin Zhang for helping me in quantum physics and quamtum chemistry. Finally, I would like to thankthe U.S. National Science Foundation and the U.S. Department of Energy. My PhD researches are supported by part by National Science Foundation under award OAC-1450280 and DMS-1454939 and by the US Department of Energy via grant DE-SC0019449. xi Chapter 1 Introduction 1.1 Leading eigenvalue problem This thesis focuses on solving the leading eigenvalue problem (LEVP) of extreme high dimensions. Given a symmetric matrix A, the LEVP is defined as Av1 = λ1v1; (1.1) n×n > n for A 2 R , A = A, λ1 is the largest eigenvalue of A, and v1 2 R is the corre- sponding eigenvector (we assume here and in the sequel that the leading eigenvalue is positive and non-degenerate). In general, assume λ1 > λ2 ≥ λ3 ≥ · · · ≥ λn are the eigenvalues of A, λ1 > 0, and the corresponding orthonormal eigenvectors are v1; v2; v3; ··· ; vn.
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