Preface |
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ix | |
Acknowledgements |
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xii | |
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1 | (36) |
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1 Correlated Models and Wave Functions |
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3 | (34) |
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3 | (3) |
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1.2 The Matrix Formulation |
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6 | (1) |
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1.3 Effective Lattice Models |
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7 | (6) |
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1.4 The Variational Principle |
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13 | (1) |
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1.5 Variational Wave Functions |
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14 | (17) |
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31 | (3) |
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1.7 Projection Techniques |
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34 | (3) |
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Part II Probability and Sampling |
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37 | (64) |
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39 | (17) |
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39 | (2) |
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2.2 Events and Probability |
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41 | (3) |
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2.3 Moments of the Distribution: Mean Value and Variance |
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44 | (3) |
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2.4 Changing Random Variables |
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47 | (1) |
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2.5 The Chebyshev's Inequality |
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48 | (1) |
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2.6 Summing Independent Random Variables |
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48 | (5) |
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2.7 The Central Limit Theorem |
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53 | (3) |
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3 Monte Carlo Sampling and Markov Chains |
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56 | (29) |
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56 | (3) |
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3.2 Reweighting Technique and Correlated Sampling |
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59 | (1) |
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60 | (1) |
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61 | (1) |
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3.5 Sampling a Discrete Distribution Probability |
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62 | (2) |
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3.6 Sampling a Continuous Density Probability |
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64 | (2) |
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66 | (3) |
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3.8 Detailed Balance and Approach to Equilibrium |
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69 | (5) |
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74 | (2) |
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3.10 How to Estimate Errorbars |
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76 | (6) |
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3.11 Errorbars in Correlated Samplings |
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82 | (3) |
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4 Langevin Molecular Dynamics |
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85 | (16) |
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85 | (2) |
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4.2 Discrete-Time Langevin Dynamics |
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87 | (2) |
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4.3 From the Langevin to the Fokker-Planck Equation |
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89 | (2) |
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4.4 Fokker-Planck Equation and Quantum Mechanics |
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91 | (5) |
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4.5 Accelerated Langevin Dynamics |
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96 | (5) |
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Part III Variational Monte Carlo |
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101 | (64) |
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5 Variational Monte Carlo |
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103 | (28) |
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5.1 Quantum Averages and Statistical Samplings |
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103 | (2) |
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5.2 The Zero-Variance Property |
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105 | (1) |
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5.3 Jastrow and Jastrow-Slater Wave Functions |
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106 | (2) |
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5.4 The Choice of the Basis Sets |
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108 | (1) |
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109 | (3) |
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5.6 Fermionic Systems with Determinants |
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112 | (11) |
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5.7 Fermionic Systems with Pfaffians |
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123 | (6) |
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5.8 Energy and Correlation Functions |
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129 | (1) |
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5.9 Practical Implementation |
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129 | (2) |
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6 Optimization of Variational Wave Functions |
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131 | (25) |
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131 | (1) |
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6.2 Reweighting Techniques for the Optimization of Wave Functions |
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132 | (2) |
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134 | (3) |
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6.4 The Stochastic Reconfiguration |
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137 | (9) |
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6.5 Stochastic Reconfiguration as a Projection Technique |
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146 | (1) |
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147 | (5) |
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6.7 Calculations of Derivatives in the Jastrow-Slater Case |
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152 | (4) |
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7 Time-Dependent Variational Monte Carlo |
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156 | (9) |
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156 | (1) |
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7.2 Real-Time Evolution of the Variational Parameters |
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157 | (4) |
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7.3 An Example for the Quantum Quench in One Dimension |
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161 | (4) |
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Part IV Projection Techniques |
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165 | (68) |
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8 Green's Function Monte Carlo |
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167 | (22) |
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8.1 Basic Notions and Formal Derivations |
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167 | (3) |
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8.2 Single Walker Technique |
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170 | (3) |
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173 | (5) |
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8.4 The Continuous-Time Limit |
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178 | (2) |
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8.5 Many Walkers Formulation |
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180 | (8) |
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8.6 Practical Implementation |
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188 | (1) |
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9 Reptation Quantum Monte Carlo |
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189 | (10) |
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9.1 A Simple Path Integral Technique |
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189 | (2) |
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9.2 A Simple Way to Sample Configurations |
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191 | (4) |
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195 | (1) |
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9.4 The Continuous-Time Limit |
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196 | (1) |
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9.5 Practical Implementation |
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197 | (2) |
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10 Fixed-Node Approximation |
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199 | (15) |
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199 | (5) |
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10.2 A Simple Example on the Continuum |
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204 | (4) |
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10.3 A Simple Example on the Lattice |
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208 | (1) |
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10.4 The Fixed-Node Approximation on the Lattice |
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209 | (4) |
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10.5 Practical Implementation |
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213 | (1) |
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11 Auxiliary Field Quantum Monte Carlo |
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214 | (19) |
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214 | (2) |
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11.2 Trotter Approximation |
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216 | (1) |
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11.3 Hubbard-Stratonovich Transformation |
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217 | (2) |
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11.4 The Path-Integral Representation |
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219 | (4) |
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223 | (5) |
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11.6 Ground-State Energy and Correlation Functions |
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228 | (1) |
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11.7 Simple Cases without Sign Problem |
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228 | (3) |
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11.8 Practical Implementation |
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231 | (2) |
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233 | (28) |
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12 Realistic Simulations on the Continuum |
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235 | (26) |
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12.1 Introduction and Motivations |
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235 | (2) |
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12.2 Variational Wave Function with Localized Orbitals |
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237 | (7) |
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12.3 Size Consistency of the Variational Wave Functions |
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244 | (1) |
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12.4 Optimization of the Variational Wave Functions |
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245 | (7) |
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12.5 Lattice-Regularized Diffusion Monte Carlo |
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252 | (4) |
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12.6 An Improved Scheme for the Lattice Regularization |
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256 | (5) |
Appendix Pseudo-Random Numbers Generated by Computers |
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261 | (3) |
References |
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264 | (8) |
Index |
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272 | |