Improvement of Monte Carlo estimates with covariance-optimized finite-size scaling at fixed phenomenological coupling

Transcription

1 Improvement of Monte Carlo estimates with covariance-optimized finite-size scaling at fixed phenomenological coupling Francesco Parisen Toldin Max Planck Institute for Physics of Complex Systems Dresden 25 November 2011 F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

2 Monte Carlo methods Powerful and flexible method to study critical phenomena General feature: accuracy 1 computational time In order to improve the accuracy: improved estimators Use of covariance analysis: - add control variates, whose expectation value vanish 1 - compute the optimal weighted average of different estimates of a critical exponent 2 Our method: - Optimization of a Finite-Size Scaling method - It allows for a significant reduction of the error bars - It does not require additional computational time 1 L. A. Fernández, V. Martín-Mayor, Phys. Rev. E 79, (2009) 2 M. Weigel, W. Janke, Phys. Rev. Lett. 102, (2009); Phys. Rev. E 81, (2010) F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

3 Finite-Size Scaling A system in a finite volume of linear size L Finite-Size Scaling (FSS) works in region of parameters where ξ L FSS behavior of long-ranged quantities O = L x f (tl 1/ν ), t T T c T c E.g. Susceptibility χ L 2 η f (tl 1/ν ) Renormalization-Group invariant quantities R R = F(tL 1/ν ) E.g. R = ξ/l, Binder ratios, etc... Also called phenomenological couplings F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

4 Finite-Size Scaling at fixed phenomenological coupling We fix a phenomenological coupling R to a constant R f R(β,L) = R f β f (L) such that R(β f (L), L) = R f From FSS relation R = F(tL 1/ν ), t = (T T c )/T c = β c /β 1: β f (L) β c L 1/ν, β f (L) β c L 1/ν ω, generic R f R f = R = F(0) All the other observables O(β,L) are calculated at β = β f (L) χ(β f (L), L) L 2 η, R β (β f (L), L) L 1/ν susceptibility M. Hasenbusch, J. Phys. A 32, 4851 (1999) F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

5 Finite-Size Scaling at fixed phenomenological coupling It does not require a precise knowledge of β c Reduced error bars by fixing ξ/l: - Fully frustrated XY model 1 - Randomly Dilute Ising Universality class 2 - Ising model in d = 3, 4, 5 3 Similarities with the Phenomenological Renormalization method: - Two system sizes are enforced to share a common value of ξ/l - Reported reduction of error bars 4 1 M. Hasenbusch, A. Pelissetto, E. Vicari, JSTAT P12002 (2005) 2 M. Hasenbusch, F. Parisen Toldin, A. Pelissetto, E. Vicari, JSTAT P02016 (2007) 3 U. Wolff, Phys. Rev. D 79, (2009) 4 H. G. Ballesteros, L. A. Fernández, V. Martín-Mayor, A. Muñoz-Sudupe, Nucl. Phys. B 483, 707 (1997) F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

6 Finite-Size Scaling at fixed phenomenological coupling Why there is a reduction of the error bars? Notation: Simulations at β = β run An observable O is calculated from a statistical estimator Ô Ô = O + δo, O = E[Ô] We choose to fix a phenomenological coupling R sampled using the estimator R We fix R(β = β f (L), L) = R f and calculate O f (L) O(β = β f, L) F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

7 Finite-Size Scaling at fixed phenomenological coupling For β close to β run R(β) R + R (β β run ), R = R(βrun ), R = R/ β. Solving R = R f βf = βrun R R f R we trade the fluctuations of R for the fluctuations of β f For a generic observable O calculated at β f Ô f Ô + Ô (β β run ) = Ô Ô R R f R. The variance of Ôf is, to the lowest order in the fluctuations ( ) O 2 VAR[Ôf ] = VAR[Ô] + R VAR[ R] 2 O COV[Ô, R] R F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

8 Optimization of Finite-Size Scaling Can we optimize the Finite-Size Scaling? Consider N phenomenological couplings R 1,...,R N We define the RG-invariant quantity R({λ i }) i λ ir i We consider FSS at fixed phenomenological coupling R({λ i }) Problem: given an observable O, what are the coefficients {λ i } that minimize the value of O f? Minimize: VAR[Ôf ] = VAR[Ô] + ( O R ) 2 VAR[ R] 2 O COV[Ô, R] R Solution: λ i = R T M 1 N O ( R T M 1 R M 1 R ) ) (M + 1 N, i i with M ij COV[ R i, R j ], N i COV[Ô, R i ], R i R i. F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

9 Optimization of Finite-Size Scaling Can we optimize the Finite-Size Scaling? Consider N phenomenological couplings R 1,...,R N We define the RG-invariant quantity R({λ i }) i λ ir i We consider FSS at fixed phenomenological coupling R({λ i }) Problem: given an observable O, what are the coefficients {λ i } that minimize the value of O f? Minimize: VAR[Ôf ] = VAR[Ô] + ( O R ) 2 VAR[ R] 2 O COV[Ô, R] R Solution: λ i = R T M 1 N O ( R T M 1 R M 1 R ) ) (M + 1 N, i i with M ij COV[ R i, R j ], N i COV[Ô, R i ], R i R i. F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

10 Optimization of Finite-Size Scaling Can we optimize the Finite-Size Scaling? Consider N phenomenological couplings R 1,...,R N We define the RG-invariant quantity R({λ i }) i λ ir i We consider FSS at fixed phenomenological coupling R({λ i }) Problem: given an observable O, what are the coefficients {λ i } that minimize the value of O f? Minimize: VAR[Ôf ] = VAR[Ô] + ( O R ) 2 VAR[ R] 2 O COV[Ô, R] R Solution: λ i = R T M 1 N O ( R T M 1 R M 1 R ) ) (M + 1 N, i i with M ij COV[ R i, R j ], N i COV[Ô, R i ], R i R i. F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

11 Some observations Finite-Size Scaling at fixed R({λ i }) i λ ir i, with λ i = R T M 1 N O ( R T M 1 R M 1 R ) ) (M + 1 N, i i M ij COV[ R i, R j ], N i COV[Ô, R i ]. M and N are related to the transition matrix of the Markov chain they depend on the model and on the dynamics {λ i } can be optimized separately for every observable O FSS limit is correctly defined when {λ i } are the same for all sizes. The optimal {λ i } depend on the lattice size. Possible strategy: choose the optimal {λ i } obtained from the largest available lattice. F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

12 Test: Ising model in d = 2, 3 H = J ij Four RG-invariant quantities: σ i σ j, σ i = ±1. U 4 M4 M 2 2, U 6 M6 M 2 3 R ξ ξ/l, R Z Z a /Z 1 p, with M i σ i magnetization, ξ second-moment correlation length Z a partition function with antiperiodic b.c. on one direction, Z p partition function with fully periodic b.c. Observables χ i σ i σ j /V L 2 η 1 M.Hasenbusch, Physica A 197, 423 (1993) U 4 β, U 6 β, R ξ β L1/ν F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

13 Results d = 2 Gain in CPU time is given by ( ) standard error bar 2 error bar at fixed R CPU gain for Metropolis dynamics χ: gain 20; gain at fixed R ξ 4 U 4 / β, U 6 / β: gain 30 50; gain at fixed U 4, U R ξ / β: gain 2 3; gain at fixed U 4, U 6, R ξ, R Z 1 CPU gain for Wolff single-cluster dynamics χ: gain 6; gain at fixed R ξ 3 U 4 / β, U 6 / β: gain 6 9; gain at fixed U 4, U R ξ / β: gain 2 3; gain at fixed U 4, U 6, R ξ, R Z 1 Gain in computational time roughly independent on L = F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

14 Results d = 3 Gain in CPU time is given by ( ) standard error bar 2 error bar at fixed R CPU gain for Metropolis dynamics χ: gain 20 30; gain at fixed R ξ 9 U 4 / β, U 6 / β: gain 20 30; gain at fixed U 4, U 6 3 R ξ / β: gain 4 6; gain at fixed U 4, U 6, R ξ, R Z 1 CPU gain for Wolff single-cluster dynamics χ: gain 6 10; gain at fixed R ξ 5 U 4 / β, U 6 / β: gain 5 8; gain at fixed U 4, U R ξ / β: gain 3; gain at fixed U 4, U 6, R ξ, R Z 1 Gain in computational time roughly independent on L = F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13

15 Outlook Substantial reduction of the error bars It does not require additional computational time Test on the Ising model - CPU gains are roughly independent on the lattice L - CPU gains are more pronounced for Metropolis update Other possible applications: - Improved models - Models with quenched disorder -... Ref: F. Parisen Toldin, Phys. Rev. E 84, (R) F. Parisen Toldin (MPI PKS) FSS at fixed phenomenological coupling 25 November / 13