Kapfer Krauth 2016

From Werner KRAUTH

(Difference between revisions)

Revision as of 09:24, 28 June 2016

S. C. Kapfer, W. Krauth Cell-veto Monte Carlo algorithm for long-range systems arXiv:1606.06780 (2016)

Paper

Abstract We present a rigorous efficient event-chain Monte Carlo algorithm for long-range interacting particle systems. Using a cell-veto scheme within the factorized Metropolis algorithm, we compute each single-particle move with a fixed number of operations. For slowly decaying potentials such as Coulomb interactions, screening line charges allow us to take into account periodic boundary conditions. We discuss the performance of the cell-veto Monte Carlo algorithm for general inverse-power-law potentials, and illustrate how it provides a new outlook on one of the prominent bottlenecks in large-scale atomistic Monte Carlo simulations.

Electronic version (from arXiv)

Illustration

Here are pictures of three bosons on an permutation cycle.

The cell-veto algorithm is an application of the event-chain paradigm that was developed in 2009, with E. P. Bernard and D. B. Wilson, and much extended together with M. Michel and S. C. Kapfer, where we introduced the factorized Metropolis algorithm. In this algorithm, unlike in 99.9% of all simulation algorithms (Monte Carlo or Molecular dynamics), one does not compute the system energy in order to decide on a change of the physical system, but rather looks at all the interactions separately. So, if a particle a (the active particle) wants to move, it has to ask all its partners t_1, t_2, .... (the target particles). If there is only a single veto, the move is rejected. In the cell-veto algorithm (see the right side of the figure), the identification of the rejecting particle is preceeded by that of a veto cell. The advantage of this is that cell vetos can be identified immediately (in a constant number of operations, that is, in O(1)), and then instantly confirmed or infirmed on the particle level.

Python implementation

The paper contains a Python demo implementation of the cell-veto algorithm

import math, random, sys, pylab
from copy import deepcopy

def CellRate(TargetCellLL, ActiveCellLL, L, CellBoundary):  # always >= 0
    Rate = 0.0
    for a in CellBoundary:
        for b in CellBoundary:
            del_x =  TargetCellLL[0] - ActiveCellLL[0] + (a[0] - b[0]) / L
            del_y =  TargetCellLL[1] - ActiveCellLL[1] + (a[1] - b[1]) / L
            Rate = max(Rate, PairRate(del_x, del_y, k_max))
    return Rate

def PairRate(del_x, del_y, k_max): # Here for 1/r potential in 2d
    q = 0.0
    for ky in range(-k_max, k_max + 1):
        for kx in range(-k_max, k_max + 1):
            q += (del_x + kx ) / (
                 (del_x + kx) ** 2 + (del_y + ky) ** 2) ** (3.0 / 2.0)
        q += 1.0 / ((del_x + kx + 0.5) ** 2 + (del_y + ky) ** 2) ** (1.0 / 2.0)
        q -= 1.0 / ((del_x - kx - 0.5) ** 2 + (del_y + ky) ** 2) ** (1.0 / 2.0)
    return max(0.0, q)

def CellTranslate(TargetCell, ActiveCell): # TargetCell -> 0 as CellTranslate -> ActiveCell
    kt_y =  (TargetCell // L) % L
    kt_x =  (TargetCell - L * kt_y) % L
    ka_y =  (ActiveCell // L) % L
    ka_x =  (ActiveCell - L * ka_y) % L
    delx = (kt_x + ka_x) % L
    dely = (kt_y + ka_y) % L
    return delx + dely * L

def CellIt(a): # Cell index for x,y positions
    return int((a[0] % L) * L) + L * int((a[1] % L) * L)

def CellLimit(CellNumber): # Returns Rightmost x position. Can be used for x and y
    return (CellNumber % L + 1) / float(L)

def dist(x,y):
   """periodic distance between two two-dimensional points x and y"""
   d_x= abs(x[0] - y[0]) % 1.0
   d_x = min(d_x, 1.0 - d_x)
   d_y= abs(x[1] - y[1]) % 1.0
   d_y = min(d_y, 1.0 - d_y)
   return  math.sqrt(d_x**2 + d_y**2)

def WalkerSet(pi_in):
    pi = deepcopy(pi_in)
    N_walker = len(pi)
    walker_mean = sum(a[0] for a in pi) / float(N_walker)
    long_s = []
    short_s = []
    for p in pi:
        if p[0] > walker_mean:
            long_s.append(p)
        else:
            short_s.append(p)
    walker_table = []
    for k in range(N_walker - 1):
        e_plus = long_s.pop()
        e_minus = short_s.pop()
        walker_table.append((e_minus[0], e_minus[1], e_plus[1]))
        e_plus[0] = e_plus[0] - (walker_mean - e_minus[0])
        if e_plus[0] < walker_mean:
            short_s.append(e_plus)
        else:
            long_s.append(e_plus)
    if long_s != []:
        walker_table.append((long_s[0][0], long_s[0][1], long_s[0][1]))
    else:
        walker_table.append((short_s[0][0], short_s[0][1], short_s[0][1]))
    return N_walker, walker_mean, walker_table

def WalkerSample(walker_table, walker_mean, N_walker):
    Upsilon = random.uniform(0.0, walker_mean)
    i = random.randint(0, N_walker - 1)
    if Upsilon < walker_table[i][0]:
        return walker_table[i][1]
    else: return walker_table[i][2]

CellBoundary = []
NStep = 10 # going around the boundary of a cell (naive)
for i in range(NStep):
    x = i / float(NStep)
    CellBoundary += [(x, 0.0), (1.0, x), (1.0 - x, 1.0), (0.0, 1.0 - x)]

histo = []
L = 10
NCell = L ** 2
NPart = 40
beta = 1.0
k_max = 2 # extension of periodic images.
CellLL = [(x / float(L), y / float(L)) for y in range(L) for x in range(L)]
CellExclude = [0, 1, L - 1, L, L + 1, 2 * L - 1, NCell - L, NCell - L + 1, NCell - 1]
QCell = []
for k in range(NCell):
    if k in CellExclude: QCell.append([0, k])
    else: Dummy = QCell.append([CellRate(CellLL[k], (0.0, 0.0), L,
            CellBoundary), k])
QPrime = sum(a[0] for a in QCell) # cell rate
N_walker, walker_mean, walker_table = WalkerSet(QCell)
Particles = []
for k in range(NPart):
    a = (random.uniform(0.0, 1.0), random.uniform(0.0, 1.0))
    Particles.append(a)
for iter in range(10000):
    if random.randint(0,1) == 1:
        for k in range(NPart):
            Particles[k] = (Particles[k][1], Particles[k][0])
    Surplus = []
    CellOcc = {}
    ltilde = 0.18
    for k in range(NPart):
        a = Particles[k]
        n_cell = CellIt(a)
        if not CellOcc.has_key(n_cell):
            CellOcc[n_cell] = a
        else:
            Surplus.append(a)
    if random.uniform(0.0, 1.0) < len(Surplus) / float(NPart):
        ActiveParticle = random.choice(Surplus)
        Surplus.remove(ActiveParticle) # Active particle into Cell, not into Surplus
        ActiveCell = CellIt(ActiveParticle)
        if CellOcc.has_key(ActiveCell):
            Surplus.append(CellOcc.pop(ActiveCell))
        CellOcc[ActiveCell] = ActiveParticle[:]
    else:
        while True:
            ActiveCell = random.randint(0, NCell - 1)
            if CellOcc.has_key(ActiveCell):
                ActiveParticle = CellOcc[ActiveCell]
                break
    ActiveCellLimit = CellLimit(ActiveCell)
    distance_to_go = ltilde
    while distance_to_go > 0.0:
        PossibleActiveParticle= ActiveParticle[:]
        Possible_distance_to_go = distance_to_go
        ActiveCellChange = False
        Lifting = False
        while True:
            DistanceLimit = PossibleActiveParticle[0] + Possible_distance_to_go
            DelS =-math.log(random.uniform(0.0, 1.0)) / QPrime
            if DistanceLimit < ActiveCellLimit and PossibleActiveParticle[0] + DelS > DistanceLimit:
                PossibleActiveParticle = (DistanceLimit, PossibleActiveParticle[1])
                Possible_distance_to_go = 0.0
                break # Distance-to-go break
            elif PossibleActiveParticle[0] + DelS > ActiveCellLimit:
                Possible_distance_to_go -= (ActiveCellLimit - PossibleActiveParticle[0])
                PossibleActiveParticle = (ActiveCellLimit % 1.0, PossibleActiveParticle[1])
                ActiveCellChange = True
                break  #AC break
            else:
                PossibleActiveParticle = (PossibleActiveParticle[0] + DelS, PossibleActiveParticle[1])
                Possible_distance_to_go -= DelS
                TargetCell = WalkerSample(walker_table, walker_mean, N_walker)
                cell_rate_active_target = QCell[TargetCell][0]
                TargetCell = CellTranslate(TargetCell, ActiveCell)
                if CellOcc.has_key(TargetCell):
                    TargetParticle = CellOcc[TargetCell]
                    Ratio = PairRate(TargetParticle[0] - PossibleActiveParticle[0], TargetParticle[1] -
                                 PossibleActiveParticle[1], k_max) / cell_rate_active_target
                    delx = (TargetParticle[0] - PossibleActiveParticle[0]) % 1.0
                    dely = (TargetParticle[1] - PossibleActiveParticle[1]) % 1.0
                    if random.uniform(0.0, 1.0) < Ratio:
                        Lifting = True
                        break # Lifting break
#
#   here the sr (naive and a bit approximate, as we suppose a constant rate)
#
        ToBeChecked = Surplus[:]
        for ECell in CellExclude:
            DummyCell = CellTranslate(ECell, ActiveCell)
            if CellOcc.has_key(DummyCell):
                ToBeChecked.append(CellOcc[DummyCell])
        ToBeChecked.remove(ActiveParticle)
        DelSMax =  PossibleActiveParticle[0] - ActiveParticle[0]
        for PossibleTargetParticle in ToBeChecked:
            QRateLoc = PairRate(PossibleTargetParticle[0] - ActiveParticle[0],
                   PossibleTargetParticle[1] - ActiveParticle[1], k_max)
            if QRateLoc > 0.0:
                DelS =-math.log(random.uniform(0.0, 1.0)) / QRateLoc
                if DelS < DelSMax: # Displacement cannot
#                       interfere with cell boundaries or distance_to_go
                    Lifting = True
                    ActiveCellChange = False
                    Possible_distance_to_go = distance_to_go - DelS
                    DelSMax = DelS
                    TargetParticle = PossibleTargetParticle[:] # have to take into
#                       account that the TargetParticle may be a Surplus one...
                    PossibleActiveParticle = (ActiveParticle[0] + DelS,
                                    PossibleActiveParticle[1])
        ActiveParticle = PossibleActiveParticle[:] #First move, then lift
        distance_to_go = Possible_distance_to_go
        if ActiveCellChange:
            NewActiveCell = CellIt((ActiveParticle[0] + 0.001, ActiveParticle[1])) # Naive
            if CellOcc.has_key(NewActiveCell):
                Surplus.append(CellOcc.pop(NewActiveCell))
            CellOcc[NewActiveCell] = ActiveParticle[:]
            CellOcc.pop(ActiveCell)  # Active cell occupied by active particle
            for a in Surplus:
                if CellIt(a) == ActiveCell:
                    CellOcc[ActiveCell] = a
                    Surplus.remove(a)
                    break
            ActiveCell = NewActiveCell
            ActiveCellLimit = CellLimit(ActiveCell)
        else:
            CellOcc[ActiveCell] = ActiveParticle[:]
        if Lifting:
            if TargetParticle in Surplus:
                TargetCell = CellIt(TargetParticle)
                Surplus.remove(TargetParticle)
                if CellOcc.has_key(TargetCell):
                    Surplus.append(CellOcc.pop(TargetCell))
                CellOcc[TargetCell] = TargetParticle[:]
            ActiveParticle = TargetParticle[:]
            ActiveCell = CellIt(ActiveParticle) # Naive , zu verbessern
            ActiveCellLimit = CellLimit(ActiveCell)
#   Naive, Particles vector for x <-> y transfer
    Particles = []
    for k in range(NCell):
        if CellOcc.has_key(k):
            Particles.append(CellOcc[k])
    Particles += Surplus
    for k in range(NPart):
        for l in range(k):
            histo.append(dist(Particles[k], Particles[l]))

pylab.hist(histo, bins=100, range=(0.0, 1.0), normed=True)
pylab.title('Demo_cell, ECMC, $k_{\max}$ = ' + str(k_max) + ' $NPart$ = ' +
             str(NPart))
pylab.savefig('eventchain.png')
pylab.show()

Retrieved from "http://www.lps.ens.fr/~krauth/index.php/Kapfer_Krauth_2016"

Kapfer Krauth 2016

From Werner KRAUTH

Revision as of 09:24, 28 June 2016

Paper

Illustration

Python implementation

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