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- Publications WK (19228 bytes)
31: ...uth ''Direction-sweep Markov chains'' J. Phys. A: Math Theor. 55 105003 (2022)]]
203: ...heres and related systems'' Journal of Physics A: Math. Gen. 28, L597 (1995)]]
241: ... $J=pm 1$ neural network'' Journal of Physics A: Math. Gen. ''' 22''' L519 (1989)
245: ...namics of neural networks'' Journal of Physics A: Math. Gen. ''' 21''' 2995 (1988)
247: ...bility in neural networks'' Journal of Physics A: Math. Gen. ''' 20''', L745 (1987) - Group WK (13268 bytes)
21: ...brillantly qualified to work on a famous paper by mathematician K. Böröczky on locally stable disk pac...
44: ... Krauth Direction-sweep Markov chains J. Phys. A: Math Theor. 55 105003 (2022)]] - Bernard Krauth Wilson 2009 (6284 bytes)
17: ...te Carlo. This was even [[Lei Krauth 2018| proven mathematically]] in a special case, together with Ze ...
37: moves starts at <math>(x,y)=(0.,0.)</math>.
48: import math, pylab, sys, cPickle
55: return math.sqrt(d_x**2 + d_y**2)
89: x_dummy = x_image[0] - math.sqrt(1.0 - x_image[1]**2) - Dress Krauth 1995 (2568 bytes)
1: ...heres and related systems'' Journal of Physics A: Math. Gen. 28, L597 (1995) - Krauth 2010 (5787 bytes)
1: ...://www.oup.com/us/catalog/general/subject/Physics/Mathematicalphysics/?view=usa&sf=toc&ci=9780199574612 ...
48: from math import sqrt, sinh, tanh,exp - Bernard Krauth 2012 (24591 bytes)
47: import math, pylab, sys, cPickle
58: return math.sqrt(d_x**2 + d_y**2)
97: del_x= math.sqrt(d**2 - x_vec[1]**2)
227: import math, pylab, cPickle
236: return math.sqrt(d_x**2 + d_y**2) - Krauth 2002 (2404 bytes)
18: import random, math
22: norm = math.sqrt(sum(xk ** 2 for xk in x))
26: dists = [math.sqrt(sum((positions[k][j] - positions[l][j]) ** 2...
47: norm = math.sqrt(sum(xk ** 2 for xk in newpos))
49: new_min_dist = min([math.sqrt(sum((positions[l][j] - newpos[j]) ** 2 \ - Trieste Lectures 2015 (2294 bytes)
22: import math, random
49: import random, math
57: d_para = math.sqrt(4.0 * sigma ** 2 - d_perp ** 2) - Direct disks box.py (793 bytes)
8: import random, math
17: min_dist = min(math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2) for... - Event disks box.py (2986 bytes)
9: import math
28: del_t = - (scal + math.sqrt(Upsilon)) / del_v_sq
52: abs_x = math.sqrt(del_x[0] ** 2 + del_x[1] ** 2) - Event chain.py (2176 bytes)
13: import random, math
21: d_para = math.sqrt(4.0 * sigma ** 2 - d_perp ** 2) - Gauss test.py (719 bytes)
7: import random, math
10: phi = random.uniform(0.0, 2.0 * math.pi)
12: Psi = - math.log(Upsilon)
13: r = sigma * math.sqrt(2.0 * Psi)
14: x = r * math.cos(phi) - Gauss 3d.py (471 bytes)
7: import random, math - Direct surface.py (529 bytes)
7: import random, math
13: radius = math.sqrt(sum(x ** 2 for x in R)) - Direct sphere.py (655 bytes)
7: import random, math
15: / math.sqrt(x ** 2 + y ** 2 + z ** 2) - Thermo ising.py (978 bytes)
10: import math, os
31: weight = math.exp(- E / T) * dos[E] - Markov ising.py (1406 bytes)
8: import random, math
27: if random.uniform(0.0, 1.0) < math.exp(-beta * delta_E): - Cluster ising.py (1040 bytes)
9: import random, math
17: p = 1.0 - math.exp(-2.0 / T) - Heat bath ising.py (1187 bytes)
7: import random, math
27: if Upsilon < 1.0 / (1.0 + math.exp(-2.0 * beta * h)): - Combinatorial ising.py (3881 bytes)
12: import numpy, math
17: nu = math.tanh(beta)
18: alpha = numpy.exp(comp_i * math.pi / 4.0) * math.tanh(beta)
19: alphabar = numpy.exp(-comp_i * math.pi / 4.0) * math.tanh(beta)
69: print 2 ** N * math.cosh(beta) ** n_edge * \
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