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  1. Main Page (27779 bytes)
    41: ...written by Brice Huang, a MIT graduate student in Mathematics. He completed an enormous body of previous...
    50: ...t/ Erwin Schrödinger International Institute for Mathematics and Physics], University of Vienna, Vienna...
    52: ...ns] (Invited talk at Institute for Pure & Applied Mathematics, UCLA, Los Angeles (USA), 2017)
    70: ...elocity trapping" which, correctly formulated and mathematically formalized, gave a manuscript which was...
    94: Our work opens the door to obtaining mathematically rigorous results for speedups of nonrev...
  2. Publications WK (19587 bytes)
    36: ...uth ''Direction-sweep Markov chains'' J. Phys. A: Math Theor. 55 105003 (2022)]]
    210: ...heres and related systems'' Journal of Physics A: Math. Gen. 28, L597 (1995)]]
    248: ... $J=pm 1$ neural network'' Journal of Physics A: Math. Gen. ''' 22''' L519 (1989)
    252: ...namics of neural networks'' Journal of Physics A: Math. Gen. ''' 21''' 2995 (1988)
    254: ...bility in neural networks'' Journal of Physics A: Math. Gen. ''' 20''', L745 (1987)
  3. 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)]]
  4. 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)
  5. Dress Krauth 1995 (2568 bytes)
    1: ...heres and related systems'' Journal of Physics A: Math. Gen. 28, L597 (1995)
  6. 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
  7. 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)
  8. 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 \
  9. 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)
  10. 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...
  11. Event disks box.py (2987 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)
  12. Event chain.py (2176 bytes)
    13: import random, math
    21: d_para = math.sqrt(4.0 * sigma ** 2 - d_perp ** 2)
  13. 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)
  14. Gauss 3d.py (471 bytes)
    7: import random, math
  15. Direct surface.py (529 bytes)
    7: import random, math
    13: radius = math.sqrt(sum(x ** 2 for x in R))
  16. Direct sphere.py (655 bytes)
    7: import random, math
    15: / math.sqrt(x ** 2 + y ** 2 + z ** 2)
  17. Thermo ising.py (978 bytes)
    10: import math, os
    31: weight = math.exp(- E / T) * dos[E]
  18. Markov ising.py (1406 bytes)
    8: import random, math
    27: if random.uniform(0.0, 1.0) < math.exp(-beta * delta_E):
  19. Cluster ising.py (1040 bytes)
    9: import random, math
    17: p = 1.0 - math.exp(-2.0 / T)
  20. Heat bath ising.py (1187 bytes)
    7: import random, math
    27: if Upsilon < 1.0 / (1.0 + math.exp(-2.0 * beta * h)):

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