/usr/share/pyshared/openopt/solvers/UkrOpt/ralg_oo.py is in python-openopt 0.38+svn1589-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 | from numpy import diag, array, sqrt, eye, ones, inf, any, copy, zeros, dot, where, all, tile, sum, nan, isfinite, float64, isnan, log10, \
max, sign, array_equal, nonzero, ix_, arctan, pi, logical_not, logical_and, atleast_2d, matrix
from openopt.kernel.ooMisc import norm
try:
from numpy.linalg import solve, LinAlgError
except ImportError:
LinAlgError = Exception
def solve(*args, **kw):
print('ralg with equality constraints is unimplemented yet')
raise Exception('ralg with equality constraints is unimplemented yet')
from openopt.kernel.nonOptMisc import scipyAbsentMsg, scipyInstalled, isPyPy
import openopt
#try:
# from numpy.linalg import cond
#except:
# print 'warning: no cond in numpy.linalg, matrix B rejuvenation check will be omitted'
# cond = lambda Matrix: 1
from openopt.kernel.baseSolver import *
from openopt.kernel.Point import Point
from openopt.kernel.ooMisc import economyMult, Len
from openopt.kernel.setDefaultIterFuncs import *
from openopt.solvers.UkrOpt.UkrOptMisc import getBestPointAfterTurn
class ralg(baseSolver):
__name__ = 'ralg'
__license__ = "BSD"
__authors__ = "Dmitrey"
__alg__ = "Naum Z. Shor R-algorithm with adaptive space dilation & some modifications"
__optionalDataThatCanBeHandled__ = ['A', 'Aeq', 'b', 'beq', 'lb', 'ub', 'c', 'h']
iterfcnConnected = True
_canHandleScipySparse = True
#ralg default parameters
B = None # if provided it should be square matrix of shape nVars x nVars
alp, h0, nh, q1, q2 = 2.0, 1.0, 3, 'default:0.9 for NLP, 1.0 for NSP', 1.1
hmult = 0.5
S = 0
T = float64
dilationType = 'plain difference'
showLS = False
show_hs = False
showRej = False
showRes = False
show_nnan = False
doBackwardSearch = True
approach = 'all active'
newLinEq = True
new_bs = True
skipPrevIterNaNsInDilation = True
innerState = None
penalties = False # True means for nonlinear equalities only
#new_s = False
def needRej(self, p, b, g, g_dilated):
# r = log10(1e15 * p.norm(g_dilated) / p.norm(g))
# if isfinite(r):
# p.debugmsg('%d' % int(r))
#p.debugmsg('%0.2g'%(p.norm(g_dilated) / p.norm(g)))
return 1e14 * p.norm(g_dilated) < p.norm(g)
#checkTurnByGradient = True
def __init__(self): pass
def __solver__(self, p):
alp, h0, nh, q1, q2 = self.alp, self.h0, self.nh, self.q1, self.q2
if isPyPy and not p.isUC:
p.err("ralg hasn't been ported to PyPy for constrained problems yet")
if type(q1) == str:
if p.probType== 'NLP' and p.isUC: q1 = 0.9
else: q1 = 1.0
T = self.T
# alternatively instead of alp=self.alp etc you can use directly self.alp etc
n = p.n
x0 = p.x0
if p.nbeq == 0 or any(abs(p._get_AeqX_eq_Beq_residuals(x0))>p.contol): # TODO: add "or Aeqconstraints(x0) out of contol"
x0[x0<p.lb] = p.lb[x0<p.lb]
x0[x0>p.ub] = p.ub[x0>p.ub]
ind_box_eq = where(p.lb==p.ub)[0]
nEQ = ind_box_eq.size
if nEQ != 0:
initLenBeq = p.nbeq
Aeq, beq, nbeq = copy(p.Aeq), copy(p.beq), p.nbeq
p.Aeq = zeros([Len(p.beq) + nEQ, p.n])
p.beq = zeros(Len(p.beq) + nEQ)
p.beq[:Len(beq)] = beq
p.Aeq[:Len(beq)] = Aeq
for i in range(len(ind_box_eq)):
p.Aeq[initLenBeq+i, ind_box_eq[i]] = 1
p.beq[initLenBeq+i] = p.lb[ind_box_eq[i]] # = p.ub[indEQ[i]], because they are the same
p.nbeq += nEQ
if not self.newLinEq or p.nbeq == 0:
needProjection = False
B0 = eye(n, dtype=T)
restoreProb = lambda *args: 0
Aeq_r, beq_r, nbeq_r = None, None, 0
else:
needProjection = True
B0 = self.getPrimevalDilationMatrixWRTlinEqConstraints(p)
#Aeq, beq, nbeq = p.Aeq, p.beq, p.nbeq
if any(abs(p._get_AeqX_eq_Beq_residuals(x0))>p.contol/16.0):
#p.debugmsg('old point Aeq residual:'+str(norm(dot(Aeq, x0)-beq)))
try:
x0 = self.linEqProjection(x0, p.Aeq, p.beq)
except LinAlgError:
s = 'Failed to obtain projection of start point to linear equality constraints subspace, probably the system is infeasible'
p.istop, p.msg = -25, s
return
#p.debugmsg('new point Aeq residual:'+str(norm(dot(Aeq, x0)-beq)))
if nEQ == 0:
Aeq_r, beq_r, nbeq_r = p.Aeq, p.beq, p.nbeq
else:
Aeq_r, beq_r, nbeq_r = Aeq, beq, nbeq
p.Aeq, p.beq, p.nbeq = None, None, 0
# TODO: return prob with unmodified Aeq, beq
def restoreProb():
p.Aeq, p.beq, p.nbeq = Aeq_r, beq_r, nbeq_r
#if nEQ != 0: restore lb, ub
b = B0.copy() if self.B is None else self.B
# B_f = diag(ones(n))
# B_constr = diag(ones(n))
hs = asarray(h0, T)
if self.innerState is not None:
hs = self.innerState['hs']
b = self.innerState['B']
ls_arr = []
w = asarray(1.0/alp-1.0, T)
""" Shor r-alg engine """
bestPoint = p.point(array(copy(x0).tolist(), T)) # tolist() for PyPy compatibility
prevIter_best_ls_point = bestPoint
prevIter_PointForDilation = bestPoint
g = bestPoint._getDirection(self.approach)
prevDirectionForDilation = g
moveDirection = g
if not any(g) and all(isfinite(g)):
# TODO: create ENUMs
p.iterfcn(bestPoint)
restoreProb()
p.istop = 14 if bestPoint.isFeas(False) else -14
p.msg = 'move direction has all-zero coords'
return
HS = []
LS = []
SwitchEncountered = False
selfNeedRej = False
doScale = False
#directionVectorsList = []
# #pass-by-ref! not copy!
# if p.isFeas(p.x0): b = B_f
# else: b = B_constr
# if p.debug and hasattr(p, 'x_opt'):
# import scipy
# exactDirection = x0-p.x_opt
# asdf_0 = exactDirection * (0.2+scipy.rand(n))
# #asdf = asdf_0.copy()
fTol = p.fTol if p.fTol is not None else 15*p.ftol
# CHANGES
if self.penalties:
oldVal = p.f(p.x0)
newVal = inf
x = p.x0
#H, DH = p.h, p.dh
if p.nh != 0:
#S = 1.0
_Aeq = p.dh(x)
_beq = -p.h(x)
df = p.df(x)
if n>=150 and not scipyInstalled:
p.pWarn(scipyAbsentMsg)
if n>100 and scipyInstalled:
from scipy.sparse import eye as Eye # to prevent numpy.eye overwrite
HH = Eye(n, n)
else:
HH = eye(n)
qp = openopt.QP(H=HH, f=df, Aeq=_Aeq, beq=_beq)
# print ('len(_beq): %d' % len(_beq))
# assert len(_beq) != 0
QPsolver = openopt.oosolver('cvxopt_qp', iprint=-1)
if not QPsolver.isInstalled:
#p.pWarn('to use ')
S = None
else:
r = qp.solve(QPsolver)
#S = 2.0*abs(r.duals).sum() if r.istop > 0 else 0
S = 10.0*sum(abs(r.duals)) if r.istop > 0 else None
while any(p.h(x)) > p.contol:
if S is not None:
p2 = getattr(openopt, p.probType)(p.f, x)
p.inspire(p2)
p2.x0 = x
p2.h = p2.dh = None
p2.userProvided.h = p2.userProvided.dh = False
p2.nh = 0
p2.f = lambda *args, **kwargs: p.f(*args, **kwargs) + sum(abs(S * p.h(*args, **kwargs)))
p2.df = lambda *args, **kwargs: p.df(*args, **kwargs) + dot(S * sign(p.h(*args, **kwargs)), p.dh(*args, **kwargs))
#p2.iterfcn = p.iterfcn
# def df2(*args, **kwargs):
# r1 = p.df(*args, **kwargs)
# r2 = S * dot(p.dh(*args, **kwargs).reshape(-1, 1), sign(p.h(*args, **kwargs))).flatten()
# #raise 0
# return r1+r2
# #p2.df = lambda *args, **kwargs: p.df(*args, **kwargs) + S * dot(p.dh(x).reshape(-1, 1), sign(p.h(*args, **kwargs))).flatten()
# p2.df = df2
# #raise 0
r2 = p2.solve(p.solver, iprint=10)
if r2.stopcase >= 0:
x = r2.xf
p.solver.innerState = r2.extras['innerState']
oldVal, newVal = newVal, r2.ff
else:
if r2.istop == IS_LINE_SEARCH_FAILED:
# TODO: custom S as raising penalties
pass
if p.isFeas(p2.xk):
p.xf = p.xk = p2.xk
p.istop, p.msg = p2.istop, p2.msg
return
else:
S *= 50
#print('max residual:%0.2e'% r2.rf)
else: # failed to solve QP
break
#print 'b:', b, '\nhs:', hs
# CHANGES END
""" Ralg main cycle """
for itn in range(p.maxIter+10):
doDilation = True
lastPointOfSameType = None # to prevent possible bugs
alp_addition = 0.0
iterStartPoint = prevIter_best_ls_point
x = iterStartPoint.x.copy()
g_tmp = economyMult(b.T, moveDirection)
if any(g_tmp): g_tmp /= p.norm(g_tmp)
g1 = p.matmult(b, g_tmp)
# norm_moveDirection = p.norm(g1)
# if doScale:
# g1 *= (norm_moveDirection_prev/norm_moveDirection) ** 0.5
# norm_moveDirection_prev = norm_moveDirection
# if p.debug and hasattr(p, 'x_opt'):
# cos_phi_0 = p.matmult(moveDirection, prevIter_best_ls_point.x - p.x_opt)/p.norm(moveDirection)/p.norm(prevIter_best_ls_point.x - p.x_opt)
# cos_phi_1 = p.matmult(g1, prevIter_best_ls_point.x - p.x_opt)/p.norm(g1)/p.norm(prevIter_best_ls_point.x - p.x_opt)
# print('beforeDilation: %f afterDilation: %f' % (cos_phi_0, cos_phi_1) )
# asdf = asdf_0.copy()
# g_tmp = economyMult(b.T, asdf)
#
# #g_tmp = p.matmult(b.T, asdf)
#
# if any(g_tmp): g_tmp /= p.norm(g_tmp)
# asdf = p.matmult(b, g_tmp)
# cos_phi = dot(asdf, exactDirection) / p.norm(asdf) / p.norm(exactDirection)
# p.debugmsg('cos_phi:%f' % cos_phi)
# assert cos_phi >0
""" Forward line search """
hs_cumsum = 0
hs_start = hs
for ls in range(p.maxLineSearch):
hs_mult = 1.0
if ls > 20:
hs_mult = 2.0
elif ls > 10:
hs_mult = 1.5
elif ls > 2:
hs_mult = 1.05
hs *= hs_mult
x -= hs * g1
hs_cumsum += hs
newPoint = p.point(x) if ls == 0 else iterStartPoint.linePoint(hs_cumsum/(hs_cumsum-hs), oldPoint) # TODO: take ls into account?
if not p.isUC:
if newPoint.isFeas(True) == iterStartPoint.isFeas(True):
lastPointOfSameType = newPoint
if self.show_nnan: p.info('ls: %d nnan: %d' % (ls, newPoint.__nnan__()))
if ls == 0:
oldPoint = prevIter_best_ls_point#prevIterPoint
oldoldPoint = oldPoint
#if not self.checkTurnByGradient:
if newPoint.betterThan(oldPoint, altLinInEq=True):
if newPoint.betterThan(bestPoint, altLinInEq=False): bestPoint = newPoint
oldoldPoint = oldPoint
oldPoint, newPoint = newPoint, None
else:
if not itn % 4:
for fn in ['_lin_ineq', '_lin_eq']:
if hasattr(newPoint, fn): delattr(newPoint, fn)
break
hs /= hs_mult
if ls == p.maxLineSearch-1:
p.istop, p.msg = IS_LINE_SEARCH_FAILED, 'maxLineSearch (' + str(p.maxLineSearch) + ') has been exceeded, the problem seems to be unbounded'
restoreProb()
return
#iterPoint = newPoint
PointForDilation = newPoint
#best_ls_point = newPoint if ls == 0 else oldPoint
#if p.debug and ls != 0: assert not oldPoint.betterThan(best_ls_point)
""" Backward line search """
mdx = max((150, 1.5*p.n))*p.xtol
if itn == 0: mdx = max((hs / 128.0, 128*p.xtol )) # TODO: set it after B rej as well
ls_backward = 0
maxLS = 3 if ls == 0 else 1
# if ls <=3 or ls > 20:
if self.doBackwardSearch:
if self.new_bs:
best_ls_point, PointForDilation, ls_backward = \
getBestPointAfterTurn(oldoldPoint, newPoint, maxLS = maxLS, maxDeltaF = 150*p.ftol, \
maxDeltaX = mdx, altLinInEq = True, new_bs = True)
if PointForDilation.isFeas(True) == iterStartPoint.isFeas(True):
lastPointOfSameType = PointForDilation
# elif best_ls_point.isFeas(altLinInEq=True) == iterStartPoint.isFeas(altLinInEq=True):
# lastPointOfSameType = best_ls_point
else:
best_ls_point, ls_backward = \
getBestPointAfterTurn(oldoldPoint, newPoint, maxLS = maxLS, altLinInEq = True, new_bs = False)
PointForDilation = best_ls_point
# TODO: extract last point from backward search, that one is better than iterPoint
if best_ls_point.betterThan(bestPoint): bestPoint = best_ls_point
#p.debugmsg('ls_backward:%d' % ls_backward)
if ls == 0 and ls_backward == -maxLS:
#pass
alp_addition += 0.25
#hs *= 0.9
if ls_backward <= -1 and itn != 0: # TODO: mb use -1 or 0 instead?
pass
#alp_addition -= 0.25*ls_backward # ls_backward less than zero
#hs *= 2 ** min((ls_backward+1, 0))
else:
pass
#hs *= 0.95
best_ls_point = PointForDilation # elseware lots of difficulties
""" Updating hs """
step_x = p.norm(PointForDilation.x - prevIter_PointForDilation.x)
step_f = abs(PointForDilation.f() - prevIter_PointForDilation.f())
HS.append(hs_start)
assert ls >= 0
LS.append(ls)
if itn > 3:
mean_ls = (3*LS[-1] + 2*LS[-2]+LS[-3]) / 6.0
j0 = 3.3
if mean_ls > j0:
hs = (mean_ls - j0 + 1)**0.5 * hs_start
else:
#hs = (ls/j0) ** 0.5 * hs_start
hs = hs_start
if ls == 0 and ls_backward == -maxLS:
shift_x = step_x / p.xtol
RD = log10(shift_x+1e-100)
if PointForDilation.isFeas(True) or prevIter_PointForDilation.isFeas(True):
RD = min((RD, asscalar(asarray(log10(step_f / p.ftol + 1e-100)))))
if RD > 1.0:
mp = (0.5, (ls/j0) ** 0.5, 1 - 0.2*RD)
hs *= max(mp)
#from numpy import argmax
#print argmax(mp), mp
""" Handling iterPoints """
best_ls_point = PointForDilation
#if not SwitchEncountered and p.nh != 0 and PointForDilation.isFeas(altLinInEq=False) != prevIter_PointForDilation.isFeas(altLinInEq=False):
#SwitchEncountered = True
#selfNeedRej = True
involve_lastPointOfSameType = False
if lastPointOfSameType is not None and PointForDilation.isFeas(True) != prevIter_PointForDilation.isFeas(True):
# TODO: add middle point for the case ls = 0
assert self.dilationType == 'plain difference'
#directionForDilation = lastPointOfSameType._getDirection(self.approach)
PointForDilation = lastPointOfSameType
involve_lastPointOfSameType = True
#directionForDilation = newPoint.__getDirection__(self.approach) # used for dilation direction obtaining
# if not self.new_bs or ls != 0:
# moveDirection = iterPoint.__getDirection__(self.approach)
# else:
# moveDirection = best_ls_point.__getDirection__(self.approach)
#directionForDilation = pointForDilation.__getDirection__(self.approach)
# cos_phi = -p.matmult(moveDirection, prevIterPoint.__getDirection__(self.approach))
# assert cos_phi.size == 1
# if cos_phi> 0:
# g2 = moveDirection#pointForDilation.__getDirection__(self.approach)
# else:
# g2 = pointForDilation.__getDirection__(self.approach)
if itn == 0:
p.debugmsg('hs: ' + str(hs))
p.debugmsg('ls: ' + str(ls))
if self.showLS: p.info('ls: ' + str(ls))
if self.show_hs: p.info('hs: ' + str(hs))
if self.show_nnan: p.info('nnan: ' + str(best_ls_point.__nnan__()))
if self.showRes:
r, fname, ind = best_ls_point.mr(True)
p.info(fname+str(ind))
""" Set dilation direction """
#if sum(p.dotmult(g, g2))>0:
#p.debugmsg('ralg warning: slope angle less than pi/2. Mb dilation for the iter will be omitted.')
#doDilation = False
# CHANGES
# if lastPointOfSameType is None:
# if currIterPointIsFeasible and not prevIterPointIsFeasible:
# alp_addition += 0.1
# elif prevIterPointIsFeasible and not currIterPointIsFeasible:
# alp_addition -= 0.0
# CHANGES END
# r_p, ind_p, fname_p = prevIter_best_ls_point.mr(1)
# r_, ind_, fname_ = PointForDilation.mr(1)
#else:
#print itn,'>>>>>>>>>', currIterPointIsFeasible
""" Excluding derivatives switched to/from NaN """
if self.skipPrevIterNaNsInDilation:
c_prev, c_current = prevIter_PointForDilation.c(), PointForDilation.c()
h_prev, h_current = prevIter_PointForDilation.h(), PointForDilation.h()
""" Handling switch to NaN """
NaN_derivatives_excluded = False
if self.skipPrevIterNaNsInDilation:
assert self.approach == 'all active'
if not prevIter_PointForDilation.isFeas(True):
""" processing NaNs in nonlin inequality constraints """
ind_switch_ineq_to_nan = where(logical_and(isnan(c_current), c_prev>0))[0]
if len(ind_switch_ineq_to_nan) != 0:
NaN_derivatives_excluded = True
tmp = prevIter_PointForDilation.dc(ind_switch_ineq_to_nan)
if hasattr(tmp, 'toarray'):
tmp = tmp.A
if len(ind_switch_ineq_to_nan)>1:
tmp *= (c_prev[ind_switch_ineq_to_nan] /sqrt((tmp**2).sum(1))).reshape(-1, 1)
else:
tmp *= c_prev[ind_switch_ineq_to_nan] / norm(tmp)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
#print '1: excluded:', norm(tmp), norm(prevDirectionForDilation)
prevDirectionForDilation -= tmp
#print '1: result=', norm(prevDirectionForDilation)
""" processing NaNs in nonlin equality constraints """
ind_switch_eq_to_nan = where(logical_and(isnan(h_current), h_prev>0))[0]
if len(ind_switch_eq_to_nan) != 0:
NaN_derivatives_excluded = True
tmp = prevIter_PointForDilation.dh(ind_switch_eq_to_nan)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
prevDirectionForDilation -= tmp
ind_switch_eq_to_nan = where(logical_and(isnan(h_current), h_prev<0))[0]
if len(ind_switch_eq_to_nan) != 0:
NaN_derivatives_excluded = True
tmp = prevIter_PointForDilation.dh(ind_switch_eq_to_nan)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
prevDirectionForDilation += tmp
directionForDilation = PointForDilation._getDirection(self.approach)
""" Handling switch from NaN """
if self.skipPrevIterNaNsInDilation:
if not PointForDilation.isFeas(True):
""" processing NaNs in nonlin inequality constraints """
ind_switch_ineq_from_nan = where(logical_and(isnan(c_prev), c_current>0))[0]
if len(ind_switch_ineq_from_nan) != 0:
NaN_derivatives_excluded = True
tmp = PointForDilation.dc(ind_switch_ineq_from_nan)
if hasattr(tmp, 'toarray'):
tmp = tmp.A
if len(ind_switch_ineq_from_nan)>1:
tmp *= (c_current[ind_switch_ineq_from_nan] /sqrt((tmp**2).sum(1))).reshape(-1, 1)
else:
tmp *= c_current[ind_switch_ineq_from_nan] / norm(tmp)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
#print '2: excluded:', norm(tmp), norm(directionForDilation)
directionForDilation -= tmp
#print '2: result=', norm(directionForDilation)
""" processing NaNs in nonlin equality constraints """
ind_switch_eq_from_nan = where(logical_and(isnan(h_prev), h_current>0))[0]
if len(ind_switch_eq_from_nan) != 0:
NaN_derivatives_excluded = True
tmp = PointForDilation.dh(ind_switch_eq_from_nan)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
directionForDilation -= tmp
ind_switch_eq_from_nan = where(logical_and(isnan(h_prev), h_current<0))[0]
if len(ind_switch_eq_from_nan) != 0:
NaN_derivatives_excluded = True
tmp = PointForDilation.dh(ind_switch_eq_from_nan)
if tmp.ndim>1: tmp = tmp.sum(0)
if not isinstance(tmp, ndarray) or isinstance(tmp, matrix): tmp = tmp.A.flatten() # dense or sparse matrix
directionForDilation += tmp
# # CHANGES
# gn = g2/norm(g2)
# if len(directionVectorsList) == 0 or n < 3: pass
# else:
# if len(directionVectorsList) == 1 or abs(dot(directionVectorsList[-1], directionVectorsList[-2]))>0.999:
# projectionComponentLenght = abs(dot(directionVectorsList[-1], gn))
# restLength = sqrt(1 - min((1, projectionComponentLenght))**2)
# else:
# e1 = directionVectorsList[-1]
# e2 = directionVectorsList[-2] - dot(directionVectorsList[-1], directionVectorsList[-2]) * directionVectorsList[-1]
# e2 /= norm(e2)
#
# proj1, proj2 = dot(e1, gn), dot(e2, gn)
# rest = gn - proj1 * e1 - proj2 * e2
# restLength = norm(rest)
# if restLength > 1+1e-5: p.pWarn('possible error in ralg solver: incorrect restLength, exceeds 1.0')
#
# # TODO: make it parameters of ralg
# commonCoeff, alp_add_coeff = 0.5, 1.0
#
# if restLength < commonCoeff * (n - 2.0) / n:
# #pass
# alpAddition = 0.5+(arctan((n - 2.0) / (n * restLength)) - pi / 4.0) / (pi / 2.0) * alp_add_coeff
# #p.debugmsg('alpAddition:' + str(alpAddition))
# assert alpAddition > 0 # if someone incorrectly modifies commonCoeff it can be less than zero
# alp_addition += alpAddition
# #p.debugmsg('alp_addition:' + str(alp_addition))
#
# directionVectorsList.append(gn)
# if len(directionVectorsList) > 2: directionVectorsList = directionVectorsList[:-2]
# # CHANGES END
if self.dilationType == 'normalized' and (not fname_p in ('lb', 'ub', 'lin_eq', 'lin_ineq') \
or not fname_ in ('lb', 'ub', 'lin_eq', 'lin_ineq')) and (fname_p != fname_ or ind_p != ind_):
G2, G = directionForDilation/norm(directionForDilation), prevDirectionForDilation/norm(prevDirectionForDilation)
else:
G2, G = directionForDilation, prevDirectionForDilation
if prevIter_PointForDilation.isFeas(True) == PointForDilation.isFeas(True):
g1 = G2 - G
elif prevIter_PointForDilation.isFeas(True):
g1 = G2.copy()
else:
g1 = G.copy()
alp_addition += 0.05
#print p.getMaxResidual(PointForDilation.x, 1)
##############################################
# the case may be occured when
# 1) lastPointOfSameType is used
# or
# 2) some NaN from constraints have been excluded
if norm(G2 - G) < 1e-12 * min((norm(G2), norm(G))) and (involve_lastPointOfSameType or NaN_derivatives_excluded):
p.debugmsg("ralg: 'last point of same type gradient' is used")
g1 = G2
##############################################
#g1 = -G.copy() # signum doesn't matter here
# changes wrt infeas constraints
# if prevIterPoint.nNaNs() != 0:
# cp, hp = prevIterPoint.c(), prevIterPoint.h()
# ind_infeas_cp, ind_infeas_hp = isnan(cp), isnan(hp)
#
# c, h = iterPoint.c(), iterPoint.h()
# ind_infeas_c, ind_infeas_h = isnan(c), isnan(h)
#
# ind_goodChange_c = logical_and(ind_infeas_cp, logical_not(ind_infeas_c))
# ind_goodChange_h = logical_and(ind_infeas_hp, logical_not(ind_infeas_h))
#
# any_c, any_h = any(ind_goodChange_c), any(ind_goodChange_h)
# altDilation = zeros(n)
# if any_c:
# altDilation += sum(atleast_2d(iterPoint.dc(where(ind_goodChange_c)[0])), 0)
# assert not any(isnan(altDilation))
# if any_h:
# altDilation += sum(atleast_2d(iterPoint.dh(where(ind_goodChange_h)[0])), 0)
# if any_c or any_h:
# #print '!>', altDilation
# #g1 = altDilation
# pass
# changes end
""" Perform dilation """
# CHANGES
# g = economyMult(b.T, g1)
# gn = g/norm(g)
# if len(directionVectorsList) == 0 or n < 3 or norm(g1) < 1e-20: pass
# else:
# if len(directionVectorsList) == 1 or abs(dot(directionVectorsList[-1], directionVectorsList[-2]))>0.999:
# projectionComponentLenght = abs(dot(directionVectorsList[-1], gn))
# restLength = sqrt(1 - min((1, projectionComponentLenght))**2)
# else:
# e1 = directionVectorsList[-1]
# e2 = directionVectorsList[-2] - dot(directionVectorsList[-1], directionVectorsList[-2]) * directionVectorsList[-1]
# print dot(directionVectorsList[-1], directionVectorsList[-2])
# e2 /= norm(e2)
# proj1, proj2 = dot(e1, gn), dot(e2, gn)
# rest = gn - proj1 * e1 - proj2 * e2
# restLength = norm(rest)
# assert restLength < 1+1e-5, 'error in ralg solver: incorrect restLength'
#
# # TODO: make it parameters of ralg
# commonCoeff, alp_add_coeff = 0.5, 1.0
#
# if restLength < commonCoeff * (n - 2.0) / n:
# #pass
# alpAddition = 0.5+(arctan((n - 2.0) / (n * restLength)) - pi / 4.0) / (pi / 2.0) * alp_add_coeff
# #p.debugmsg('alpAddition:' + str(alpAddition))
# assert alpAddition > 0 # if someone incorrectly modifies commonCoeff it can be less than zero
# alp_addition += alpAddition
# #p.debugmsg('alp_addition:' + str(alp_addition))
#
# directionVectorsList.append(gn)
# if len(directionVectorsList) > 2: directionVectorsList = directionVectorsList[:-2]
# CHANGES END
if doDilation:
g = economyMult(b.T, g1)
ng = p.norm(g)
if self.needRej(p, b, g1, g) or selfNeedRej:
selfNeedRej = False
if self.showRej or p.debug:
p.info('debug msg: matrix B restoration in ralg solver')
b = B0.copy()
hs = p.norm(prevIter_best_ls_point.x - best_ls_point.x)
# TODO: iterPoint = projection(iterPoint,Aeq) if res_Aeq > 0.75*contol
if ng < 1e-40:
hs *= 0.9
p.debugmsg('small dilation direction norm (%e), skipping' % ng)
if all(isfinite(g)) and ng > 1e-50 and doDilation:
g = (g / ng).reshape(-1,1)
vec1 = economyMult(b, g).reshape(-1,1)# TODO: remove economyMult, use dot?
#if alp_addition != 0: p.debugmsg('alp_addition:' + str(alp_addition))
w = asarray(1.0/(alp+alp_addition)-1.0, T)
vec2 = w * g.T
b += p.matmult(vec1, vec2)
""" Call OO iterfcn """
if hasattr(p, '_df'): delattr(p, '_df')
if best_ls_point.isFeas(False) and hasattr(best_ls_point, '_df'):
p._df = best_ls_point.df().copy()
p.iterfcn(best_ls_point)
""" Check stop criteria """
cond_same_point = array_equal(best_ls_point.x, prevIter_best_ls_point.x)
if cond_same_point and not p.istop:
p.istop = 14
p.msg = 'X[k-1] and X[k] are same'
p.stopdict[SMALL_DELTA_X] = True
restoreProb()
self.innerState = {'B': b, 'hs': hs}
return
s2 = 0
if p.istop and not p.userStop:
if p.istop not in p.stopdict: p.stopdict[p.istop] = True # it's actual for converters, TODO: fix it
if SMALL_DF in p.stopdict:
if best_ls_point.isFeas(False): s2 = p.istop
p.stopdict.pop(SMALL_DF)
if SMALL_DELTA_F in p.stopdict:
# TODO: implement it more properly
if best_ls_point.isFeas(False) and prevIter_best_ls_point.f() != best_ls_point.f(): s2 = p.istop
p.stopdict.pop(SMALL_DELTA_F)
if SMALL_DELTA_X in p.stopdict:
if best_ls_point.isFeas(False) or not prevIter_best_ls_point.isFeas(False) or cond_same_point: s2 = p.istop
p.stopdict.pop(SMALL_DELTA_X)
# if s2 and (any(isnan(best_ls_point.c())) or any(isnan(best_ls_point.h()))) \
# and not p.isNaNInConstraintsAllowed\
# and not cond_same_point:
# s2 = 0
if not s2 and any(p.stopdict.values()):
for key, val in p.stopdict.items():
if val == True:
s2 = key
break
p.istop = s2
for key, val in p.stopdict.items():
if key < 0 or key in set([FVAL_IS_ENOUGH, USER_DEMAND_STOP, BUTTON_ENOUGH_HAS_BEEN_PRESSED]):
p.iterfcn(bestPoint)
self.innerState = {'B': b, 'hs': hs}
return
""" If stop required """
if p.istop:
# if self.needRej(p, b, g1, g) or not feasiblePointWasEncountered:
# b = B0.copy()
# hs = max((p.norm(prevIter_best_ls_point.x - best_ls_point.x) , 128*p.xtol))
# p.istop = 0
# else:
restoreProb()
p.iterfcn(bestPoint)
#p.istop, p.msg = istop, msg
self.innerState = {'B': b, 'hs': hs}
return
""" Some final things for ralg main cycle """
# p.debugmsg('new point Aeq residual:'+str(norm(dot(Aeq, iterPoint.x)-beq)))
# if needProjection and itn!=0:
# #pass
# x2 = self.linEqProjection(iterPoint.x, Aeq, beq)
# p.debugmsg('norm(delta):' + str(norm(iterPoint.x-x2)))
# iterPoint = p.point(x2)
# p.debugmsg('2: new point Aeq residual:'+str(norm(dot(Aeq, iterPoint.x)-beq)))
#p.hs.append(hs)
#g = moveDirection.copy()
#prevDirectionForDilation = directionForDilation
#iterPoint = None
#doScale = self.new_s and prevIter_PointForDilation.isFeas(True) != best_ls_point.isFeas(True)
#print doScale
prevIter_best_ls_point = best_ls_point
prevIter_PointForDilation = best_ls_point
prevDirectionForDilation = best_ls_point._getDirection(self.approach)
moveDirection = best_ls_point._getDirection(self.approach)
def getPrimevalDilationMatrixWRTlinEqConstraints(self, p):
n, Aeq, beq = p.n, p.Aeq, p.beq
nLinEq = len(p.beq)
ind_fixed = where(p.lb==p.ub)[0]
arr=ones(n, dtype=self.T)
arr[ind_fixed] = 0
b = diag(arr)
if hasattr(Aeq, 'tocsc'):Aeq = Aeq.tocsc()
for i in range(nLinEq):
vec = Aeq[i]
#raise 0
if hasattr(vec, 'toarray'): vec = vec.toarray().flatten()
g = economyMult(b.T, vec)
if not any(g): continue
#ind_nnz = nonzero(g)[0]
ng = norm(g)
g = (g / ng).reshape(-1,1)
vec1 = p.matmult(b, g)# TODO: remove economyMult, use dot?
vec2 = -g.T
b += p.matmult(vec1, vec2)
# if len(ind_nnz) > 0.7 * g.size:
# b += p.matmult(vec1, vec2)
# else:
# ind_nnz1 = nonzero(vec1)[0]
# ind_nnz2 = nonzero(vec2)[1]
# r = dot(vec1[ind_nnz1, :], vec2[:, ind_nnz2])
# if p.debug:
# assert abs(norm(p.matmult(vec1, vec2).flatten()) - norm(r.flatten())) < 1e-5
# b[ix_(ind_nnz1, ind_nnz2)] += r
return b
def linEqProjection(self, x, Aeq, beq):
# TODO: handle case nbeq = 1 ?
if hasattr(Aeq, 'toarray'):Aeq = Aeq.toarray()
AeqT = Aeq.T
AeqAeqT = dot(Aeq, AeqT)
Aeqx = dot(Aeq, x)
AeqT_AeqAeqT_inv_Aeqx = dot(AeqT, ravel(solve(AeqAeqT, Aeqx)))
AeqT_AeqAeqT_inv_beq = dot(AeqT, ravel(solve(AeqAeqT, beq)))
xf = x - AeqT_AeqAeqT_inv_Aeqx + AeqT_AeqAeqT_inv_beq
return xf
|