/usr/share/pyshared/openopt/solvers/UkrOpt/interalg_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 | import numpy
from numpy import isfinite, all, argmax, where, delete, array, asarray, inf, argmin, hstack, vstack, arange, amin, \
logical_and, float64, ceil, amax, inf, ndarray, isinf, any, logical_or, nan, logical_not, asanyarray, searchsorted, \
logical_xor, empty
from numpy.linalg import norm, solve, LinAlgError
from openopt.kernel.setDefaultIterFuncs import SMALL_DELTA_X, SMALL_DELTA_F, MAX_NON_SUCCESS, IS_NAN_IN_X
from openopt.kernel.baseSolver import *
from openopt.kernel.Point import Point
from openopt.solvers.UkrOpt.interalgMisc import *
from FuncDesigner import sum as fd_sum, abs as fd_abs, max as fd_max, oopoint
from ii_engine import *
from interalgCons import processConstraints, processConstraints2
from interalgODE import interalg_ODE_routine
from interalgMOP import r14MOP
from interalgLLR import adjustr4WithDiscreteVariables
bottleneck_is_present = False
try:
from bottleneck import nanargmin, nanargmax, nanmin
bottleneck_is_present = True
except ImportError:
from numpy import nanmin, nanargmin, nanargmax
class interalg(baseSolver):
__name__ = 'interalg'
__license__ = "BSD"
__authors__ = "Dmitrey"
__alg__ = ""
__optionalDataThatCanBeHandled__ = ['lb', 'ub', 'c', 'h', 'A', 'Aeq', 'b', 'beq', 'discreteVars']
iterfcnConnected = True
fStart = None
dataType = float64
#maxMem = '150MB'
maxNodes = 150000
maxActiveNodes = 150
sigma = 0.1 # for MOP, unestablished
_requiresBestPointDetection = True
__isIterPointAlwaysFeasible__ = lambda self, p: \
p.__isNoMoreThanBoxBounded__() or p.probType in ('MOP', 'IP') #and p.probType != 'IP'
_requiresFiniteBoxBounds = True
def __init__(self):
self.dataHandling = 'auto'
self.intervalObtaining = 'auto'
def __solver__(self, p):
isMOP = p.probType == 'MOP'
isOpt = p.probType in ['NLP', 'NSP', 'GLP', 'MINLP']
isODE = p.probType == 'ODE'
isSNLE = p.probType in ('NLSP', 'SNLE')
if self.intervalObtaining == 'auto':
self.intervalObtaining=2
if not p.__isFiniteBoxBounded__() and not isODE:
p.err('''
solver %s requires finite lb, ub:
lb <= x <= ub
(you can use "implicitBoounds")
''' % self.__name__)
# if p.fixedVars is not None:
# p.err('solver %s cannot handle FuncDesigner problems with some variables declared as fixed' % self.__name__)
if p.probType in ('LP', 'MILP'):
p.err("the solver can't handle problems of type " + p.probType)
if not p.isFDmodel:
p.err('solver %s can handle only FuncDesigner problems' % self.__name__)
isIP = p.probType == 'IP'
if isIP:
pb = r14IP
p._F = asarray(0, self.dataType)
p._residual = 0.0
f_int = p.user.f[0].interval(p.domain, self.dataType)
p._r0 = prod(p.ub-p.lb) * (f_int.ub - f_int.lb)
p._volume = 0.0
p.kernelIterFuncs.pop(IS_NAN_IN_X)
elif isMOP:
pb = r14MOP
else:
pb = r14
for val in p._x0.values():
if isinstance(val, (list, tuple, ndarray)) and len(val) > 1:
p.pWarn('''
solver %s currently can handle only single-element variables,
use oovars(n) instead of oovar(size=n),
elseware correct result is not guaranteed
'''% self.__name__)
vv = list(p._freeVarsList)
x0 = dict([(v, p._x0[v]) for v in vv])
for val in x0.values():
if isinstance(val, (list, tuple, ndarray)) and len(val) > 1:
p.err('''
solver %s currently can handle only single-element variables,
use oovars(n) instead of oovar(size=n)'''% self.__name__)
point = p.point
p.kernelIterFuncs.pop(SMALL_DELTA_X, None)
p.kernelIterFuncs.pop(SMALL_DELTA_F, None)
p.kernelIterFuncs.pop(MAX_NON_SUCCESS, None)
if not bottleneck_is_present and not isODE:
p.pWarn('''
installation of Python module "bottleneck"
(http://berkeleyanalytics.com/bottleneck,
available via easy_install, takes several minutes for compilation)
could speedup the solver %s''' % self.__name__)
n = p.n
maxSolutions = p.maxSolutions
if maxSolutions == 0: maxSolutions = 10**50
if maxSolutions != 1 and p.fEnough != -inf:
p.warn('''
using the solver interalg with non-single solutions mode
is not ajusted with fEnough stop criterium yet, it will be omitted
''')
p.kernelIterFuncs.pop(FVAL_IS_ENOUGH)
nNodes = []
p.extras['nNodes'] = nNodes
nActiveNodes = []
p.extras['nActiveNodes'] = nActiveNodes
Solutions = Solution()
Solutions.maxNum = maxSolutions
Solutions.solutions = []
Solutions.coords = array([]).reshape(0, n)
p.solutions = Solutions
dataType = self.dataType
if type(dataType) == str:
if not hasattr(numpy, dataType):
p.pWarn('your architecture has no type "%s", float64 will be used instead')
dataType = 'float64'
dataType = getattr(numpy, dataType)
lb, ub = asarray(p.lb, dataType).copy(), asarray(p.ub, dataType).copy()
fTol = p.fTol
if isIP or isODE:
if p.ftol is None:
if fTol is not None:
p.ftol = fTol
else:
p.err('interalg requires user-supplied ftol (required precision)')
if fTol is None: fTol = p.ftol
elif fTol != p.ftol:
p.err('you have provided both ftol and fTol')
if fTol is None and not isMOP: # TODO: require tols for MOP
fTol = 1e-7
p.warn('solver %s require p.fTol value (required objective function tolerance); 10^-7 will be used' % self.__name__)
xRecord = 0.5 * (lb + ub)
adjustr4WithDiscreteVariables(xRecord.reshape(1, -1), p)
r40 = inf
y = lb.reshape(1, -1)
e = ub.reshape(1, -1)
r41 = inf
# TODO: maybe rework it, especially for constrained case
fStart = self.fStart
# TODO: remove it after proper SNLE handling implementation
if isSNLE:
r41 = 0.0
eqs = [fd_abs(elem) for elem in p.user.f]
asdf1 = fd_sum(eqs)
# TODO: check it, for reducing calculations
#C.update([elem == 0 for elem in p.user.f])
elif isMOP:
asdf1 = p.user.f
Solutions.F = []
if point(p.x0).isFeas(altLinInEq=False):
Solutions.solutions.append(p.x0.copy())
Solutions.coords = asarray(Solutions.solutions)
Solutions.F.append(p.f(p.x0))
p._solutions = Solutions
elif not isODE:
asdf1 = p.user.f[0]
if p.fOpt is not None: fOpt = p.fOpt
if p.goal in ('max', 'maximum'):
asdf1 = -asdf1
if p.fOpt is not None:
fOpt = -p.fOpt
if fStart is not None and fStart < r40:
r41 = fStart
for X0 in [point(xRecord), point(p.x0)]:
if X0.isFeas(altLinInEq=False) and X0.f() < r40:
r40 = X0.f()
if p.isFeas(p.x0):
tmp = asdf1(p._x0)
if tmp < r41:
r41 = tmp
if p.fOpt is not None:
if p.fOpt > r41:
p.warn('user-provided fOpt seems to be incorrect, ')
r41 = p.fOpt
if isSNLE:
if self.dataHandling == 'raw':
p.pWarn('''
this interalg data handling approach ("%s")
is unimplemented for SNLE yet, dropping to "sorted"'''%self.dataHandling)
# handles 'auto' as well
self.dataHandling ='sorted'
domain = oopoint([(v, [p.lb[i], p.ub[i]]) for i, v in enumerate(vv)], skipArrayCast=True)
domain.dictOfFixedFuncs = p.dictOfFixedFuncs
from FuncDesigner.ooFun import BooleanOOFun, SmoothFDConstraint
if self.dataHandling == 'auto':
if isIP or isODE:
self.dataHandling = 'sorted'
elif isMOP or p.hasLogicalConstraints:
self.dataHandling = 'raw'
else:
r = p.user.f[0].interval(domain, self.dataType)
M = max((max(atleast_1d(abs(r.lb))), max(atleast_1d(abs(r.ub)))))
for (c, func, lb, ub, tol) in p._FD.nonBoxCons:#[Elem[1] for Elem in p._FD.nonBoxCons]:
if isinstance(c, BooleanOOFun) and not isinstance(c, SmoothFDConstraint): continue
r = func.interval(domain, self.dataType)
M = max((M, max(atleast_1d(abs(r.lb)))))
M = max((M, max(atleast_1d(abs(r.ub)))))
self.dataHandling = 'raw' if M < 1e5 else 'sorted'
#print M
#self.dataHandling = 'sorted' if isIP or (p.__isNoMoreThanBoxBounded__() and n < 50) else 'raw'
# TODO: is it required yet?
if not isMOP and not p.hasLogicalConstraints:
p._isOnlyBoxBounded = p.__isNoMoreThanBoxBounded__()
if isODE or (asdf1.isUncycled and p._isOnlyBoxBounded and all(isfinite(p.user.f[0].interval(domain).lb))):
#maxNodes = 1
self.dataHandling = 'sorted'
if self.dataHandling == 'sorted' and p.hasLogicalConstraints:
p.warn("interalg: for general logical constraints only dataHandling='raw' mode works")
self.dataHandling = 'raw'
self.maxActiveNodes = int(self.maxActiveNodes)
# if self.maxActiveNodes < 2:
# p.warn('maxActiveNodes should be at least 2 while you have provided %d. Setting it to 2.' % self.maxActiveNodes)
self.maxNodes = int(self.maxNodes)
_in = array([], object)
g = inf
C = p._FD.nonBoxConsWithTolShift
C0 = p._FD.nonBoxCons
# if isOpt:
# r = []
# for (elem, lb, ub, tol) in C0:
# if tol == 0: tol = p.contol
# if lb == ub:
# r.append(fd_max((fd_abs(elem-lb)-tol, 0)) * (fTol/tol))
# elif lb == -inf:
# r.append(fd_max((0, elem-ub-tol)) * (fTol/tol))
# elif ub == inf:
# r.append(fd_max((0, lb-elem-tol)) * (fTol/tol))
# else:
# p.err('finite box constraints are unimplemented for interalg yet')
#p._cons_obj = 1e100 * fd_sum(r) if len(r) != 0 else None
#p._cons_obj = fd_sum(r) if len(r) != 0 else None
if isSNLE:
C += [(elem==0, elem, -(elem.tol if elem.tol != 0 else p.ftol), (elem.tol if elem.tol != 0 else p.ftol)) for elem in p.user.f]
C0 += [(elem==0, elem, 0, 0, (elem.tol if elem.tol != 0 else p.ftol)) for elem in p.user.f]
# TODO: hanlde fixed variables here
varTols = p.variableTolerances
if Solutions.maxNum != 1:
if not isSNLE:
p.err('''
"search several solutions" mode is unimplemented
for the prob type %s yet''' % p.probType)
if any(varTols == 0):
p.err('''
for the mode "search all solutions"
you have to provide all non-zero tolerances
for each variable (oovar)
''')
pnc = 0
an = []
maxNodes = self.maxNodes
_s = nan
if isODE or (isIP and p.n == 1):
interalg_ODE_routine(p, self)
return
for itn in range(p.maxIter+10):
if len(C0) != 0:
Func = processConstraints if self.intervalObtaining == 1 else processConstraints2
y, e, nlhc, residual, definiteRange, indT = Func(C0, y, e, p, dataType)
else:
nlhc, residual, definiteRange, indT = None, None, True, None
if y.size != 0:
an, g, fo, _s, Solutions, xRecord, r41, r40 = \
pb(p, nlhc, residual, definiteRange, y, e, vv, asdf1, C, r40, itn, g, \
nNodes, r41, fTol, Solutions, varTols, _in, \
dataType, maxNodes, _s, indT, xRecord)
if _s is None:
break
else:
an = _in
fo = 0.0 if isSNLE or isMOP else min((r41, r40 - (fTol if Solutions.maxNum == 1 else 0.0)))
pnc = max((len(atleast_1d(an)), pnc))
if isIP:
y, e, _in, _s = \
func12(an, self.maxActiveNodes, p, Solutions, vv, varTols, inf)
else:
y, e, _in, _s = \
func12(an, self.maxActiveNodes, p, Solutions, vv, varTols, fo)
nActiveNodes.append(y.shape[0]/2)
if y.size == 0:
if len(Solutions.coords) > 1:
p.istop, p.msg = 1001, 'all solutions have been obtained'
else:
p.istop, p.msg = 1000, 'solution has been obtained'
break
############# End of main cycle ###############
if not isSNLE and not isIP and not isMOP:
if p._bestPoint.betterThan(p.point(p.xk)):
p.iterfcn(p._bestPoint)
else:
p.iterfcn(p.xk)
ff = p.fk # ff may be not assigned yet
# ff = p._bestPoint.f()
# p.xk = p._bestPoint.x
if isIP:
p.xk = array([nan]*p.n)
p.rk = p._residual
p.fk = p._F
isFeas = len(Solutions.F) != 0 if isMOP else p.isFeas(p.xk) if not isIP else p.rk < fTol
if not isFeas and p.istop > 0:
p.istop, p.msg = -1000, 'no feasible solution has been obtained'
o = asarray([t.o for t in an])
if o.size != 0:
g = nanmin([nanmin(o), g])
if not isMOP:
p.extras['isRequiredPrecisionReached'] = \
True if ff - g < fTol and isFeas else False
# and (k is False or (isSNLE and (p._nObtainedSolutions >= maxSolutions or maxSolutions==1)))
if not isMOP and not p.extras['isRequiredPrecisionReached'] and p.istop > 0:
p.istop = -1
p.msg = 'required precision is not guarantied'
# TODO: simplify it
if not isMOP:
tmp = [nanmin(hstack((ff, g, o.flatten()))), numpy.asscalar(array((ff)))]
if p.goal in ['max', 'maximum']: tmp = (-tmp[1], -tmp[0])
p.extras['extremumBounds'] = tmp if not isIP else 'unimplemented for IP yet'
p.solutions = [p._vector2point(s) for s in Solutions.coords] if not isMOP else \
MOPsolutions([p._vector2point(s) for s in Solutions.coords])
if isMOP:
for i, s in enumerate(p.solutions):
s.useAsMutable = True
for j, goal in enumerate(p.user.f):
s[goal] = Solutions.F[i][j]
s.useAsMutable = False
p.solutions.values = asarray(Solutions.F)
p.solutions.coords = Solutions.coords
if not isMOP and p.maxSolutions == 1: delattr(p, 'solutions')
if isSNLE and p.maxSolutions != 1:
for v in p._categoricalVars:
for elem in r.solutions:
elem.useAsMutable = True
elem[v] = v.aux_domain[elem[v]]
elem.useAsMutable = False
if p.iprint >= 0 and not isMOP:
# s = 'Solution with required tolerance %0.1e \n is%s guarantied (obtained precision: %0.1e)' \
# %(fTol, '' if p.extras['isRequiredPrecisionReached'] else ' NOT', tmp[1]-tmp[0])
s = 'Solution with required tolerance %0.1e \n is%s guarantied' \
%(fTol, '' if p.extras['isRequiredPrecisionReached'] else ' NOT')
if not isIP and p.maxSolutions == 1:
s += ' (obtained precision: %0.1e)' % abs(tmp[1]-tmp[0])
if not p.extras['isRequiredPrecisionReached'] and pnc == self.maxNodes: s += '\nincrease maxNodes (current value %d)' % self.maxNodes
p.info(s)
class Solution:
pass
class MOPsolutions(list):
pass
|