def __solver__(self, p):
n = p.n
x0 = copy(p.x0)
xPrev = x0.copy()
xf = x0.copy()
xk = x0.copy()
p.xk = x0.copy()
f0 = p.f(x0)
fk = f0
ff = f0
p.fk = fk
df0 = p.df(x0)
#####################################################################
## #handling box-bounded problems
## if p.__isNoMoreThanBoxBounded__():
## for k in range(int(p.maxIter)):
##
## #end of handling box-bounded problems
isBB = p.__isNoMoreThanBoxBounded__()
## isBB = 0
H = diag(ones(p.n))
if not p.userProvided.c:
p.c = lambda x: array([])
p.dc = lambda x: array([]).reshape(0, p.n)
if not p.userProvided.h:
p.h = lambda x: array([])
p.dh = lambda x: array([]).reshape(0, p.n)
p.use_subproblem = 'QP'
#p.use_subproblem = 'LLSP'
for k in range(p.maxIter + 4):
if isBB:
f0 = p.f(xk)
df = p.df(xk)
direction = -df
f1 = p.f(xk + direction)
ind_l = direction <= p.lb - xk
direction[ind_l] = (p.lb - xk)[ind_l]
ind_u = direction >= p.ub - xk
direction[ind_u] = (p.ub - xk)[ind_u]
ff = p.f(xk + direction)
## print 'f0', f0, 'f1', f1, 'ff', ff
else:
mr = p.getMaxResidual(xk)
if mr > p.contol: mr_grad = p.getMaxConstrGradient(xk)
lb = p.lb - xk #- p.contol/2
ub = p.ub - xk #+ p.contol/2
c, dc, h, dh, df = p.c(xk), p.dc(xk), p.h(xk), p.dh(xk), p.df(
xk)
A, Aeq = vstack((dc, p.A)), vstack((dh, p.Aeq))
b = concatenate((-c, p.b - p.matmult(p.A, xk))) #+ p.contol/2
beq = concatenate((-h, p.beq - p.matmult(p.Aeq, xk)))
if b.size != 0:
isFinite = isfinite(b)
ind = where(isFinite)[0]
A, b = A[ind], b[ind]
if beq.size != 0:
isFinite = isfinite(beq)
ind = where(isFinite)[0]
Aeq, beq = Aeq[ind], beq[ind]
if p.use_subproblem == 'LP': #linear
linprob = LP(df, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub=ub)
linprob.iprint = -1
r2 = linprob.solve(
'cvxopt_glpk') # TODO: replace lpSolve by autoselect
if r2.istop <= 0:
p.istop = -12
p.msg = "failed to solve LP subproblem"
return
elif p.use_subproblem == 'QP': #quadratic
qp = QP(H=H,
f=df,
A=A,
Aeq=Aeq,
b=b,
beq=beq,
lb=lb,
ub=ub)
qp.iprint = -1
r2 = qp.solve(
'cvxopt_qp') # TODO: replace solver by autoselect
#r2 = qp.solve('qld') # TODO: replace solver by autoselect
if r2.istop <= 0:
for i in range(4):
if p.debug:
p.warn("iter " + str(k) + ": attempt Num " +
str(i) +
" to solve QP subproblem has failed")
#qp.f += 2*N*sum(qp.A,0)
A2 = vstack((A, Aeq, -Aeq))
b2 = concatenate(
(b, beq, -beq)) + pow(10, i) * p.contol
qp = QP(H=H, f=df, A=A2, b=b2, iprint=-5)
qp.lb = lb - pow(10, i) * p.contol
qp.ub = ub + pow(10, i) * p.contol
# I guess lb and ub don't matter here
try:
r2 = qp.solve(
'cvxopt_qp'
) # TODO: replace solver by autoselect
except:
r2.istop = -11
if r2.istop > 0: break
if r2.istop <= 0:
p.istop = -11
p.msg = "failed to solve QP subproblem"
return
elif p.use_subproblem == 'LLSP':
direction_c = getConstrDirection(p,
xk,
regularization=1e-7)
else:
p.err('incorrect or unknown subproblem')
if isBB:
X0 = xk.copy()
N = 0
result, newX = chLineSearch(p, X0, direction, N, isBB)
elif p.use_subproblem != 'LLSP':
duals = r2.duals
N = 1.05 * abs(duals).sum()
direction = r2.xf
X0 = xk.copy()
result, newX = chLineSearch(p, X0, direction, N, isBB)
else: # case LLSP
direction_f = -df
p2 = NSP(LLSsubprobF, [0.8, 0.8],
ftol=0,
gtol=0,
xtol=1e-5,
iprint=-1)
p2.args.f = (xk, direction_f, direction_c, p, 1e20)
r_subprob = p2.solve('ralg')
alpha = r_subprob.xf
newX = xk + alpha[0] * direction_f + alpha[1] * direction_c
# dw = (direction_f * direction_c).sum()
# cos_phi = dw/p.norm(direction_f)/p.norm(direction_c)
# res_0, res_1 = p.getMaxResidual(xk), p.getMaxResidual(xk+1e-1*direction_c)
# print cos_phi, res_0-res_1
# res_0 = p.getMaxResidual(xk)
# optimConstrPoint = getDirectionOptimPoint(p, p.getMaxResidual, xk, direction_c)
# res_1 = p.getMaxResidual(optimConstrPoint)
#
# maxConstrLimit = p.contol
#xk = getDirectionOptimPoint(p, p.f, optimConstrPoint, -optimConstrPoint+xk+direction_f, maxConstrLimit = maxConstrLimit)
#print 'res_0', res_0, 'res_1', res_1, 'res_2', p.getMaxResidual(xk)
#xk = getDirectionOptimPoint(p, p.f, xk, direction_f, maxConstrLimit)
#newX = xk.copy()
result = 0
# x_0 = X0.copy()
# N = j = 0
# while p.getMaxResidual(x_0) > Residual0 + 0.1*p.contol:
# j += 1
# x_0 = xk + 0.75**j * (X0-xk)
# X0 = x_0
# result, newX = 0, X0
# print 'newIterResidual = ', p.getMaxResidual(x_0)
if result != 0:
p.istop = result
p.xf = newX
return
xk = newX.copy()
fk = p.f(xk)
p.xk, p.fk = copy(xk), copy(fk)
#p._df = p.df(xk)
####################
p.iterfcn()
if p.istop:
p.xf = xk
p.ff = fk
#p._df = g FIXME: implement me
return
def __solver__(self, p):
n = p.n
x0 = copy(p.x0)
xPrev = x0.copy()
xf = x0.copy()
xk = x0.copy()
p.xk = x0.copy()
f0 = p.f(x0)
fk = f0
ff = f0
p.fk = fk
df0 = p.df(x0)
#####################################################################
## #handling box-bounded problems
## if p.__isNoMoreThanBoxBounded__():
## for k in range(int(p.maxIter)):
##
## #end of handling box-bounded problems
isBB = p.__isNoMoreThanBoxBounded__()
## isBB = 0
H = diag(ones(p.n))
if not p.userProvided.c:
p.c = lambda x : array([])
p.dc = lambda x : array([]).reshape(0, p.n)
if not p.userProvided.h:
p.h = lambda x : array([])
p.dh = lambda x : array([]).reshape(0, p.n)
p.use_subproblem = 'QP'
#p.use_subproblem = 'LLSP'
for k in range(p.maxIter+4):
if isBB:
f0 = p.f(xk)
df = p.df(xk)
direction = -df
f1 = p.f(xk+direction)
ind_l = direction<=p.lb-xk
direction[ind_l] = (p.lb-xk)[ind_l]
ind_u = direction>=p.ub-xk
direction[ind_u] = (p.ub-xk)[ind_u]
ff = p.f(xk + direction)
## print 'f0', f0, 'f1', f1, 'ff', ff
else:
mr = p.getMaxResidual(xk)
if mr > p.contol: mr_grad = p.getMaxConstrGradient(xk)
lb = p.lb - xk #- p.contol/2
ub = p.ub - xk #+ p.contol/2
c, dc, h, dh, df = p.c(xk), p.dc(xk), p.h(xk), p.dh(xk), p.df(xk)
A, Aeq = vstack((dc, p.A)), vstack((dh, p.Aeq))
b = concatenate((-c, p.b-p.matmult(p.A,xk))) #+ p.contol/2
beq = concatenate((-h, p.beq-p.matmult(p.Aeq,xk)))
if b.size != 0:
isFinite = isfinite(b)
ind = where(isFinite)[0]
A, b = A[ind], b[ind]
if beq.size != 0:
isFinite = isfinite(beq)
ind = where(isFinite)[0]
Aeq, beq = Aeq[ind], beq[ind]
if p.use_subproblem == 'LP': #linear
linprob = LP(df, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub=ub)
linprob.iprint = -1
r2 = linprob.solve('cvxopt_glpk') # TODO: replace lpSolve by autoselect
if r2.istop <= 0:
p.istop = -12
p.msg = "failed to solve LP subproblem"
return
elif p.use_subproblem == 'QP': #quadratic
qp = QP(H=H,f=df, A=A, Aeq=Aeq, b=b, beq=beq, lb=lb, ub = ub)
qp.iprint = -1
r2 = qp.solve('cvxopt_qp') # TODO: replace solver by autoselect
#r2 = qp.solve('qld') # TODO: replace solver by autoselect
if r2.istop <= 0:
for i in range(4):
if p.debug: p.warn("iter " + str(k) + ": attempt Num " + str(i) + " to solve QP subproblem has failed")
#qp.f += 2*N*sum(qp.A,0)
A2 = vstack((A, Aeq, -Aeq))
b2 = concatenate((b, beq, -beq)) + pow(10,i)*p.contol
qp = QP(H=H,f=df, A=A2, b=b2, iprint = -5)
qp.lb = lb - pow(10,i)*p.contol
qp.ub = ub + pow(10,i)*p.contol
# I guess lb and ub don't matter here
try:
r2 = qp.solve('cvxopt_qp') # TODO: replace solver by autoselect
except:
r2.istop = -11
if r2.istop > 0: break
if r2.istop <= 0:
p.istop = -11
p.msg = "failed to solve QP subproblem"
return
elif p.use_subproblem == 'LLSP':
direction_c = getConstrDirection(p, xk, regularization = 1e-7)
else: p.err('incorrect or unknown subproblem')
if isBB:
X0 = xk.copy()
N = 0
result, newX = chLineSearch(p, X0, direction, N, isBB)
elif p.use_subproblem != 'LLSP':
duals = r2.duals
N = 1.05*abs(duals).sum()
direction = r2.xf
X0 = xk.copy()
result, newX = chLineSearch(p, X0, direction, N, isBB)
else: # case LLSP
direction_f = -df
p2 = NSP(LLSsubprobF, [0.8, 0.8], ftol=0, gtol=0, xtol = 1e-5, iprint = -1)
p2.args.f = (xk, direction_f, direction_c, p, 1e20)
r_subprob = p2.solve('ralg')
alpha = r_subprob.xf
newX = xk + alpha[0]*direction_f + alpha[1]*direction_c
# dw = (direction_f * direction_c).sum()
# cos_phi = dw/p.norm(direction_f)/p.norm(direction_c)
# res_0, res_1 = p.getMaxResidual(xk), p.getMaxResidual(xk+1e-1*direction_c)
# print cos_phi, res_0-res_1
# res_0 = p.getMaxResidual(xk)
# optimConstrPoint = getDirectionOptimPoint(p, p.getMaxResidual, xk, direction_c)
# res_1 = p.getMaxResidual(optimConstrPoint)
#
# maxConstrLimit = p.contol
#xk = getDirectionOptimPoint(p, p.f, optimConstrPoint, -optimConstrPoint+xk+direction_f, maxConstrLimit = maxConstrLimit)
#print 'res_0', res_0, 'res_1', res_1, 'res_2', p.getMaxResidual(xk)
#xk = getDirectionOptimPoint(p, p.f, xk, direction_f, maxConstrLimit)
#newX = xk.copy()
result = 0
# x_0 = X0.copy()
# N = j = 0
# while p.getMaxResidual(x_0) > Residual0 + 0.1*p.contol:
# j += 1
# x_0 = xk + 0.75**j * (X0-xk)
# X0 = x_0
# result, newX = 0, X0
# print 'newIterResidual = ', p.getMaxResidual(x_0)
if result != 0:
p.istop = result
p.xf = newX
return
xk = newX.copy()
fk = p.f(xk)
p.xk, p.fk = copy(xk), copy(fk)
#p._df = p.df(xk)
####################
p.iterfcn()
if p.istop:
p.xf = xk
p.ff = fk
#p._df = g FIXME: implement me
return
