def ipPD(func, grad, hessian=None, x0=None,
lb=None, ub=None,
G=None, h=None,
A=None, b=None,
maxiter=100,
disp=0, full_output=False):
z, G, h, y, A, b = _setup(lb, ub, G, h, A, b)
x = _checkInitialValue(x0, G, h, A, b)
p = len(x)
if hessian is None:
approxH = BFGS
elif type(hessian) is str:
if hessian.lower()=='bfgs':
approxH = BFGS
elif hessian.lower()=='sr1':
approxH = SR1
elif hessian.lower()=='dfp':
approxH = DFP
else:
raise Exception("Input name of hessian is not recognizable")
if grad is None:
def finiteForward(func,p):
def finiteForward1(x):
return forward(func,x.ravel())
return finiteForward1
grad = finiteForward(func,p)
g = numpy.zeros((p,1))
gOrig = g.copy()
oldFx = numpy.inf
oldGrad = None
deltaX = None
deltaY = 0
deltaZ = 0
H = numpy.zeros((p,p))
Haug = H.copy()
fx = func(x)
dispObj = Disp(disp)
i = 0
mu = 5.0
step = 1.0
t = 1.0
# because we determine the size of the back tracking
# step on the fly, we don't give it a maximum. At the
# same time, because we are only evaluating the residuals
# of the KKT system, there are times where we may want to
# give the descent a nudge
#step0 = 1.0 # back tracking search step maximum value
if G is not None:
s = h - G.dot(x)
z = 1.0/s
#z = numpy.ones(s.shape)
# print G.dot(x)
# print s
# print z
m = G.shape[0]
eta = _surrogateGap(x, z, G, h, y, A, b)
# print eta
t = mu * m / eta
# print t
while maxiter>i:
gOrig[:] = grad(x).reshape(p,1)
g[:] = gOrig.copy()
if hessian is None:
if oldGrad is None:
H = numpy.eye(len(x))
else:
diffG = (gOrig - oldGrad).ravel()
H = approxH(H, diffG, step * deltaX.ravel())
else:
H = hessian(x)
Haug[:] = H.copy()
x, y, z, fx, step, oldFx, oldGrad, deltaX = _solveKKTAndUpdatePD(x, func, grad,
fx,
g, gOrig,
Haug,
z, G, h,
y, A, b, t)
# ## standard log barrier, \nabla f(x) / -f(x)
# if G is not None:
# s = h - G.dot(x)
# Gs = G/s
# zs = z/s
# # now find the matrix/vector of our qp
# Haug += numpy.einsum('ji,ik->jk',G.T, G*zs)
# Dphi = Gs.sum(axis=0).reshape(p,1)
# g += Dphi / t
# ## solving the QP to get the descent direction
# if A is not None:
# bTemp = _rPriFunc(x, A, b)
# g += A.T.dot(y)
# #print "here"
# LHS = scipy.sparse.bmat([[Haug,A.T],[A,None]],'csc')
# RHS = numpy.append(g,bTemp,axis=0)
# # print LHS
# # print RHS
# # if the total number of elements (in sparse format) is
# # more than half total possible elements, it is a dense matrix
# if LHS.size>= (LHS.shape[0] * LHS.shape[1])/2:
# deltaTemp = scipy.linalg.solve(LHS.todense(),-RHS).reshape(len(RHS),1)
# else:
# deltaTemp = scipy.linalg.solve(LHS.todense(),-RHS).reshape(len(RHS),1)
# deltaX = deltaTemp[:p]
# deltaY = deltaTemp[p::]
# else:
# deltaX = scipy.linalg.solve(Haug,-g).reshape(p,1)
# # store the information for the next iteration
# oldFx = fx
# oldGrad = gOrig.copy()
# if G is None:
# maxStep = 1
# barrierFunc = _logBarrier(x, func, t, G, h)
# lineFunc = lineSearch(maxStep, x, deltaX, barrierFunc)
# searchScale = deltaX.ravel().dot(g.ravel())
# else:
# maxStep = _maxStepSize(z, x, deltaX, t, G, h)
# lineFunc = residualLineSearch(maxStep,
# x, deltaX,
# grad, t,
# z, _deltaZFunc, G, h,
# y, deltaY, A, b)
# searchScale = -lineFunc(0.0)
# # perform a line search. Because the minimization routine
# # in scipy can sometimes be a bit weird, we assume that the
# # exact line search can sometimes fail, so we do a
# # back tracking line search if that is the case
# step, fx = exactLineSearch(maxStep, lineFunc)
# if fx >= oldFx or step <=0:
# step, fx = backTrackingLineSearch(maxStep, lineFunc, searchScale)
# # found one iteration, now update the information
# if z is not None:
# z += step * _deltaZFunc(x, deltaX, t, z, G, h)
# if y is not None:
# y += step * deltaY
# print "deltaX"
# print deltaX
# print "deltaZ"
# print _deltaZFunc(x, deltaX, t, z, G, h)
# x += step * deltaX
i += 1
dispObj.d(i, x , fx, deltaX.ravel(), g.ravel(), step)
feasible = False
if G is not None:
feasible = True
eta = _surrogateGap(x, z, G, h, y, A, b)
if eta >= EPSILON:
feasible = False
if G is not None:
r = _rDualFunc(x, grad, z, G, y, A)
if scipy.linalg.norm(r) >= EPSILON:
feasible = False
if A is not None:
r = _rPriFunc(x, A, b)
if scipy.linalg.norm(r) >= EPSILON:
feasible = False
t = mu * m / eta
else:
if abs(fx-oldFx)<=EPSILON:
break
if feasible:
break
# TODO: full_output- dual variables
if full_output:
output = dict()
output['t'] = t
if G is not None:
gap = _surrogateGap(x, z, G, h, y, A, b)
else:
gap = 0
output['dgap'] = gap
output['fx'] = func(x)
output['H'] = H
output['g'] = gOrig.ravel()
if G is not None:
output['s'] = s.ravel()
output['z'] = z.ravel()
output['rDual'] = _rDualFunc(x, grad, z, G, y, A).ravel()
if A is not None:
output['rPri'] = _rPriFunc(x, A, b).ravel()
output['y'] = y.ravel()
return x.ravel(), output
else:
return x.ravel()
def sqp(func, grad=None, hessian=None, x0=None,
lb=None, ub=None,
G=None, h=None,
A=None, b=None,
maxiter=100,
method='trust',
disp=0, full_output=False):
if method.lower()=='trust' or method.lower()=='line':
pass
else:
raise Exception("Input method not recognized")
z, G, h, y, A, b = _setup(lb, ub, G, h, A, b)
x = _checkInitialValue(x0, G, h, A, b)
p = len(x)
if hessian is None:
approxH = BFGS
if grad is None:
def finiteForward(x,func,p):
def finiteForward1(x):
return forward(func,x.ravel())
return finiteForward1
grad = finiteForward(x,func,p)
g = numpy.zeros((p,1))
H = numpy.zeros((p,p))
oldFx = numpy.inf
oldOldFx = numpy.inf
oldGrad = None
update = True
deltaX = numpy.zeros((p,1))
fx = func(x)
dispObj = Disp(disp)
i = 0
innerI = 0
step = 1.0
radius = 1.0
if hessian is None:
H = numpy.eye(len(x))
while maxiter>=i:
g[:] = grad(x.ravel()).reshape(p,1)
if hessian is None:
if oldGrad is not None:
if update: # update is always true for line search
diffG = (g - oldGrad).ravel()
H = approxH(H, diffG, step * deltaX.ravel())
else:
H = hessian(x.ravel())
if method=='trust':
if hessian is None:
# we assume that the hessian is always a PSD
x, update, radius, deltaX, z, y, fx, oldFx, oldGrad, innerIter = _updateTrustRegion(x, fx, oldFx, deltaX, p, radius, g, oldGrad, H, func, grad, z, G, h, y, A, b)
else:
x, update, radius, deltaX, z, y, fx, oldFx, oldGrad, innerIter = _updateTrustRegionSOCP(x, fx, oldFx, deltaX, p, radius, g, oldGrad, H, func, grad, z, G, h, y, A, b)
else:
x, deltaX, z, y, fx, oldFx, oldOldFx, oldGrad, step, innerIter = _updateLineSearch(x, fx, oldFx, oldOldFx, deltaX, g, H, func, grad, z, G, h, y, A, b)
innerI += innerIter
# print qpOut
# print "b Temp"
# print bTemp
# print "b"
# print b - A.dot(x)
i += 1
dispObj.d(i, x , fx, deltaX.ravel(), g.ravel(), radius)
# print "s"
# print h - G.dot(x)
# print "z"
# print numpy.array(qpOut['z']).ravel()
if sufficientNewtonDecrement(deltaX.ravel(),g.ravel()):
break
if abs(fx-oldFx)<=EPSILON:
break
# TODO: full_output- dual variables
if full_output:
output = dict()
output['H'] = H
output['g'] = g.flatten()
output['fx'] = fx
output['iter'] = i
output['innerIter'] = innerI
if G is not None:
output['z'] = z.flatten()
output['s'] = (h - G.dot(x)).flatten()
if A is not None:
output['y'] = y.flatten()
return x, output
else:
return x
def sqp(func,
grad=None,
hessian=None,
x0=None,
lb=None,
ub=None,
G=None,
h=None,
A=None,
b=None,
maxiter=100,
method='trust',
disp=0,
full_output=False):
if method.lower() == 'trust' or method.lower() == 'line':
pass
else:
raise Exception("Input method not recognized")
z, G, h, y, A, b = _setup(lb, ub, G, h, A, b)
x = _checkInitialValue(x0, G, h, A, b)
p = len(x)
if hessian is None:
approxH = BFGS
if grad is None:
def finiteForward(x, func, p):
def finiteForward1(x):
return forward(func, x.ravel())
return finiteForward1
grad = finiteForward(x, func, p)
g = numpy.zeros((p, 1))
H = numpy.zeros((p, p))
oldFx = numpy.inf
oldOldFx = numpy.inf
oldGrad = None
update = True
deltaX = numpy.zeros((p, 1))
fx = func(x)
dispObj = Disp(disp)
i = 0
innerI = 0
step = 1.0
radius = 1.0
if hessian is None:
H = numpy.eye(len(x))
while maxiter >= i:
g[:] = grad(x.ravel()).reshape(p, 1)
if hessian is None:
if oldGrad is not None:
if update: # update is always true for line search
diffG = (g - oldGrad).ravel()
H = approxH(H, diffG, step * deltaX.ravel())
else:
H = hessian(x.ravel())
if method == 'trust':
if hessian is None:
# we assume that the hessian is always a PSD
x, update, radius, deltaX, z, y, fx, oldFx, oldGrad, innerIter = _updateTrustRegion(
x, fx, oldFx, deltaX, p, radius, g, oldGrad, H, func, grad,
z, G, h, y, A, b)
else:
x, update, radius, deltaX, z, y, fx, oldFx, oldGrad, innerIter = _updateTrustRegionSOCP(
x, fx, oldFx, deltaX, p, radius, g, oldGrad, H, func, grad,
z, G, h, y, A, b)
else:
x, deltaX, z, y, fx, oldFx, oldOldFx, oldGrad, step, innerIter = _updateLineSearch(
x, fx, oldFx, oldOldFx, deltaX, g, H, func, grad, z, G, h, y,
A, b)
innerI += innerIter
# print qpOut
# print "b Temp"
# print bTemp
# print "b"
# print b - A.dot(x)
i += 1
dispObj.d(i, x, fx, deltaX.ravel(), g.ravel(), radius)
# print "s"
# print h - G.dot(x)
# print "z"
# print numpy.array(qpOut['z']).ravel()
if sufficientNewtonDecrement(deltaX.ravel(), g.ravel()):
break
if abs(fx - oldFx) <= EPSILON:
break
# TODO: full_output- dual variables
if full_output:
output = dict()
output['H'] = H
output['g'] = g.flatten()
output['fx'] = fx
output['iter'] = i
output['innerIter'] = innerI
if G is not None:
output['z'] = z.flatten()
output['s'] = (h - G.dot(x)).flatten()
if A is not None:
output['y'] = y.flatten()
return x, output
else:
return x
def ipPDandPDC(func,
grad,
hessian=None,
x0=None,
lb=None,
ub=None,
G=None,
h=None,
A=None,
b=None,
maxiter=100,
method="pd",
disp=0,
full_output=False):
z, G, h, y, A, b = _setup(lb, ub, G, h, A, b)
x = _checkInitialValue(x0, G, h, A, b)
p = len(x)
if hessian is None:
approxH = BFGS
elif type(hessian) is str:
if hessian.lower() == 'bfgs':
approxH = BFGS
elif hessian.lower() == 'sr1':
approxH = SR1
elif hessian.lower() == 'dfp':
approxH = DFP
else:
raise Exception("Input name of hessian is not recognizable")
hessian = None
if grad is None:
def finiteForward(func, p):
def finiteForward1(x):
return forward(func, x.ravel())
return finiteForward1
grad = finiteForward(func, p)
if method.lower() == 'pd' or method.lower() == 'pdc':
updateFunc = _solveKKTAndUpdatePD
else:
raise Exception("interior point update method not recognized")
g = numpy.zeros((p, 1))
gOrig = g.copy()
oldOldFx = numpy.inf
oldFx = numpy.inf
oldGrad = None
deltaX = None
deltaY = 0
deltaZ = 0
H = numpy.zeros((p, p))
Haug = H.copy()
fx = func(x)
dispObj = Disp(disp)
i = 0
mu = 1.0
step = 1.0
t = 1.0
# because we determine the size of the back tracking
# step on the fly, we don't give it a maximum. At the
# same time, because we are only evaluating the residuals
# of the KKT system, there are times where we may want to
# give the descent a nudge
#step0 = 1.0 # back tracking search step maximum value
if G is not None:
s = h - G.dot(x)
z = 1.0 / s
m = G.shape[0]
eta = _surrogateGap(x, z, G, h, y, A, b)
t = mu * m / eta
while maxiter > i:
gOrig[:] = grad(x).reshape(p, 1)
g[:] = gOrig.copy()
if hessian is None:
if oldGrad is None:
H = numpy.eye(len(x))
else:
diffG = (gOrig - oldGrad).ravel()
H = approxH(H, diffG, step * deltaX.ravel())
else:
H = hessian(x)
Haug[:] = H.copy()
oldOldFxTemp = oldFx
x, y, z, fx, step, oldFx, oldGrad, deltaX = updateFunc(
x, func, grad, fx, oldFx, oldOldFx, g, gOrig, Haug, z, G, h, y, A,
b, t, method)
oldOldFx = oldOldFxTemp
i += 1
dispObj.d(i, x, fx, deltaX.ravel(), g.ravel(), step)
feasible = False
if G is not None:
feasible = True
eta = _surrogateGap(x, z, G, h, y, A, b)
if eta >= EPSILON:
feasible = False
if G is not None:
r = _rDualFunc(x, grad, z, G, y, A)
if scipy.linalg.norm(r) >= EPSILON:
feasible = False
if A is not None:
r = _rPriFunc(x, A, b)
if scipy.linalg.norm(r) >= EPSILON:
feasible = False
t = mu * m / eta
else:
if abs(fx - oldFx) <= EPSILON:
break
if feasible:
break
# TODO: full_output- dual variables
if full_output:
output = dict()
output['t'] = t
if G is not None:
gap = _surrogateGap(x, z, G, h, y, A, b)
else:
gap = 0
output['dgap'] = gap
output['fx'] = func(x)
output['H'] = H
output['g'] = gOrig.ravel()
if G is not None:
output['s'] = s.ravel()
output['z'] = z.ravel()
output['rDual'] = _rDualFunc(x, grad, z, G, y, A).ravel()
if A is not None:
output['rPri'] = _rPriFunc(x, A, b).ravel()
output['y'] = y.ravel()
return x.ravel(), output
else:
return x.ravel()
def ipBar(func,
grad,
hessian=None,
x0=None,
lb=None,
ub=None,
G=None,
h=None,
A=None,
b=None,
maxiter=100,
disp=0,
full_output=False):
z, G, h, y, A, b = _setup(lb, ub, G, h, A, b)
x = _checkInitialValue(x0, G, h, A, b)
p = len(x)
if hessian is None:
approxH = BFGS
elif type(hessian) is str:
if hessian.lower() == 'bfgs':
approxH = BFGS
elif hessian.lower() == 'sr1':
approxH = SR1
elif hessian.lower() == 'dfp':
approxH = DFP
else:
raise Exception("Input name of hessian is not recognizable")
hessian = None
if grad is None:
def finiteForward(x, func, p):
def finiteForward1(x):
return forward(func, x.ravel())
return finiteForward1
grad = finiteForward(x, func, p)
if G is not None:
m = G.shape[0]
else:
m = 1
fx = None
oldFx = None
oldOldFx = None
oldGrad = None
deltaX = numpy.zeros((p, 1))
g = numpy.zeros((p, 1))
H = numpy.zeros((p, p))
Haug = numpy.zeros((p, p))
dispObj = Disp(disp)
i = 0
t = 0.01
mu = 20.0
step0 = 1.0 # back tracking search step maximum value
step = 0.0
j = 0
while maxiter >= j:
oldFx = numpy.inf
# define the barrier function given t. Note that
# t is adjusted at each outer iteration
barrierFunc = _logBarrier(func, t, G, h)
if j == 0:
fx = barrierFunc(x)
#print "barrier = " +str(fx)
update = True
#while (abs(fx-oldFx)/fx)>=rtol and abs(fx-oldFx)>=atol:
# for i in range(1):
while update:
# print abs(fx-oldFx)
# print abs(fx-oldFx)/fx
# print fx
# print oldFx
gOrig = grad(x.ravel()).reshape(p, 1)
if hessian is None:
if oldGrad is None:
H = numpy.eye(len(x))
else:
diffG = numpy.array(gOrig - oldGrad).ravel()
H = approxH(H, diffG, step * deltaX.ravel())
else:
H = hessian(x.ravel())
## standard log barrier
if G is not None:
s = h - G.dot(x)
Gs = G / s
s2 = s**2
Dphi = Gs.sum(axis=0).reshape(p, 1)
if j == 0:
t = _findInitialBarrier(gOrig, Dphi, A)
# print "initial barrier = " +str(t)
# print "fake barrier = "+str(_findInitialBarrier(gOrig,Dphi,A))
Haug = t * H + numpy.einsum('ji,ik->jk', G.T, G / s2)
g = t * gOrig + Dphi
else:
Haug = t * H
g = t * gOrig
## solving the QP to get the descent direction
if A is not None:
# re-adjust the bounds
bTemp = b - A.dot(x)
LHS = scipy.sparse.bmat([[Haug, A.T], [A, None]], 'csc')
RHS = numpy.append(g, -bTemp, axis=0)
if LHS.size >= (LHS.shape[0] * LHS.shape[1]) / 2:
deltaTemp = scipy.linalg.solve(LHS.todense(),
-RHS).reshape(len(RHS), 1)
else:
deltaTemp = scipy.sparse.linalg.spsolve(LHS, -RHS).reshape(
len(RHS), 1)
deltaX = deltaTemp[:p]
y = deltaTemp[p::]
else:
deltaX = scipy.linalg.solve(Haug, -g)
oldOldFxTemp = oldFx
oldFx = fx
oldGrad = gOrig
lineFunc = lineSearch(x, deltaX, barrierFunc)
#step, fx = exactLineSearch2(step0, lineFunc, deltaX.ravel().dot(g.ravel()), oldFx)
# step, fx = backTrackingLineSearch(step0, lineFunc, deltaX.ravel().dot(g.ravel()), alpha=0.0001,beta=0.8)
barrierGrad = _logBarrierGrad(func, gOrig, t, G, h)
step, fc, gc, fx, oldFx, new_slope = scipy.optimize.line_search(
barrierFunc, barrierGrad, x.ravel(), deltaX.ravel(), g.ravel(),
oldFx, oldOldFx)
# print "fx = " +str(fx)
# print "step= " +str(step)
# if step is not None:
# print "step = "+str(step)+ " with fx" +str(fx)+ " and barrier = " +str(barrierFunc(x + step * deltaX))
# print "s"
# print h - G.dot(x + step * deltaX)
if step is None:
# step, fx = exactLineSearch2(step0, lineFunc, deltaX.ravel().dot(g.ravel()), oldFx)
step, fx = backTrackingLineSearch(step0,
lineFunc,
deltaX.ravel().dot(
g.ravel()),
alpha=0.0001,
beta=0.8)
# print "fail wolfe = " +str(step)+ " maxStep = " +str(step0)
# print "fx = " +str(fx)
# print "step= " +str(step)
update = False
oldOldFx = oldOldFxTemp
x += step * deltaX
# print "stepped func = "+str(func(x))
j += 1
# dispObj.d(j, x.ravel() , fx, deltaX.ravel(), g.ravel(), step)
dispObj.d(j, x.ravel(), func(x.ravel()), deltaX.ravel(), g.ravel(),
step)
# end of inner iteration
i += 1
# obtain the missing Lagrangian multiplier
if G is not None:
s = h - G.dot(x)
z = 1.0 / (t * s)
if m / t < atol:
if sufficientNewtonDecrement(deltaX.ravel(), g.ravel()):
break
else:
t *= mu
# print scipy.linalg.norm(_rDualFunc(x, grad, z, G, y, A))
if scipy.linalg.norm(_rDualFunc(x, grad, z, G, y, A)) <= EPSILON:
break
# end of outer iteration
# TODO: full_output- dual variables
if full_output:
output = dict()
output['t'] = t
output['outerIter'] = i
output['innerIter'] = j
if G is not None:
s = h - G.dot(x)
z = 1.0 / (t * s)
output['s'] = s.ravel()
output['z'] = z.ravel()
if A is not None:
y = y / t
output['y'] = y.ravel()
gap = _dualityGap(func, x, z, G, h, y, A, b)
output['subopt'] = m / t
output['dgap'] = gap
output['fx'] = func(x)
output['H'] = H
output['g'] = gOrig.ravel()
output['rDual'] = _rDualFunc(x, grad, z, G, y, A)
return x.ravel(), output
else:
return x.ravel()
def ipPDC(func,
grad,
hessian=None,
x0=None,
lb=None,
ub=None,
G=None,
h=None,
A=None,
b=None,
maxiter=100,
disp=0,
full_output=False):
z, G, h, y, A, b = _setup(lb, ub, G, h, A, b)
x = _checkInitialValue(x0, G, h, A, b)
p = len(x)
if hessian is None:
approxH = BFGS
elif type(hessian) is str:
if hessian.lower() == 'bfgs':
approxH = BFGS
elif hessian.lower() == 'sr1':
approxH = SR1
elif hessian.lower() == 'dfp':
approxH = DFP
else:
raise Exception("Input name of hessian is not recognizable")
if grad is None:
def finiteForward(x, func, p):
def finiteForward1(x):
return forward(func, x.ravel())
return finiteForward1
grad = finiteForward(x, func, p)
g = numpy.zeros((p, 1))
gOrig = g.copy()
oldFx = numpy.inf
oldGrad = None
deltaX = None
deltaY = 0
deltaZ = 0
H = numpy.zeros((p, p))
Haug = H.copy()
fx = func(x)
dispObj = Disp(disp)
i = 0
mu = 5.0
step = 1.0
t = 1.0
# because we determine the size of the back tracking
# step on the fly, we don't give it a maximum. At the
# same time, because we are only evaluating the residuals
# of the KKT system, there are times where we may want to
# give the descent a nudge
#step0 = 1.0 # back tracking search step maximum value
if G is not None:
s = h - G.dot(x)
z = 1.0 / s
m = G.shape[0]
eta = _surrogateGap(x, z, G, h, y, A, b)
t = mu * m / eta
while maxiter >= i:
gOrig[:] = grad(x).reshape(p, 1)
g[:] = gOrig.copy()
if hessian is None:
if oldGrad is None:
H = numpy.eye(len(x))
else:
diffG = (gOrig - oldGrad).ravel()
H = approxH(H, diffG, step * deltaX.ravel())
else:
H = hessian(x)
Haug[:] = H.copy()
x, y, z, fx, step, oldFx, oldGrad, deltaX = _solveKKTAndUpdatePDC(
x, func, grad, fx, g, gOrig, Haug, z, G, h, y, A, b, t)
# ## standard log barrier, \nabla f(x) / -f(x)
# # print h
# # print G.dot(x)
# rDual = _rDualFunc(x, grad, z, G, y, A)
# RHS = rDual
# if G is not None:
# s = h - G.dot(x)
# Gs = G/s
# zs = z/s
# # now find the matrix/vector of our qp
# #Haug += numpy.einsum('ji,ik->jk',G.T, G*zs)
# rCent = _rCentFunc(z, s, t)
# RHS = numpy.append(RHS,rCent,axis=0)
# ## solving the QP to get the descent direction
# if A is not None:
# bTemp = b - A.dot(x)
# g += A.T.dot(y)
# rPri = _rPriFunc(x, A, b)
# RHS = numpy.append(RHS,rPri,axis=0)
# if G is not None:
# LHS = scipy.sparse.bmat([
# [Haug, G.T, A.T],
# [G*-z, scipy.sparse.diags(s.ravel(),0),None],
# [A, None, None]
# ],'csc')
# if LHS.size>= (LHS.shape[0] * LHS.shape[1])/2:
# deltaTemp = scipy.sparse.linalg.spsolve(LHS,-RHS).reshape(len(RHS),1)
# else:
# deltaTemp = scipy.linalg.solve(LHS.todense(),-RHS).reshape(len(RHS),1)
# # print deltaTemp
# deltaX = deltaTemp[:p]
# deltaZ = deltaTemp[p:-len(A)]
# deltaY = deltaTemp[-len(A):]
# else:
# LHS = scipy.sparse.bmat([
# [Haug, A.T],
# [A, None]
# ],'csc')
# if LHS.size>= (LHS.shape[0] * LHS.shape[1])/2:
# deltaTemp = scipy.sparse.linalg.spsolve(LHS,-RHS).reshape(len(RHS),1)
# else:
# deltaTemp = scipy.linalg.solve(LHS.todense(),-RHS).reshape(len(RHS),1)
# deltaX = deltaTemp[:p]
# deltaY = deltaTemp[p::]
# else:
# if G is not None:
# LHS = scipy.sparse.bmat([
# [Haug, G.T],
# [G*-z, scipy.sparse.diags(s.ravel(),0)],
# ],'csc')
# deltaTemp = scipy.sparse.linalg.spsolve(LHS,-RHS).reshape(len(RHS),1)
# deltaX = deltaTemp[:p]
# deltaZ = deltaTemp[p::]
# else:
# deltaX = scipy.linalg.solve(H,-RHS).reshape(len(RHS),1)
# # store the information for the next iteration
# oldFx = fx
# oldGrad = gOrig.copy()
# if G is None:
# # print "obj"
# maxStep = 1
# barrierFunc = _logBarrier(x, func, t, G, h)
# lineFunc = lineSearch(maxStep, x, deltaX, barrierFunc)
# searchScale = deltaX.ravel().dot(g.ravel())
# else:
# maxStep = _maxStepSize(z, deltaZ, x, deltaX, G, h)
# lineFunc = residualLineSearch(step, x, deltaX,
# grad, t,
# z, deltaZ, G, h,
# y, deltaY, A, b)
# searchScale = -lineFunc(0.0)
# # perform a line search. Because the minimization routine
# # in scipy can sometimes be a bit weird, we assume that the
# # exact line search can sometimes fail, so we do a
# # back tracking line search if that is the case
# step, fx = exactLineSearch(maxStep, lineFunc)
# if fx >= oldFx or step <= 0:
# step, fx = backTrackingLineSearch(maxStep, lineFunc, searchScale, oldFx)
# if z is not None:
# z += step * deltaZ
# if y is not None:
# y += step * deltaY
# x += step * deltaX
# print "deltaX"
# print deltaX
# print "deltaZ"
# print deltaZ
i += 1
dispObj.d(i, x, fx, deltaX.ravel(), g.ravel(), step)
feasible = False
if G is not None:
feasible = True
eta = _surrogateGap(x, z, G, h, y, A, b)
if eta >= EPSILON:
feasible = False
if G is not None:
r = _rDualFunc(x, grad, z, G, y, A)
if scipy.linalg.norm(r) >= EPSILON:
feasible = False
if A is not None:
r = _rPriFunc(x, A, b)
if scipy.linalg.norm(r) >= EPSILON:
feasible = False
t = mu * m / eta
else:
if abs(fx - oldFx) <= EPSILON:
break
if feasible:
break
# TODO: full_output- dual variables
if full_output:
output = dict()
output['t'] = t
if G is not None:
gap = _surrogateGap(x, z, G, h, y, A, b)
else:
gap = 0
output['dgap'] = gap
output['fx'] = func(x)
output['H'] = H
output['g'] = gOrig.ravel()
if G is not None:
output['s'] = s.ravel()
output['z'] = z.ravel()
output['rDual'] = _rDualFunc(x, grad, z, G, y, A).ravel()
if A is not None:
output['rPri'] = _rPriFunc(x, A, b).ravel()
output['y'] = y.ravel()
return x.ravel(), output
else:
return x.ravel()
def trustRegion(func, grad, hessian=None, x0=None,
maxiter=100,
method='exact',
disp=0, full_output=False):
x = checkArrayType(x0)
p = len(x)
if grad is None:
def finiteForward(x,func,p):
def finiteForward1(x):
return forward(func,x.ravel())
return finiteForward1
grad = finiteForward(x,func,p)
if hessian is None:
approxH = BFGS
elif type(hessian) is str:
if hessian.lower()=='bfgs':
approxH = BFGS
elif hessian.lower()=='sr1':
approxH = SR1
elif hessian.lower()=='dfp':
approxH = DFP
else:
raise Exception("Input name of hessian is not recognizable")
hessian = None
if method is None:
trustMethod = trustExact
elif type(method) is str:
if method.lower()=='exact':
trustMethod = trustExact
else:
raise Exception("Input name of hessian is not recognizable")
fx = None
oldGrad = None
deltaX = None
oldFx = numpy.inf
i = 0
oldi = -1
j = 0
tau = 1.0
radius = 1.0
maxRadius = 1.0
dispObj = Disp(disp)
while maxiter>i:
# if we have successfully moved on, then
# we would need to recompute some of the quantities
if i!=oldi:
g = grad(x)
fx = func(x)
if hessian is None:
if oldGrad is None:
H = numpy.eye(len(x))
else:
diffG = numpy.array(g - oldGrad)
H = approxH(H, diffG, deltaX)
else:
H = hessian(x)
deltaX, tau = trustMethod(x, g, H, radius)
deltaX = deltaX.real
M = diffM(deltaX, g, H)
# print x
# print deltaX
newFx = func(x + deltaX)
predRatio = (fx - newFx) / M(deltaX)
if predRatio>=0.75:
if tau>0.0:
radius = min(2.0*radius, maxRadius)
elif predRatio<=0.25:
radius *= 0.25
if predRatio>=0.25:
oldGrad = g
x += deltaX
oldFx = fx
fx = newFx
i +=1
oldi = i - 1
# we only allow termination if we make a move
if (abs(fx-oldFx)/fx)<=reltol:
break
if abs(deltaX.dot(g))<=atol:
break
else:
oldi = i
dispObj.d(j, x , fx, deltaX, g, i)
j += 1
if full_output:
output = dict()
output['totalIter'] = i
output['outerIter'] = j
output['fx'] = func(x)
output['H'] = H
output['g'] = g
return x, output
else:
return x
def ipBar(func, grad, hessian=None, x0=None,
lb=None, ub=None,
G=None, h=None,
A=None, b=None,
maxiter=100,
disp=0, full_output=False):
z, G, h, y, A, b = _setup(lb, ub, G, h, A, b)
x = _checkInitialValue(x0, G, h, A, b)
p = len(x)
if hessian is None:
approxH = BFGS
elif type(hessian) is str:
if hessian.lower()=='bfgs':
approxH = BFGS
elif hessian.lower()=='sr1':
approxH = SR1
elif hessian.lower()=='dfp':
approxH = DFP
else:
raise Exception("Input name of hessian is not recognizable")
hessian = None
if grad is None:
def finiteForward(x,func,p):
def finiteForward1(x):
return forward(func,x.ravel())
return finiteForward1
grad = finiteForward(x,func,p)
if G is not None:
m = G.shape[0]
else:
m = 1
fx = None
oldFx = None
oldOldFx = None
oldGrad = None
deltaX = numpy.zeros((p,1))
g = numpy.zeros((p,1))
H = numpy.zeros((p,p))
Haug = numpy.zeros((p,p))
dispObj = Disp(disp)
i = 0
t = 0.01
mu = 20.0
step0 = 1.0 # back tracking search step maximum value
step = 0.0
j = 0
while maxiter>=j:
oldFx = numpy.inf
# define the barrier function given t. Note that
# t is adjusted at each outer iteration
barrierFunc = _logBarrier(func, t, G, h)
if j==0:
fx = barrierFunc(x)
#print "barrier = " +str(fx)
update = True
#while (abs(fx-oldFx)/fx)>=rtol and abs(fx-oldFx)>=atol:
# for i in range(1):
while update:
# print abs(fx-oldFx)
# print abs(fx-oldFx)/fx
# print fx
# print oldFx
gOrig = grad(x.ravel()).reshape(p,1)
if hessian is None:
if oldGrad is None:
H = numpy.eye(len(x))
else:
diffG = numpy.array(gOrig - oldGrad).ravel()
H = approxH(H, diffG, step * deltaX.ravel())
else:
H = hessian(x.ravel())
## standard log barrier
if G is not None:
s = h - G.dot(x)
Gs = G/s
s2 = s**2
Dphi = Gs.sum(axis=0).reshape(p,1)
if j==0:
t = _findInitialBarrier(gOrig,Dphi,A)
# print "initial barrier = " +str(t)
# print "fake barrier = "+str(_findInitialBarrier(gOrig,Dphi,A))
Haug = t*H + numpy.einsum('ji,ik->jk',G.T, G/s2)
g = t*gOrig + Dphi
else:
Haug = t*H
g = t*gOrig
## solving the QP to get the descent direction
if A is not None:
# re-adjust the bounds
bTemp = b - A.dot(x)
LHS = scipy.sparse.bmat([
[Haug,A.T],
[A,None]
], 'csc')
RHS = numpy.append(g,-bTemp,axis=0)
if LHS.size>= (LHS.shape[0] * LHS.shape[1])/2:
deltaTemp = scipy.linalg.solve(LHS.todense(),-RHS).reshape(len(RHS),1)
else:
deltaTemp = scipy.sparse.linalg.spsolve(LHS,-RHS).reshape(len(RHS),1)
deltaX = deltaTemp[:p]
y = deltaTemp[p::]
else:
deltaX = scipy.linalg.solve(Haug,-g)
oldOldFxTemp = oldFx
oldFx = fx
oldGrad = gOrig
lineFunc = lineSearch(x, deltaX, barrierFunc)
#step, fx = exactLineSearch2(step0, lineFunc, deltaX.ravel().dot(g.ravel()), oldFx)
# step, fx = backTrackingLineSearch(step0, lineFunc, deltaX.ravel().dot(g.ravel()), alpha=0.0001,beta=0.8)
barrierGrad = _logBarrierGrad(func, gOrig, t, G, h)
step, fc, gc, fx, oldFx, new_slope = scipy.optimize.line_search(barrierFunc,
barrierGrad,
x.ravel(),
deltaX.ravel(),
g.ravel(),
oldFx,
oldOldFx
)
# print "fx = " +str(fx)
# print "step= " +str(step)
# if step is not None:
# print "step = "+str(step)+ " with fx" +str(fx)+ " and barrier = " +str(barrierFunc(x + step * deltaX))
# print "s"
# print h - G.dot(x + step * deltaX)
if step is None:
# step, fx = exactLineSearch2(step0, lineFunc, deltaX.ravel().dot(g.ravel()), oldFx)
step, fx = backTrackingLineSearch(step0, lineFunc, deltaX.ravel().dot(g.ravel()), alpha=0.0001,beta=0.8)
# print "fail wolfe = " +str(step)+ " maxStep = " +str(step0)
# print "fx = " +str(fx)
# print "step= " +str(step)
update = False
oldOldFx = oldOldFxTemp
x += step * deltaX
# print "stepped func = "+str(func(x))
j += 1
# dispObj.d(j, x.ravel() , fx, deltaX.ravel(), g.ravel(), step)
dispObj.d(j, x.ravel() , func(x.ravel()), deltaX.ravel(), g.ravel(), step)
# end of inner iteration
i += 1
# obtain the missing Lagrangian multiplier
if G is not None:
s = h - G.dot(x)
z = 1.0 / (t * s)
if m/t < atol:
if sufficientNewtonDecrement(deltaX.ravel(),g.ravel()):
break
else:
t *= mu
# print scipy.linalg.norm(_rDualFunc(x, grad, z, G, y, A))
if scipy.linalg.norm(_rDualFunc(x, grad, z, G, y, A))<=EPSILON:
break
# end of outer iteration
# TODO: full_output- dual variables
if full_output:
output = dict()
output['t'] = t
output['outerIter'] = i
output['innerIter'] = j
if G is not None:
s = h - G.dot(x)
z = 1.0 / (t * s)
output['s'] = s.ravel()
output['z'] = z.ravel()
if A is not None:
y = y/t
output['y'] = y.ravel()
gap = _dualityGap(func, x,
z, G, h,
y, A, b)
output['subopt'] = m/t
output['dgap'] = gap
output['fx'] = func(x)
output['H'] = H
output['g'] = gOrig.ravel()
output['rDual'] = _rDualFunc(x, grad, z, G, y, A)
return x.ravel(), output
else:
return x.ravel()