def getQP(n=10, beta=1.0e-5, phi=0):
'Generate the QP data structure'
prob = dict()
prob['H'] = getH(n, beta)
prob['c'] = getc(n)
prob['Aeq'], prob['beq'] = geteq(n)
prob['l'], prob['u'] = getlu(n, phi)
prob['A'] = sp_rand(0, 2 * n**2, 0)
prob['bl'] = sp_rand(0, 1, 0)
prob['bu'] = sp_rand(0, 1, 0)
#prob['x0'] = sp_rand(2*n**2,1,0.5)
prob['x0'] = matrix(1.0, (2 * n**2, 1))
return QP(prob)
def __init__(self, prob=QP(), Free=[], **kwargs):
'Initialize the PDASc'
# Initialize the superclass
super(PDASc, self).__init__(prob, **kwargs)
# Add additional data
self.F = Free
nf = len(self.F)
m = self.QP.numeq
Q1 = sparse([self.QP.H[self.F, self.F], self.QP.Aeq[:, self.F]])
Q2 = sparse([self.QP.Aeq[:, self.F].T, spmatrix([], [], [], (m, m))])
self.ipiv = matrix(1, (nf + m, 1))
self.Q = matrix([[Q1], [Q2]])
lapack.sytrf(self.Q, self.ipiv)
# LDL factorization of Q, dense, option 1
# lapack.sytrf(self.Q,self.ipiv)
# self.DiagQi = spdiag([self.Q[i,i] for i in range(nf+m)])
# self.LQ = copy(self.Q)
# for i in range(nf+m):
# self.LQ[i,i] = 1
# for j in range(i+1,nf+m):
# self.LQ[i,j] = 0
# LDL factorization of Q, dense, option 2 by calling Matlab library
# mlab = Matlab()
# mlab.start()
filename = 'temp/' + ''.join([
random.choice(string.ascii_letters + string.digits)
for n in xrange(32)
])
s = dict()
s['A'] = sparse([[Q1], [Q2]])
write(filename + '.mat', s)
d = '/home/zhh210/workspace/pypdas/numeric/control/'
# Memory failure when size is large
# output = mlab.run_func('getldp.m',{'arg1':filename})
# self.LQ = matrix(output['result']['L'])
# self.Dinv = matrix(output['result']['Dinv'])
# self.P = matrix(output['result']['P'])
# output = mlab.run_func('saveldp.m',{'arg1':filename})
# Execute shell command
cmd = 'matlab -r ' + '"' + "saveldp('" + filename + "');quit" + '"'
print cmd
os.system(cmd)
data = read(filename + '.mat')
self.LQ = matrix(data['L'])
self.Dinv = data['Dinv']
self.P = data['P']
def _generateQP(H, c, Aeq, beq, A, bl, bu, l, u, x0):
'Auxilliary function for generating QP blass'
prob = dict()
prob['H'] = H
prob['c'] = c
prob['Aeq'] = Aeq
prob['beq'] = beq
prob['A'] = A
prob['bl'] = bl
prob['bu'] = bu
prob['l'] = l
prob['u'] = u
prob['x0'] = x0
qp = QP(prob)
return qp
def test_qp():
'Function to test QP class'
prob = dict()
n = 5
m = 1
prob['H'] = sprandsym(n)
prob['c'] = sp_rand(n, 1, 0.8)
prob['A'] = sp_rand(m, n, 0.8)
prob['bl'] = sp_rand(m, 1, 1)
prob['bu'] = prob['bl'] + 1
prob['Aeq'] = sp_rand(m, n, 0.8)
prob['beq'] = sp_rand(m, 1, 1)
prob['l'] = sp_rand(n, 1, 1)
prob['u'] = prob['l'] + 1
prob['x0'] = sp_rand(n, 1, 0.8)
qp = QP(prob)
print(qp)
return qp
def __init__(self, QP=QP(), **kwargs):
#super(solver,self).__init__(self,QP,**kwargs)
self.QP = QP
n = QP.numvar
m = QP.numeq
mi = QP.numineq
self.x = copy(QP.x0)
self.czl = spmatrix([], [], [], (mi, 1))
self.czu = spmatrix([], [], [], (mi, 1))
self.y = matrix(1.0, (m, 1))
self.zl = spmatrix([], [], [], (n, 1))
self.zu = spmatrix([], [], [], (n, 1))
#self.zl = spmatrix([0],[max(0,n-1)],[0])
#self.zu = spmatrix([0],[max(0,n-1)],[0])
self.iter = 0
self.cAL = []
self.cAU = []
self.cI = []
self.AL = []
self.AU = []
self.I = []
self.violations = Violations()
self.option = ut.optimset(**kwargs)
self._guess()
self._ObserverList = dict(PDAS.observerlist)
self.state = 'Initial'
self._CGiter = 0
self.TotalCG = 0
self.CG_r = matrix([10, 100, 1000])
self.correctV = Violations()
if 'inv_norm' in self.option.options.keys():
self.inv_norm = self.option['inv_norm']
else:
self.inv_norm = 10
def __init__(self, prob=QP(), **kwargs):
# Initialize the solver
self.prob = prob
super().__init__()
pass