def solve(self, A, F, N, b):
"""
Solve linear system ``Ax = b`` using numeric factorization ``N`` and symbolic factorization ``F``.
Store the solution in ``b``.
Parameters
----------
A
Sparse matrix
F
Symbolic factorization
N
Numeric factorization
b
RHS of the equation
Returns
-------
None
"""
if self.sparselib == 'umfpack':
umfpack.solve(A, N, b)
return b
elif self.sparselib == 'klu':
klu.solve(A, F, N, b)
return b
elif self.sparselib in ('spsolve', 'cupy'):
raise NotImplementedError
def calcInc():
global F
system.Line.gcall()
system.PQ.gcall()
system.Shunt.gcall()
system.PV.gcall()
system.SW.gcall()
system.Line.Gycall()
system.PQ.Gycall()
system.Shunt.Gycall()
system.PV.Gycall()
system.SW.Gycall()
A = sparse(system.DAE.Gy)
inc = matrix(system.DAE.g)
if system.DAE.factorize:
F = symbolic(A) #重新排列A矩阵以减少填充并执行LU分解，返回为可以传递的不透明 C object到umfpack.numeric（）。
system.DAE.factorize = False
try:
N = numeric(A, F)
solve(A, N, inc)
except:
print('unexpect')
F = symbolic(A)
solve(A, numeric(A, F), inc)
return inc
def solve(rhs):
x = cvxopt.matrix(rhs)
## If cvxopt_solver is cvxopt.umfpack
cvxopt_solver.solve(system, full_factorization, x)
## If cvxopt_solver is cvxopt.cholmod
## UPDATE: Neither of these work.
# cvxopt_solver.solve( full_factorization, x, sys = 1 )
# cvxopt_solver.spsolve( system, x, sys = 0 )
return numpy.asarray(x).reshape(rhs.shape)
def solve( rhs ):
x = cvxopt.matrix( rhs )
## If cvxopt_solver is cvxopt.umfpack
cvxopt_solver.solve( system, full_factorization, x )
## If cvxopt_solver is cvxopt.cholmod
## UPDATE: Neither of these work.
# cvxopt_solver.solve( full_factorization, x, sys = 1 )
# cvxopt_solver.spsolve( system, x, sys = 0 )
return numpy.asarray( x ).reshape( rhs.shape )
def solveNumeric(A,b, Fs):
''' given a static Fs, or symbolic factorization of the matrix A, performs the numeric part '''
aLocal = A.tocoo()
s = A.shape
AC = cvxopt.spmatrix(aLocal.data.tolist(),aLocal.row.tolist(), aLocal.col.tolist(),s)
# Fs = umfpack.symbolic(AC)
FA = umfpack.numeric(AC,Fs)
bLocal = cvxopt.matrix(copy.deepcopy(b))
umfpack.solve(AC,FA,bLocal)
bLocal = np.array(bLocal).flatten()
return bLocal
def calcInc():
global F
system.Line.gcall()
system.PQ.gcall()
system.Shunt.gcall()
system.PV.gcall()
system.SW.gcall()
system.Ind3.gcall()
system.Line.Gycall()
system.Shunt.Gycall()
system.PV.Gycall()
system.SW.Gycall()
system.Ind3.Gycall()
system.Ind3.fcall()
system.DAE.Fx = matrix(0.0, (system.DAE.nx, system.DAE.nx))
system.DAE.Fy = matrix(0.0, (system.DAE.nx, system.DAE.ny))
system.DAE.Gx = matrix(0.0, (system.DAE.ny, system.DAE.nx))
system.Ind3.Fxcall()
system.PV.Fxcall()
system.SW.Fxcall()
system.DAE.Gy = sparse(system.DAE.Gy)
system.DAE.Fx = sparse(system.DAE.Fx)
system.DAE.Fy = sparse(system.DAE.Fy)
system.DAE.Gx = sparse(system.DAE.Gx)
A = sparse([[system.DAE.Fx, system.DAE.Gx],
[system.DAE.Fy, system.DAE.Gy]])
inc = matrix([system.DAE.f, system.DAE.g])
if system.DAE.factorize:
F = symbolic(
A
) # 重新排列A矩阵以减少填充并执行LU分解，返回为可以传递的不透明 C object到umfpack.numeric（）。
system.DAE.factorize = False
try:
N = numeric(A, F)
solve(A, N, inc)
except:
print('unexpect')
F = symbolic(A)
solve(A, numeric(A, F), inc)
return inc
def calcInc():
global F
print('y')
print(system.DAE.y)
system.Line.gcall()
system.Statcom.gcall()
system.Sssc.gcall()
system.PQ.gcall()
system.Shunt.gcall()
system.PV.gcall()
system.SW.gcall()
print('g')
print(system.DAE.g)
system.Line.Gycall()
system.Statcom.Gycall()
system.Sssc.Gycall()
system.Shunt.Gycall()
system.PV.Gycall()
system.SW.Gycall()
# system.Statcom.Gycall()
print('Gy')
print(system.DAE.Gy)
# for i in range(30):
# print(system.DAE.Gy[13, i])
# for i in range(30):
# print(system.DAE.Gy[27, i])
# for i in range(30):
# print(system.DAE.Gy[28, i])
# for i in range(30):
# print(system.DAE.Gy[29, i])
A = sparse(system.DAE.Gy)
inc = matrix(system.DAE.g)
if system.DAE.factorize:
F = symbolic(
A) #重新排列A矩阵以减少填充并执行LU分解，返回为可以传递的不透明 C object到umfpack.numeric（）。
system.DAE.factorize = False
try:
N = numeric(A, F)
solve(A, N, inc)
except:
print('unexpect')
F = symbolic(A)
solve(A, numeric(A, F), inc)
return inc
def Q( b ):
bLocal = cvxopt.matrix(copy.deepcopy(b))
umfpack.solve(AC,FA,bLocal)
bLocal = np.array(bLocal).flatten()
return bLocal
A = spmatrix(V, I, J) B = matrix(1.0, (5, 1)) umfpack.linsolve(A, B) print(B) VA = [2, 3, 3, -1, 4, 4, -3, 1, 2, 2, 6, 1] VB = [4, 3, 3, -1, 4, 4, -3, 1, 2, 2, 6, 2] I = [0, 1, 0, 2, 4, 1, 2, 3, 4, 2, 1, 4] J = [0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 4, 4] A = spmatrix(VA, I, J) B = spmatrix(VB, I, J) x = matrix(1.0, (5, 1)) Fs = umfpack.symbolic(A) FA = umfpack.numeric(A, Fs) FB = umfpack.numeric(B, Fs) umfpack.solve(A, FA, x) umfpack.solve(B, FB, x) umfpack.solve(A, FA, x, trans='T') print(x) A = spmatrix([10, 3, 5, -2, 5, 2], [0, 2, 1, 3, 2, 3], [0, 0, 1, 1, 2, 3]) X = matrix(range(8), (4, 2), 'd') cholmod.linsolve(A, X) print(X) X = cholmod.splinsolve(A, spmatrix(1.0, range(4), range(4))) print(X) X = matrix(range(8), (4, 2), 'd') F = cholmod.symbolic(A) cholmod.numeric(A, F)
