def lp_engine(c, A, b):
# c = asarray(c)
# A = asarray(A)
# b = asarray(b)
m,n = A.shape
ind = b < 0
if any(ind):
b = abs(b)
#A[ind, :] = -A[ind, :] doesn't work for sparse
if type(A) == ndarray and not isPyPy:
A[ind] = -A[ind]
else:
for i in where(ind)[0]:
A[i,:] = -A[i,:]
d = -A.sum(axis=0)
if not isscalar(d) and type(d) != ndarray:
d = d.A.flatten()
if not isinstance(d, ndarray): d = d.A.flatten() # d may be dense matrix
w0 = sum(b)
# H = [A b;c' 0;d -w0];
#H = bmat('A b; c 0; d -w0')
''''''
H = Vstack([ # The initial _simplex table of phase one
Hstack([A, atleast_2d(b).T]), # first m rows
hstack([c, 0.]), # last-but-one
hstack([d, -asfarray(w0)])]) # last
#print sum(abs(Hstack([A, array([b]).T])))
if isspmatrix(H): H = H.tolil()
''''''
indx = arange(n)
basis = arange(n, n+m)
#H, basis, is_bounded = _simplex(H, basis, indx, 1)
is_bounded = _simplex(H, basis, indx, 1)
if H[m+1,n] < -1e-10: # last row, last column
sol = False
#print('unsolvable')
optx = []
zmin = []
is_bounded = False
else:
sol = True
j = -1
#NEW
tmp = H[m+1,:]
if type(tmp) != ndarray: tmp = tmp.A.flatten()
ind = tmp > 1e-10
if any(ind):
j = where(logical_not(ind))[0]
H = H[:, j]
indx = indx[j]
#Old
# for i in range(n):
# j = j+1
# if H[m+1,j] > 1e-10:
# H[:,j] = []
# indx[j] = []
# j = j-1
#H(m+2,:) = [] % delete last row
H = H[0:m+1,:]
if size(indx) > 0:
# Phase two
#H, basis, is_bounded = _simplex(H,basis,indx,2);
is_bounded = _simplex(H,basis,indx,2)
if is_bounded:
optx = zeros(n+m)
n1,n2 = H.shape
for i in range(m):
optx[basis[i]] = H[i,n2-1]
# optx(n+1:n+m,1) = []; % delete last m elements
optx = optx[0:n]
zmin = -H[n1-1,n2-1] # last row, last column
else:
optx = []
zmin = -Inf
else:
optx = zeros(n+m)
zmin = 0
return (optx, zmin, is_bounded, sol, basis)
def lp_engine(c, A, b):
# c = asarray(c)
# A = asarray(A)
# b = asarray(b)
m, n = A.shape
ind = b < 0
if any(ind):
b = abs(b)
#A[ind, :] = -A[ind, :] doesn't work for sparse
if type(A) == ndarray and not isPyPy:
A[ind] = -A[ind]
else:
for i in where(ind)[0]:
A[i, :] = -A[i, :]
d = -A.sum(axis=0)
if not isscalar(d) and type(d) != ndarray:
d = d.A.flatten()
if not isinstance(d, ndarray): d = d.A.flatten() # d may be dense matrix
w0 = sum(b)
# H = [A b;c' 0;d -w0];
#H = bmat('A b; c 0; d -w0')
''''''
H = Vstack([ # The initial _simplex table of phase one
Hstack([A, atleast_2d(b).T]), # first m rows
hstack([c, 0.]), # last-but-one
hstack([d, -asfarray(w0)])
]) # last
#print sum(abs(Hstack([A, array([b]).T])))
if isspmatrix(H): H = H.tolil()
''''''
indx = arange(n)
basis = arange(n, n + m)
#H, basis, is_bounded = _simplex(H, basis, indx, 1)
is_bounded = _simplex(H, basis, indx, 1)
if H[m + 1, n] < -1e-10: # last row, last column
sol = False
#print('unsolvable')
optx = []
zmin = []
is_bounded = False
else:
sol = True
j = -1
#NEW
tmp = H[m + 1, :]
if type(tmp) != ndarray: tmp = tmp.A.flatten()
ind = tmp > 1e-10
if any(ind):
j = where(logical_not(ind))[0]
H = H[:, j]
indx = indx[j]
#Old
# for i in range(n):
# j = j+1
# if H[m+1,j] > 1e-10:
# H[:,j] = []
# indx[j] = []
# j = j-1
#H(m+2,:) = [] % delete last row
H = H[0:m + 1, :]
if size(indx) > 0:
# Phase two
#H, basis, is_bounded = _simplex(H,basis,indx,2);
is_bounded = _simplex(H, basis, indx, 2)
if is_bounded:
optx = zeros(n + m)
n1, n2 = H.shape
for i in range(m):
optx[basis[i]] = H[i, n2 - 1]
# optx(n+1:n+m,1) = []; % delete last m elements
optx = optx[0:n]
zmin = -H[n1 - 1, n2 - 1] # last row, last column
else:
optx = []
zmin = -Inf
else:
optx = zeros(n + m)
zmin = 0
return (optx, zmin, is_bounded, sol, basis)
