def test_basic_complex(self):
import kvxopt
a = kvxopt.matrix([1, -2, 3])
b = kvxopt.matrix([1.0, -2.0, 3.0])
c = kvxopt.matrix([1.0 + 2j, 1 - 2j, 0 + 1j])
d = kvxopt.spmatrix(
[complex(1.0, 0.0),
complex(0.0, 1.0),
complex(2.0, -1.0)], [0, 1, 3], [0, 2, 3], (4, 4))
e = kvxopt.spmatrix(
[complex(1.0, 0.0),
complex(0.0, 1.0),
complex(2.0, -1.0)], [2, 3, 3], [1, 2, 3], (4, 4))
self.assertAlmostEqualLists(list(kvxopt.div(b, c)),
[0.2 - 0.4j, -0.4 - 0.8j, -3j])
self.assertAlmostEqualLists(list(kvxopt.div(b, 2.0j)),
[-0.5j, 1j, -1.5j])
self.assertAlmostEqualLists(list(kvxopt.div(a, c)),
[0.2 - 0.4j, -0.4 - 0.8j, -3j])
self.assertAlmostEqualLists(list(kvxopt.div(c, a)),
[(1 + 2j),
(-0.5 + 1j), 0.3333333333333333j])
self.assertAlmostEqualLists(list(kvxopt.div(c, c)), [1.0, 1.0, 1.0])
self.assertAlmostEqualLists(list(kvxopt.div(a, 2.0j)),
[-0.5j, 1j, -1.5j])
self.assertAlmostEqualLists(list(kvxopt.div(c, 1.0j)),
[2 - 1j, -2 - 1j, 1 + 0j])
self.assertAlmostEqualLists(list(kvxopt.div(1j, c)),
[0.4 + 0.2j, -0.4 + 0.2j, 1 + 0j])
self.assertTrue(len(d) + len(e) == len(kvxopt.sparse([d, e])))
self.assertTrue(len(d) + len(e) == len(kvxopt.sparse([[d], [e]])))
def ilp(c, G, h, A=None, b=None, I=None, taskfile=None, **kwargs):
"""
Solves the mixed integer LP
minimize c'*x
subject to G*x + s = h
A*x = b
s >= 0
xi integer, forall i in I
using MOSEK 8.
solsta, x = ilp(c, G, h, A=None, b=None, I=None, taskfile=None).
Input arguments
G is m x n, h is m x 1, A is p x n, b is p x 1. G and A must be
dense or sparse 'd' matrices. h and b are dense 'd' matrices
with one column. The default values for A and b are empty
matrices with zero rows.
I is a Python set with indices of integer elements of x. By
default all elements in x are constrained to be integer, i.e.,
the default value of I is I = set(range(n))
Dual variables are not returned for MOSEK.
Optionally, the interface can write a .task file, required for
support questions on the MOSEK solver.
Return values
solsta is a MOSEK solution status key.
If solsta is mosek.solsta.integer_optimal, then x contains
the solution.
If solsta is mosek.solsta.unknown, then x is None.
Other return values for solsta include:
mosek.solsta.near_integer_optimal
in which case the x value may not be well-defined,
c.f., section 17.48 of the MOSEK Python API manual.
x is the solution
Options are passed to MOSEK solvers via the msk.options dictionary,
e.g., the following turns off output from the MOSEK solvers
>>> msk.options = {mosek.iparam.log: 0}
see the MOSEK Python API manual.
"""
with mosek.Env() as env:
if type(c) is not matrix or c.typecode != 'd' or c.size[1] != 1:
raise TypeError("'c' must be a dense column matrix")
n = c.size[0]
if n < 1: raise ValueError("number of variables must be at least 1")
if (type(G) is not matrix and type(G) is not spmatrix) or \
G.typecode != 'd' or G.size[1] != n:
raise TypeError("'G' must be a dense or sparse 'd' matrix "\
"with %d columns" %n)
m = G.size[0]
if m == 0: raise ValueError("m cannot be 0")
if type(h) is not matrix or h.typecode != 'd' or h.size != (m, 1):
raise TypeError("'h' must be a 'd' matrix of size (%d,1)" % m)
if A is None: A = spmatrix([], [], [], (0, n), 'd')
if (type(A) is not matrix and type(A) is not spmatrix) or \
A.typecode != 'd' or A.size[1] != n:
raise TypeError("'A' must be a dense or sparse 'd' matrix "\
"with %d columns" %n)
p = A.size[0]
if b is None: b = matrix(0.0, (0, 1))
if type(b) is not matrix or b.typecode != 'd' or b.size != (p, 1):
raise TypeError("'b' must be a dense matrix of size (%d,1)" % p)
if I is None: I = set(range(n))
if type(I) is not set:
raise TypeError("invalid argument for integer index set")
for i in I:
if type(i) is not int:
raise TypeError("invalid integer index set I")
if len(I) > 0 and min(I) < 0:
raise IndexError("negative element in integer index set I")
if len(I) > 0 and max(I) > n - 1:
raise IndexError(
"maximum element in in integer index set I is larger than n-1")
bkc = m * [mosek.boundkey.up] + p * [mosek.boundkey.fx]
blc = m * [-inf] + [bi for bi in b]
buc = list(h) + list(b)
bkx = n * [mosek.boundkey.fr]
blx = n * [-inf]
bux = n * [+inf]
colptr, asub, acof = sparse([G, A]).CCS
aptrb, aptre = colptr[:-1], colptr[1:]
with env.Task(0, 0) as task:
task.set_Stream(mosek.streamtype.log, streamprinter)
# set MOSEK options
options = kwargs.get('options', globals()['options'])
for (param, val) in options.items():
if str(param)[:6] == "iparam":
task.putintparam(param, val)
elif str(param)[:6] == "dparam":
task.putdouparam(param, val)
elif str(param)[:6] == "sparam":
task.putstrparam(param, val)
else:
raise ValueError("invalid MOSEK parameter: " + str(param))
task.inputdata(
m + p, # number of constraints
n, # number of variables
list(c), # linear objective coefficients
0.0, # objective fixed value
list(aptrb),
list(aptre),
list(asub),
list(acof),
bkc,
blc,
buc,
bkx,
blx,
bux)
task.putobjsense(mosek.objsense.minimize)
# Define integer variables
if len(I) > 0:
task.putvartypelist(list(I),
len(I) * [mosek.variabletype.type_int])
task.putintparam(mosek.iparam.mio_mode, mosek.miomode.satisfied)
if taskfile:
task.writetask(taskfile)
task.optimize()
task.solutionsummary(mosek.streamtype.msg)
if len(I) > 0:
solsta = task.getsolsta(mosek.soltype.itg)
else:
solsta = task.getsolsta(mosek.soltype.bas)
x = n * [0.0]
if len(I) > 0:
task.getsolutionslice(mosek.soltype.itg, mosek.solitem.xx, 0,
n, x)
else:
task.getsolutionslice(mosek.soltype.bas, mosek.solitem.xx, 0,
n, x)
x = matrix(x)
if (solsta is mosek.solsta.unknown):
return (solsta, None)
else:
return (solsta, x)
def qp(P, q, G=None, h=None, A=None, b=None, taskfile=None, **kwargs):
"""
Solves a quadratic program
minimize (1/2)*x'*P*x + q'*x
subject to G*x <= h
A*x = b.
using MOSEK 8.
solsta, x, z, y = qp(P, q, G=None, h=None, A=None, b=None, taskfile=None)
Return values
solsta is a MOSEK solution status key.
If solsta is mosek.solsta.optimal,
then (x, y, z) contains the primal-dual solution.
If solsta is mosek.solsta.prim_infeas_cer,
then (x, y, z) is a certificate of primal infeasibility.
If solsta is mosek.solsta.dual_infeas_cer,
then (x, y, z) is a certificate of dual infeasibility.
If solsta is mosek.solsta.unknown, then (x, y, z) are all None.
Other return values for solsta include:
mosek.solsta.dual_feas
mosek.solsta.near_dual_feas
mosek.solsta.near_optimal
mosek.solsta.near_prim_and_dual_feas
mosek.solsta.near_prim_feas
mosek.solsta.prim_and_dual_feas
mosek.solsta.prim_feas
in which case the (x,y,z) value may not be well-defined.
x, z, y the primal-dual solution.
Options are passed to MOSEK solvers via the msk.options dictionary,
e.g., the following turns off output from the MOSEK solvers
>>> msk.options = {mosek.iparam.log: 0}
see the MOSEK Python API manual.
Optionally, the interface can write a .task file, required for
support questions on the MOSEK solver.
"""
with mosek.Env() as env:
if (type(P) is not matrix and type(P) is not spmatrix) or \
P.typecode != 'd' or P.size[0] != P.size[1]:
raise TypeError("'P' must be a square dense or sparse 'd' matrix ")
n = P.size[0]
if n < 1: raise ValueError("number of variables must be at least 1")
if type(q) is not matrix or q.typecode != 'd' or q.size != (n, 1):
raise TypeError("'q' must be a 'd' matrix of size (%d,1)" % n)
if G is None: G = spmatrix([], [], [], (0, n), 'd')
if (type(G) is not matrix and type(G) is not spmatrix) or \
G.typecode != 'd' or G.size[1] != n:
raise TypeError("'G' must be a dense or sparse 'd' matrix "\
"with %d columns" %n)
m = G.size[0]
if h is None: h = matrix(0.0, (0, 1))
if type(h) is not matrix or h.typecode != 'd' or h.size != (m, 1):
raise TypeError("'h' must be a 'd' matrix of size (%d,1)" % m)
if A is None: A = spmatrix([], [], [], (0, n), 'd')
if (type(A) is not matrix and type(A) is not spmatrix) or \
A.typecode != 'd' or A.size[1] != n:
raise TypeError("'A' must be a dense or sparse 'd' matrix "\
"with %d columns" %n)
p = A.size[0]
if b is None: b = matrix(0.0, (0, 1))
if type(b) is not matrix or b.typecode != 'd' or b.size != (p, 1):
raise TypeError("'b' must be a dense matrix of size (%d,1)" % p)
if m + p == 0: raise ValueError("m + p must be greater than 0")
c = list(q)
bkc = m * [mosek.boundkey.up] + p * [mosek.boundkey.fx]
blc = m * [-inf] + [bi for bi in b]
buc = list(h) + list(b)
bkx = n * [mosek.boundkey.fr]
blx = n * [-inf]
bux = n * [+inf]
colptr, asub, acof = sparse([G, A]).CCS
aptrb, aptre = colptr[:-1], colptr[1:]
with env.Task(0, 0) as task:
task.set_Stream(mosek.streamtype.log, streamprinter)
# set MOSEK options
options = kwargs.get('options', globals()['options'])
for (param, val) in options.items():
if str(param)[:6] == "iparam":
task.putintparam(param, val)
elif str(param)[:6] == "dparam":
task.putdouparam(param, val)
elif str(param)[:6] == "sparam":
task.putstrparam(param, val)
else:
raise ValueError("invalid MOSEK parameter: " + str(param))
task.inputdata(
m + p, # number of constraints
n, # number of variables
c, # linear objective coefficients
0.0, # objective fixed value
list(aptrb),
list(aptre),
list(asub),
list(acof),
bkc,
blc,
buc,
bkx,
blx,
bux)
Ps = sparse(P)
I, J = Ps.I, Ps.J
tril = [k for k in range(len(I)) if I[k] >= J[k]]
task.putqobj(list(I[tril]), list(J[tril]), list(Ps.V[tril]))
task.putobjsense(mosek.objsense.minimize)
if taskfile:
task.writetask(taskfile)
task.optimize()
task.solutionsummary(mosek.streamtype.msg)
solsta = task.getsolsta(mosek.soltype.itr)
x = n * [0.0]
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.xx, 0, n, x)
x = matrix(x)
if m != 0:
z = m * [0.0]
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.suc, 0,
m, z)
z = matrix(z)
else:
z = matrix(0.0, (0, 1))
if p != 0:
yu, yl = p * [0.0], p * [0.0]
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.suc, m,
m + p, yu)
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.slc, m,
m + p, yl)
y = matrix(yu) - matrix(yl)
else:
y = matrix(0.0, (0, 1))
if (solsta is mosek.solsta.unknown):
return (solsta, None, None, None)
else:
return (solsta, x, z, y)
def socp(c, Gl=None, hl=None, Gq=None, hq=None, taskfile=None, **kwargs):
"""
Solves a pair of primal and dual SOCPs
minimize c'*x
subject to Gl*x + sl = hl
Gq[k]*x + sq[k] = hq[k], k = 0, ..., N-1
sl >= 0,
sq[k] >= 0, k = 0, ..., N-1
maximize -hl'*zl - sum_k hq[k]'*zq[k]
subject to Gl'*zl + sum_k Gq[k]'*zq[k] + c = 0
zl >= 0, zq[k] >= 0, k = 0, ..., N-1.
using MOSEK 8.
solsta, x, zl, zq = socp(c, Gl = None, hl = None, Gq = None, hq = None, taskfile=None)
Return values
solsta is a MOSEK solution status key.
If solsta is mosek.solsta.optimal,
then (x, zl, zq) contains the primal-dual solution.
If solsta is mosek.solsta.prim_infeas_cer,
then (x, zl, zq) is a certificate of dual infeasibility.
If solsta is mosek.solsta.dual_infeas_cer,
then (x, zl, zq) is a certificate of primal infeasibility.
If solsta is mosek.solsta.unknown,
then (x, zl, zq) are all None
Other return values for solsta include:
mosek.solsta.dual_feas
mosek.solsta.near_dual_feas
mosek.solsta.near_optimal
mosek.solsta.near_prim_and_dual_feas
mosek.solsta.near_prim_feas
mosek.solsta.prim_and_dual_feas
mosek.solsta.prim_feas
in which case the (x,y,z) value may not be well-defined.
x, zl, zq the primal-dual solution.
Options are passed to MOSEK solvers via the msk.options dictionary,
e.g., the following turns off output from the MOSEK solvers
>>> msk.options = {mosek.iparam.log: 0}
see the MOSEK Python API manual.
Optionally, the interface can write a .task file, required for
support questions on the MOSEK solver.
"""
with mosek.Env() as env:
if type(c) is not matrix or c.typecode != 'd' or c.size[1] != 1:
raise TypeError("'c' must be a dense column matrix")
n = c.size[0]
if n < 1: raise ValueError("number of variables must be at least 1")
if Gl is None: Gl = spmatrix([], [], [], (0, n), tc='d')
if (type(Gl) is not matrix and type(Gl) is not spmatrix) or \
Gl.typecode != 'd' or Gl.size[1] != n:
raise TypeError("'Gl' must be a dense or sparse 'd' matrix "\
"with %d columns" %n)
ml = Gl.size[0]
if hl is None: hl = matrix(0.0, (0, 1))
if type(hl) is not matrix or hl.typecode != 'd' or \
hl.size != (ml,1):
raise TypeError("'hl' must be a dense 'd' matrix of " \
"size (%d,1)" %ml)
if Gq is None: Gq = []
if type(Gq) is not list or [
G for G in Gq
if (type(G) is not matrix and type(G) is not spmatrix)
or G.typecode != 'd' or G.size[1] != n
]:
raise TypeError("'Gq' must be a list of sparse or dense 'd' "\
"matrices with %d columns" %n)
mq = [G.size[0] for G in Gq]
a = [k for k in range(len(mq)) if mq[k] == 0]
if a: raise TypeError("the number of rows of Gq[%d] is zero" % a[0])
if hq is None: hq = []
if type(hq) is not list or len(hq) != len(mq) or [
h for h in hq
if (type(h) is not matrix and type(h) is not spmatrix)
or h.typecode != 'd'
]:
raise TypeError("'hq' must be a list of %d dense or sparse "\
"'d' matrices" %len(mq))
a = [k for k in range(len(mq)) if hq[k].size != (mq[k], 1)]
if a:
k = a[0]
raise TypeError("'hq[%d]' has size (%d,%d). Expected size "\
"is (%d,1)." %(k, hq[k].size[0], hq[k].size[1], mq[k]))
N = ml + sum(mq)
h = matrix(0.0, (N, 1))
if type(Gl) is matrix or [Gk for Gk in Gq if type(Gk) is matrix]:
G = matrix(0.0, (N, n))
else:
G = spmatrix([], [], [], (N, n), 'd')
h[:ml] = hl
G[:ml, :] = Gl
ind = ml
for k in range(len(mq)):
h[ind:ind + mq[k]] = hq[k]
G[ind:ind + mq[k], :] = Gq[k]
ind += mq[k]
bkc = n * [mosek.boundkey.fx]
blc = list(-c)
buc = list(-c)
bkx = ml * [mosek.boundkey.lo] + sum(mq) * [mosek.boundkey.fr]
blx = ml * [0.0] + sum(mq) * [-inf]
bux = N * [+inf]
c = -h
colptr, asub, acof = sparse([G.T]).CCS
aptrb, aptre = colptr[:-1], colptr[1:]
with env.Task(0, 0) as task:
task.set_Stream(mosek.streamtype.log, streamprinter)
# set MOSEK options
options = kwargs.get('options', globals()['options'])
for (param, val) in options.items():
if str(param)[:6] == "iparam":
task.putintparam(param, val)
elif str(param)[:6] == "dparam":
task.putdouparam(param, val)
elif str(param)[:6] == "sparam":
task.putstrparam(param, val)
else:
raise ValueError("invalid MOSEK parameter: " + str(param))
task.inputdata(
n, # number of constraints
N, # number of variables
list(c), # linear objective coefficients
0.0, # objective fixed value
list(aptrb),
list(aptre),
list(asub),
list(acof),
bkc,
blc,
buc,
bkx,
blx,
bux)
task.putobjsense(mosek.objsense.maximize)
for k in range(len(mq)):
task.appendcone(
mosek.conetype.quad, 0.0,
list(range(ml + sum(mq[:k]), ml + sum(mq[:k + 1]))))
if taskfile:
task.writetask(taskfile)
task.optimize()
task.solutionsummary(mosek.streamtype.msg)
solsta = task.getsolsta(mosek.soltype.itr)
xu, xl, zq = n * [0.0], n * [0.0], sum(mq) * [0.0]
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.slc, 0, n,
xl)
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.suc, 0, n,
xu)
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.xx, ml, N,
zq)
x = matrix(xu) - matrix(xl)
zq = [
matrix(zq[sum(mq[:k]):sum(mq[:k + 1])]) for k in range(len(mq))
]
if ml:
zl = ml * [0.0]
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.xx, 0,
ml, zl)
zl = matrix(zl)
else:
zl = matrix(0.0, (0, 1))
if (solsta is mosek.solsta.unknown):
return (solsta, None, None, None)
else:
return (solsta, x, zl, zq)
def lp(c, G, h, A=None, b=None, taskfile=None, **kwargs):
"""
Solves a pair of primal and dual LPs
minimize c'*x maximize -h'*z - b'*y
subject to G*x + s = h subject to G'*z + A'*y + c = 0
A*x = b z >= 0.
s >= 0
using MOSEK 8.
(solsta, x, z, y) = lp(c, G, h, A=None, b=None).
Input arguments
c is n x 1, G is m x n, h is m x 1, A is p x n, b is p x 1. G and
A must be dense or sparse 'd' matrices. c, h and b are dense 'd'
matrices with one column. The default values for A and b are
empty matrices with zero rows.
Optionally, the interface can write a .task file, required for
support questions on the MOSEK solver.
Return values
solsta is a MOSEK solution status key.
If solsta is mosek.solsta.optimal, then (x, y, z) contains the
primal-dual solution.
If solsta is mosek.solsta.prim_infeas_cer, then (x, y, z) is a
certificate of primal infeasibility.
If solsta is mosek.solsta.dual_infeas_cer, then (x, y, z) is a
certificate of dual infeasibility.
If solsta is mosek.solsta.unknown, then (x, y, z) are all None.
Other return values for solsta include:
mosek.solsta.dual_feas
mosek.solsta.near_dual_feas
mosek.solsta.near_optimal
mosek.solsta.near_prim_and_dual_feas
mosek.solsta.near_prim_feas
mosek.solsta.prim_and_dual_feas
mosek.solsta.prim_feas
in which case the (x,y,z) value may not be well-defined.
x, y, z the primal-dual solution.
Options are passed to MOSEK solvers via the msk.options dictionary.
For example, the following turns off output from the MOSEK solvers
>>> msk.options = {mosek.iparam.log: 0}
see the MOSEK Python API manual.
"""
with mosek.Env() as env:
if type(c) is not matrix or c.typecode != 'd' or c.size[1] != 1:
raise TypeError("'c' must be a dense column matrix")
n = c.size[0]
if n < 1: raise ValueError("number of variables must be at least 1")
if (type(G) is not matrix and type(G) is not spmatrix) or \
G.typecode != 'd' or G.size[1] != n:
raise TypeError("'G' must be a dense or sparse 'd' matrix "\
"with %d columns" %n)
m = G.size[0]
if m == 0: raise ValueError("m cannot be 0")
if type(h) is not matrix or h.typecode != 'd' or h.size != (m, 1):
raise TypeError("'h' must be a 'd' matrix of size (%d,1)" % m)
if A is None: A = spmatrix([], [], [], (0, n), 'd')
if (type(A) is not matrix and type(A) is not spmatrix) or \
A.typecode != 'd' or A.size[1] != n:
raise TypeError("'A' must be a dense or sparse 'd' matrix "\
"with %d columns" %n)
p = A.size[0]
if b is None: b = matrix(0.0, (0, 1))
if type(b) is not matrix or b.typecode != 'd' or b.size != (p, 1):
raise TypeError("'b' must be a dense matrix of size (%d,1)" % p)
bkc = m * [mosek.boundkey.up] + p * [mosek.boundkey.fx]
blc = m * [-inf] + [bi for bi in b]
buc = list(h) + list(b)
bkx = n * [mosek.boundkey.fr]
blx = n * [-inf]
bux = n * [+inf]
colptr, asub, acof = sparse([G, A]).CCS
aptrb, aptre = colptr[:-1], colptr[1:]
with env.Task(0, 0) as task:
task.set_Stream(mosek.streamtype.log, streamprinter)
# set MOSEK options
options = kwargs.get('options', globals()['options'])
for (param, val) in options.items():
if str(param)[:6] == "iparam":
task.putintparam(param, val)
elif str(param)[:6] == "dparam":
task.putdouparam(param, val)
elif str(param)[:6] == "sparam":
task.putstrparam(param, val)
else:
raise ValueError("invalid MOSEK parameter: " + str(param))
task.inputdata(
m + p, # number of constraints
n, # number of variables
list(c), # linear objective coefficients
0.0, # objective fixed value
list(aptrb),
list(aptre),
list(asub),
list(acof),
bkc,
blc,
buc,
bkx,
blx,
bux)
task.putobjsense(mosek.objsense.minimize)
if taskfile:
task.writetask(taskfile)
task.optimize()
task.solutionsummary(mosek.streamtype.msg)
solsta = task.getsolsta(mosek.soltype.bas)
x, z = n * [0.0], m * [0.0]
task.getsolutionslice(mosek.soltype.bas, mosek.solitem.xx, 0, n, x)
task.getsolutionslice(mosek.soltype.bas, mosek.solitem.suc, 0, m,
z)
x, z = matrix(x), matrix(z)
if p != 0:
yu, yl = p * [0.0], p * [0.0]
task.getsolutionslice(mosek.soltype.bas, mosek.solitem.suc, m,
m + p, yu)
task.getsolutionslice(mosek.soltype.bas, mosek.solitem.slc, m,
m + p, yl)
y = matrix(yu) - matrix(yl)
else:
y = matrix(0.0, (0, 1))
if (solsta is mosek.solsta.unknown):
return (solsta, None, None, None)
else:
return (solsta, x, z, y)
def conelp(c, G, h, dims=None, taskfile=None, **kwargs):
"""
Solves a pair of primal and dual SOCPs
minimize c'*x
subject to G*x + s = h
s >= 0
maximize -h'*z
subject to G'*z + c = 0
z >= 0
using MOSEK 8.
The inequalities are with respect to a cone C defined as the Cartesian
product of N + M + 1 cones:
C = C_0 x C_1 x .... x C_N x C_{N+1} x ... x C_{N+M}.
The first cone C_0 is the nonnegative orthant of dimension ml.
The next N cones are second order cones of dimension mq[0], ...,
mq[N-1]. The second order cone of dimension m is defined as
{ (u0, u1) in R x R^{m-1} | u0 >= ||u1||_2 }.
The next M cones are positive semidefinite cones of order ms[0], ...,
ms[M-1] >= 0.
The formats of G and h are identical to that used in solvers.conelp().
Input arguments.
c is a dense 'd' matrix of size (n,1).
dims is a dictionary with the dimensions of the components of C.
It has three fields.
- dims['l'] = ml, the dimension of the nonnegative orthant C_0.
(ml >= 0.)
- dims['q'] = mq = [ mq[0], mq[1], ..., mq[N-1] ], a list of N
integers with the dimensions of the second order cones C_1, ...,
C_N. (N >= 0 and mq[k] >= 1.)
- dims['s'] = ms = [ ms[0], ms[1], ..., ms[M-1] ], a list of M
integers with the orders of the semidefinite cones C_{N+1}, ...,
C_{N+M}. (M >= 0 and ms[k] >= 0.)
The default value of dims is {'l': G.size[0], 'q': [], 's': []}.
G is a dense or sparse 'd' matrix of size (K,n), where
K = ml + mq[0] + ... + mq[N-1] + ms[0]**2 + ... + ms[M-1]**2.
Each column of G describes a vector
v = ( v_0, v_1, ..., v_N, vec(v_{N+1}), ..., vec(v_{N+M}) )
in V = R^ml x R^mq[0] x ... x R^mq[N-1] x S^ms[0] x ... x S^ms[M-1]
stored as a column vector
[ v_0; v_1; ...; v_N; vec(v_{N+1}); ...; vec(v_{N+M}) ].
Here, if u is a symmetric matrix of order m, then vec(u) is the
matrix u stored in column major order as a vector of length m**2.
We use BLAS unpacked 'L' storage, i.e., the entries in vec(u)
corresponding to the strictly upper triangular entries of u are
not referenced.
h is a dense 'd' matrix of size (K,1), representing a vector in V,
in the same format as the columns of G.
A is a dense or sparse 'd' matrix of size (p,n). The default value
is a sparse 'd' matrix of size (0,n).
b is a dense 'd' matrix of size (p,1). The default value is a
dense 'd' matrix of size (0,1).
Optionally, the interface can write a .task file, required for
support questions on the MOSEK solver.
Return values
solsta is a MOSEK solution status key.
If solsta is mosek.solsta.optimal,
then (x, zl, zq, zs) contains the primal-dual solution.
If solsta is moseksolsta.prim_infeas_cer,
then (x, zl, zq, zs) is a certificate of dual infeasibility.
If solsta is moseksolsta.dual_infeas_cer,
then (x, zl, zq, zs) is a certificate of primal infeasibility.
If solsta is mosek.solsta.unknown,
then (x, zl, zq, zs) are all None
Other return values for solsta include:
mosek.solsta.dual_feas
mosek.solsta.near_dual_feas
mosek.solsta.near_optimal
mosek.solsta.near_prim_and_dual_feas
mosek.solsta.near_prim_feas
mosek.solsta.prim_and_dual_feas
mosek.solsta.prim_feas
in which case the (x,y,z) value may not be well-defined.
x, z the primal-dual solution.
Options are passed to MOSEK solvers via the msk.options dictionary,
e.g., the following turns off output from the MOSEK solvers
>>> msk.options = {mosek.iparam.log:0}
see the MOSEK Python API manual.
"""
with mosek.Env() as env:
if dims is None:
(solsta, x, y, z) = lp(c, G, h)
return (solsta, x, z, None)
N, n = G.size
ml, mq, ms = dims['l'], dims['q'], [k * k for k in dims['s']]
cdim = ml + sum(mq) + sum(ms)
if cdim == 0: raise ValueError("ml+mq+ms cannot be 0")
# Data for kth 'q' constraint are found in rows indq[k]:indq[k+1] of G.
indq = [dims['l']]
for k in dims['q']:
indq = indq + [indq[-1] + k]
# Data for the kth 's' constraint are found in rows indq[-1] + (inds[k]:inds[k+1]) of G.
inds = [0]
for k in dims['s']:
inds = inds + [inds[-1] + k * k]
if type(h) is not matrix or h.typecode != 'd' or h.size[1] != 1:
raise TypeError("'h' must be a 'd' matrix with 1 column")
if type(G) is matrix or type(G) is spmatrix:
if G.typecode != 'd' or G.size[0] != cdim:
raise TypeError("'G' must be a 'd' matrix with %d rows " %
cdim)
if h.size[0] != cdim:
raise TypeError("'h' must have %d rows" % cdim)
else:
raise TypeError("'G' must be a matrix")
if len(dims['q']) and min(dims['q']) < 1:
raise TypeError("dimensions of quadratic cones must be positive")
if len(dims['s']) and min(dims['s']) < 1:
raise TypeError(
"dimensions of semidefinite cones must be positive")
bkc = n * [mosek.boundkey.fx]
blc = list(-c)
buc = list(-c)
dimx = ml + sum(mq)
bkx = ml * [mosek.boundkey.lo] + sum(mq) * [mosek.boundkey.fr]
blx = ml * [0.0] + sum(mq) * [-inf]
bux = dimx * [+inf]
c = list(-h)
cl, cs = c[:dimx], sparse(c[dimx:])
Gl, Gs = sparse(G[:dimx, :]), sparse(G[dimx:, :])
colptr, asub, acof = Gl.T.CCS
aptrb, aptre = colptr[:-1], colptr[1:]
with env.Task(0, 0) as task:
task.set_Stream(mosek.streamtype.log, streamprinter)
# set MOSEK options
options = kwargs.get('options', globals()['options'])
for (param, val) in options.items():
if str(param)[:6] == "iparam":
task.putintparam(param, val)
elif str(param)[:6] == "dparam":
task.putdouparam(param, val)
elif str(param)[:6] == "sparam":
task.putstrparam(param, val)
else:
raise ValueError("invalid MOSEK parameter: " + str(param))
task.inputdata(
n, # number of constraints
dimx, # number of variables
cl, # linear objective coefficients
0.0, # objective fixed value
list(aptrb),
list(aptre),
list(asub),
list(acof),
bkc,
blc,
buc,
bkx,
blx,
bux)
task.putobjsense(mosek.objsense.maximize)
numbarvar = len(dims['s'])
task.appendbarvars(dims['s'])
barcsubj, barcsubk, barcsubl = (inds[-1]) * [0], (
inds[-1]) * [0], (inds[-1]) * [0]
barcval = [-h[indq[-1] + k] for k in range(inds[0], inds[-1])]
for s in range(numbarvar):
for (k, idx) in enumerate(range(inds[s], inds[s + 1])):
barcsubk[idx] = k // dims['s'][s]
barcsubl[idx] = k % dims['s'][s]
barcsubj[idx] = s
# filter out upper triangular part
trilidx = [
idx for idx in range(len(barcsubk))
if barcsubk[idx] >= barcsubl[idx]
]
barcsubj = [barcsubj[k] for k in trilidx]
barcsubk = [barcsubk[k] for k in trilidx]
barcsubl = [barcsubl[k] for k in trilidx]
barcval = [barcval[k] for k in trilidx]
task.putbarcblocktriplet(len(trilidx), barcsubj, barcsubk,
barcsubl, barcval)
Gst = Gs.T
barasubi = len(Gst) * [0]
barasubj = len(Gst) * [0]
barasubk = len(Gst) * [0]
barasubl = len(Gst) * [0]
baraval = len(Gst) * [0.0]
colptr, row, val = Gst.CCS
for s in range(numbarvar):
for j in range(ms[s]):
for idx in range(colptr[inds[s] + j],
colptr[inds[s] + j + 1]):
barasubi[idx] = row[idx]
barasubj[idx] = s
barasubk[idx] = j // dims['s'][s]
barasubl[idx] = j % dims['s'][s]
baraval[idx] = val[idx]
# filter out upper triangular part
trilidx = [
idx for (idx, (k, l)) in enumerate(zip(barasubk, barasubl))
if k >= l
]
barasubi = [barasubi[k] for k in trilidx]
barasubj = [barasubj[k] for k in trilidx]
barasubk = [barasubk[k] for k in trilidx]
barasubl = [barasubl[k] for k in trilidx]
baraval = [baraval[k] for k in trilidx]
task.putbarablocktriplet(len(trilidx), barasubi, barasubj,
barasubk, barasubl, baraval)
for k in range(len(mq)):
task.appendcone(mosek.conetype.quad, 0.0,
range(ml + sum(mq[:k]), ml + sum(mq[:k + 1])))
if taskfile:
task.writetask(taskfile)
task.optimize()
task.solutionsummary(mosek.streamtype.msg)
solsta = task.getsolsta(mosek.soltype.itr)
xu, xl, zq = n * [0.0], n * [0.0], sum(mq) * [0.0]
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.slc, 0, n,
xl)
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.suc, 0, n,
xu)
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.xx, ml,
dimx, zq)
x = matrix(xu) - matrix(xl)
zq = matrix(zq)
for s in range(numbarvar):
xx = (dims['s'][s] * (dims['s'][s] + 1) >> 1) * [0.0]
task.getbarxj(mosek.soltype.itr, s, xx)
xs = matrix(0.0, (dims['s'][s], dims['s'][s]))
idx = 0
for j in range(dims['s'][s]):
for i in range(j, dims['s'][s]):
xs[i, j] = xx[idx]
if i != j:
xs[j, i] = xx[idx]
idx += 1
zq = matrix([zq, xs[:]])
if ml:
zl = ml * [0.0]
task.getsolutionslice(mosek.soltype.itr, mosek.solitem.xx, 0,
ml, zl)
zl = matrix(zl)
else:
zl = matrix(0.0, (0, 1))
if (solsta is mosek.solsta.unknown):
return (solsta, None, None)
else:
return (solsta, x, matrix([zl, zq]))
