def optimizeconcurrentMIO(task, seeds):
n = len(seeds)
tasks = [ mosek.Task(task) for _ in range(n) ]
# Choose various seeds for cloned tasks
for i in range(n):
tasks[i].putintparam(mosek.iparam.mio_seed, seeds[i])
# Solve tasks in parallel
firstOK, res, trm = optimize(tasks)
if firstOK >= 0:
# Pick the task that ended with res = ok
# and contains an integer solution with best objective value
sense = task.getobjsense();
bestObj = 1.0e+10 if sense == mosek.objsense.minimize else -1.0e+10
bestPos = -1
for i in range(n):
print("{0} {1}".format(i,tasks[i].getprimalobj(mosek.soltype.itg)))
for i in range(n):
if ((res[i] == mosek.rescode.ok) and
(tasks[i].getsolsta(mosek.soltype.itg) == mosek.solsta.prim_feas or
tasks[i].getsolsta(mosek.soltype.itg) == mosek.solsta.integer_optimal) and
((tasks[i].getprimalobj(mosek.soltype.itg) < bestObj)
if (sense == mosek.objsense.minimize) else
(tasks[i].getprimalobj(mosek.soltype.itg) > bestObj))):
bestObj = tasks[i].getprimalobj(mosek.soltype.itg)
bestPos = i
if bestPos >= 0:
return bestPos, tasks[bestPos], trm[bestPos], res[bestPos]
return -1, None, None, None
def main(args):
# To run a continuous problem example
filename = "../data/25fv47.mps"
slvr = "intpnt"
# To run a mixed-integer example
#filename = "../data/milo1.lp"
#slvr = ""
if len(args) < 3:
print("Usage: callback ( psim | dsim | intpnt ) filename")
if len(args) > 1:
slvr = args[1]
if len(args) > 2:
filename = args[2]
with mosek.Env() as env:
with mosek.Task(env) as task:
task.readdata(filename)
if slvr == 'psim':
task.putintparam(iparam.optimizer,
optimizertype.primal_simplex)
elif slvr == "dsim":
task.putintparam(iparam.optimizer, optimizertype.dual_simplex)
elif slvr == "intpnt":
task.putintparam(iparam.optimizer, optimizertype.intpnt)
usercallback = makeUserCallback(maxtime=0.05, task=task)
task.set_InfoCallback(usercallback)
task.optimize()
def main(args):
if len(args) < 3:
print("Too few input arguments. Syntax:")
print("\tcallback.py psim inputfile")
print("\tcallback.py dsim inputfile")
print("\tcallback.py intpnt inputfile")
return
with mosek.Env() as env:
with mosek.Task(env) as task:
filename = args[2]
task.readdata(filename)
task.set_Stream(streamtype.log, msgPrinter)
if args[1] == 'psim':
task.putintparam(iparam.optimizer,
optimizertype.primal_simplex)
elif args[1] == "dsim":
task.putintparam(iparam.optimizer, optimizertype.dual_simplex)
elif args[1] == "intpnt":
task.putintparam(iparam.optimizer, optimizertype.intpnt)
# Turn all MOSEK logging off (note that errors and other messages
# are still sent through the log stream)
task.putintparam(iparam.log, 0)
usercallback = makeUserCallback(maxtime=3600)
task.set_Progress(usercallback)
task.optimize()
task.solutionsummary(streamtype.msg)
def main():
numcon = 2
numvar = 2
aval = [[-1.0], [1.0, 1.0]]
asub = [[1], [0, 1]]
ptrb = [0, 1]
ptre = [1, 3]
#int[] bsub = new int[numvar];
#double[] b = new double[numvar];
#int[] basis = new int[numvar];
with mosek.Env() as env:
with mosek.Task(env) as task:
# Directs the log task stream to the user specified
# method task_msg_obj.streamCB
task.set_Stream(mosek.streamtype.log,
lambda msg: sys.stdout.write(msg))
# Put A matrix and factor A.
# Call this function only once for a given task.
basis = [0] * numvar
b = [0.0, -2.0]
bsub = [0, 1]
put_a(task, aval, asub, ptrb, ptre, numvar, basis)
# now solve rhs
b = [1, -2]
bsub = [0, 1]
nz = task.solvewithbasis(0, 2, bsub, b)
print("\nSolution to Bx = b:\n")
# Print solution and show correspondents
# to original variables in the problem
for i in range(nz):
if basis[bsub[i]] < numcon:
print("This should never happen")
else:
print("x%d = %d" % (basis[bsub[i]] - numcon, b[bsub[i]]))
b[0] = 7
bsub[0] = 0
nz = task.solvewithbasis(0, 1, bsub, b)
print("\nSolution to Bx = b:\n")
# Print solution and show correspondents
# to original variables in the problem
for i in range(nz):
if basis[bsub[i]] < numcon:
print("This should never happen")
else:
print("x%d = %d" % (basis[bsub[i]] - numcon, b[bsub[i]]))
def optimizeconcurrent(task, optimizers):
n = len(optimizers)
tasks = [ mosek.Task(task) for _ in range(n) ]
# Choose various optimizers for cloned tasks
for i in range(n):
tasks[i].putintparam(mosek.iparam.optimizer, optimizers[i])
# Solve tasks in parallel
firstOK, res, trm = optimize(tasks)
if firstOK >= 0:
return firstOK, tasks[firstOK], trm[firstOK], res[firstOK]
else:
return -1, None, None, None
def main(argv):
n = len(argv) - 1
tasks = []
with mosek.Env() as env:
for i in range(n):
t = mosek.Task(env, 0, 0)
t.readdata(argv[i + 1])
# Each task will be single-threaded
t.putintparam(mosek.iparam.intpnt_multi_thread, mosek.onoffkey.off)
tasks.append(t)
res, trm = paropt(tasks)
for i in range(n):
print(
"Task {0} res {1} trm {2} obj_val {3} time {4}".format(
i, res[i], trm[i],
tasks[i].getdouinf(mosek.dinfitem.intpnt_primal_obj),
tasks[i].getdouinf(mosek.dinfitem.optimizer_time)))
def main(argv):
with mosek.Env() as env:
with mosek.Task(env, 0, 0) as task:
if len(argv) >= 2:
task.readdata(argv[1])
else:
task.readdata("../data/25fv47.mps")
# Optional time limit
if len(argv) >= 3:
task.putdouparam(mosek.dparam.optimizer_max_time, float(argv[2]))
if (task.getnumintvar() == 0):
# If the problem is continuous
# optimize it with three continuous optimizers.
# (Simplex will fail for non-linear problems)
optimizers = [
mosek.optimizertype.conic,
mosek.optimizertype.dual_simplex,
mosek.optimizertype.primal_simplex
]
idx, t, trm, res = optimizeconcurrent(task, optimizers)
else:
# Mixed-integer problem.
# Try various seeds.
seeds = [ 42, 13, 71749373 ]
idx, t, trm, res = optimizeconcurrentMIO(task, seeds)
# Check results and print the best answer
if idx >= 0:
print("Result from optimizer with index {0}: res {1} trm {2}".format(idx, res, trm))
t.set_Stream(mosek.streamtype.log, streamwriter)
t.optimizersummary(mosek.streamtype.log)
t.solutionsummary(mosek.streamtype.log);
else:
print("All optimizers failed.")
def __init__(self, dat, dimPCA, presolveTol=1.0e-30, outputFlag=False):
self.numPoints = dat.shape[0]
self.numFields = dat.shape[1]
self.dimPCA = dimPCA
self.dat = (np.eye(self.numPoints) - np.ones(
(self.numPoints, self.numPoints)) / self.numPoints).dot(
dat) #dat - np.ones((self.numPoints,1)).dot(sum(dat)[None,:])
for col, var in enumerate(np.var(self.dat, axis=0)):
self.dat[:, col] *= 1 / (math.sqrt(var) if var > 0 else 1)
self.normalizer = la.norm(self.dat)
self.B = np.zeros((self.numFields, self.dimPCA))
self.RunTime = 0
self.normConstr = 0
self.cons = []
self.symmats = []
# Make mosek environment
with mosek.Env() as env:
# Create a task object and attach log stream printer
with env.Task(0, 0) as task:
task.putdouparam(mosek.dparam.presolve_tol_x, presolveTol)
# options for convexity check are none, simple, full
task.putintparam(mosek.iparam.check_convexity,
mosek.checkconvexitytype.none)
task.putintparam(mosek.iparam.infeas_report_auto,
mosek.onoffkey.on)
task.putintparam(mosek.iparam.infeas_report_level, 40)
#task.putdouparam(mosek.dparam.intpnt_co_tol_infeas,1e-5)
if outputFlag:
task.set_Stream(mosek.streamtype.msg, streamprinter)
# Bound keys for constraints, vars
barvardim = [self.numFields, self.numFields]
numvar = 0
numcon = 1 + int((self.numFields + 1) * self.numFields / 2)
idx, jdx = np.tril_indices(self.numFields)
bkc = [mosek.boundkey.fx] + [mosek.boundkey.fx] * (numcon - 1)
blc = [self.dimPCA] + list((idx == jdx).astype(int))
buc = [self.dimPCA] + list((idx == jdx).astype(int))
task.appendvars(numvar)
task.appendcons(numcon)
task.putconboundslice(0, numcon, bkc, blc, buc)
task.appendbarvars(barvardim)
task.putbarcj(0, [
task.appendsparsesymmat(
self.numFields, idx, jdx,
-(1 / self.numPoints / self.normalizer) *
self.dat.T.dot(self.dat)[idx, jdx])
], [1.0])
self.symmats.append(('Obj', self.numFields))
task.putbaraij(0, 0, [
task.appendsparsesymmat(
self.numFields, range(self.numFields),
range(self.numFields), [1.0] * self.numFields)
], [1.0])
self.cons.append('traceCon')
self.symmats.append(('traceCon', self.numFields))
for count, (i, j) in enumerate(zip(idx, jdx)):
task.putbaraij(count + 1, 0, [
task.appendsparsesymmat(self.numFields, [i], [j],
[1.0])
], [1.0])
task.putbaraij(count + 1, 1, [
task.appendsparsesymmat(self.numFields, [i], [j],
[1.0])
], [1.0])
self.cons.append('X<=ICon_%s_%s' % (i, j))
self.symmats.append(('X<=ICon%s_%s_%s' % (0, i, j), 1))
self.symmats.append(('X<=ICon%s_%s_%s' % (1, i, j), 1))
task.putobjsense(mosek.objsense.minimize)
self.m = mosek.Task(task)
try:
try:
import mosek
except ImportError as e:
import traceback
text = traceback.format_exc()
with open(logfile, "a", encoding='utf-8', errors="ignore") as f:
f.write(text)
f.write('\n')
f.write(str(e))
else:
try:
import solfmt
with mosek.Env() as e:
with mosek.Task(e) as t:
t.readdata(probfile)
# Reset all string parameters. This should ensure that no rogue files are written
for p in mosek.sparam.members():
t.putstrparam(p, "")
t.set_Progress(pgscb)
t.linkfiletostream(mosek.streamtype.log, logfile, 0)
trm = t.optimize()
t.writetasksolverresult_file(tsolfile)
t.writejsonsol(jsolfile)
solfmt.formatSolution(t, 'ascii', asolfile)
with open(resfile, "wt", encoding="ascii") as f:
f.write("MSK_RES_OK")
def mosek_inner_point_solver(A, output, y_target, threshold_value, r_change):
"""
Solving Linear Programming on a Sample use Mosek
the tutorial of Mosek on (https://docs.mosek.com/9.1/pythonapi/tutorial-lo-shared.html)
:param A: jac matrix
shape: [1, num_labels, input_dimension]
:param output: label confidence
shape: [1, num_labels]
:param y_target: target label
shape: [1, num_labels]
value: {0, 1}
:param threshold_value: target label confidence, default is threshold(0.5)
shape: [1, num_labels]
value: 0.5
:param r_change:
:return:
"""
num_labels = y_target.shape[-1]
num_instances = A.shape[0]
d = A.shape[-1]
inf = 0.0
output = np.clip(output, 1e-6, 1.0 - (1e-6))
loss_current = -(y_target * np.log(output) +
(1 - y_target) * np.log(1 - output))
loss_target = -(y_target * np.log(np.ones(
(num_labels)) * threshold_value) + (1 - y_target) * np.log(1 - np.ones(
(num_labels)) * threshold_value))
delta_loss = loss_target - loss_current
r_change = r_change.reshape(num_instances, d)
tasks = []
with mosek.Env() as env:
for i in range(num_instances):
task = mosek.Task(env, 0, 0)
# task.set_Stream(mosek.streamtype.log, streamprinter)
task.putintparam(mosek.iparam.intpnt_multi_thread,
mosek.onoffkey.off)
A = A[i]
delta_loss = delta_loss[i]
# delete the full zero rows in jac matrix
delete_idx = []
for j in range(num_labels):
if (A[j] == 0).all():
delete_idx.append(j)
if len(delete_idx) != 0:
A = np.delete(A, delete_idx, axis=0)
delta_loss = np.delete(delta_loss, delete_idx, axis=0)
rows = A.shape[0]
# Set the boundry of all variables
# boundkey.fr [-inf, +inf]
# boundkey.lo [value, +inf]
# boundry for r1, r2,..., rd, z
# bkx: boundry type
# blx: low boundry
# blx: up boundry
bkx = [mosek.boundkey.ra for i in range(d)] + [mosek.boundkey.lo]
blx = [-1 for i in range(d)] + [0.0]
bux = [1 for i in range(d)] + [1.]
# Set the boundry of all constraints
# boundkey.up [-inf, value]
# bkc: boundry type
# blc: low boundry
# blc: up boundry
bkc = [mosek.boundkey.up for i in range(rows + d * 2)]
blc = [-inf for i in range(rows + d * 2)]
buc = delta_loss.tolist() + [0.0 for i in range(d * 2)]
# sparse matrix ordinal value, matrix stored by column.
# shape: n_x_feature, n_label
# [[0, 1, 2, ..., 19],
# ...
# [0, 1, 2, ..., 19]]
zeros_to_rows = np.tile(np.arange(rows).reshape(1, -1), (d, 1))
# [20, 22, 24, ...,]
rows_to_rows_add_d = np.arange(start=rows,
stop=rows + d * 2,
step=2).reshape(-1, 1)
# [21, 23, 25, ...,]
rows_add1_to_rows_add_d = (rows_to_rows_add_d + 1).reshape(-1, 1)
B = np.c_[zeros_to_rows, rows_to_rows_add_d,
rows_add1_to_rows_add_d]
asub = B.tolist()
ones_d = np.ones(d).reshape(-1, 1)
A = np.c_[A.T, ones_d, -ones_d]
aval = A.tolist()
asub.extend([[i for i in range(rows, rows + d * 2)]])
aval.extend([[-1 for i in range(rows, rows + d * 2)]])
c = [0.0 for i in range(d)] + [1]
numvar = len(bkx)
numcon = len(bkc)
task.appendcons(numcon)
task.appendvars(numvar)
#garbage collection
del A
del B
del output
del zeros_to_rows
del rows_to_rows_add_d
del rows_add1_to_rows_add_d
gc.collect()
for j in range(numvar):
# Set the linear term c_j in the objective.
task.putcj(j, c[j])
task.putvarbound(j, bkx[j], blx[j], bux[j])
task.putacol(
j, # Variable (column) index.
asub[j], # Row index of non-zeros in column j.
aval[j]) # Non-zero Values of column j.
for j in range(numcon):
task.putconbound(j, bkc[j], blc[j], buc[j])
task.putobjsense(mosek.objsense.minimize)
task.putintparam(mosek.iparam.optimizer,
mosek.optimizertype.intpnt)
task.putintparam(mosek.iparam.intpnt_basis, mosek.basindtype.never)
tasks.append(task)
# if the solution fails, then result is full zero
result = np.zeros((num_instances, d))
for i in range(num_instances):
tasks[i].optimize()
# tasks[i].solutionsummary(mosek.streamtype.msg)
# Get status information about the solution
solsta = tasks[i].getsolsta(mosek.soltype.itr)
if (solsta == mosek.solsta.optimal):
xx = [0.] * numvar
tasks[i].getxx(
mosek.soltype.itr, # Request the basic solution.
xx)
xx = np.asarray(xx)
result[i] = xx[:-1]
elif (solsta == mosek.solsta.dual_infeas_cer
or solsta == mosek.solsta.prim_infeas_cer):
print("Primal or dual infeasibility certificate found.\n")
elif solsta == mosek.solsta.unknown:
print("Unknown solution status")
else:
print("Other solution status")
return result
def __init__(self,
rsp,
dat,
lam=1,
conic=True,
dual=False,
K=None,
Y=None,
presolveTol=1.0e-30,
outputFlag=False):
inf = 0.0
numPnt = dat.shape[0]
self.K = K
self.Y = Y
self.numFields = dat.shape[1]
self.numPoints = numPnt
self.dat = dat
# To normalize variances
#for col,var in enumerate(np.var(self.dat,axis=0)):
# self.dat[:,col] -= np.mean(self.dat[:,col])
# self.dat[:,col] *= 1/math.sqrt(var)
# self.dat[:,col] += np.mean(self.dat[:,col])
self.rsp = rsp
self.lam = lam
self.dual = dual
self.alpha = [0.] * self.numPoints
self.B = [0.] * self.numFields
self.b = 0.0
self.eps = [0.] * self.numPoints
self.RunTime = 0
self.conList = []
self.quadConstr = False
self.conic = conic
# Make mosek environment
with mosek.Env() as env:
# Create a task object and attach log stream printer
with env.Task(0, 0) as task:
task.putdouparam(mosek.dparam.presolve_tol_x, presolveTol)
# options for convexity check are none, simple, full
task.putintparam(mosek.iparam.check_convexity,
mosek.checkconvexitytype.none)
if outputFlag:
task.set_Stream(mosek.streamtype.msg, streamprinter)
if not dual:
# Bound keys for constraints, vars
bkc = [mosek.boundkey.lo] * numPnt
bkx = [mosek.boundkey.fr] * (
self.numFields + 1) + [mosek.boundkey.lo] * numPnt
blc = [1.0] * numPnt
buc = [+math.inf] * numPnt
blx = [-math.inf] * (self.numFields + 1) + [0.0] * numPnt
bux = [+math.inf] * (self.numFields + numPnt + 1)
# Below is the sparse representation of the A
# matrix stored by row.
asub = [
list(range(self.numFields + 1)) +
[self.numFields + i + 1] for i in range(numPnt)
]
aval = [
list((2 * rsp[i] - 1) * dat[i]) +
[2 * rsp[i] - 1.0, 1.0] for i in range(numPnt)
]
numvar = self.numFields + numPnt + 1
numcon = len(bkc)
task.appendvars(numvar)
task.appendcons(numcon)
# Set objective.
task.putclist(
[self.numFields + i + 1 for i in range(numPnt)],
[lam] * numPnt)
task.putvarboundslice(0, numvar, bkx, blx, bux)
task.putconboundslice(0, numcon, bkc, blc, buc)
for i in range(numcon):
task.putarow(i, asub[i], aval[i])
if self.conic:
# t>=0, z==1
task.appendvars(2)
task.putvarbound(task.getnumvar() - 2,
mosek.boundkey.up, 0.0, +inf)
task.putvarbound(task.getnumvar() - 1,
mosek.boundkey.fx, 1., 1.)
# t*z>=sumsqr(B), obj coeff of t == 1
task.appendcone(
mosek.conetype.rquad, 0.0,
[task.getnumvar() - 2,
task.getnumvar() - 1] +
list(range(self.numFields)))
task.putcj(task.getnumvar() - 2, 1.)
else:
# sumqrt(B) in obj
qsubi = list(range(self.numFields))
qsubj = list(range(self.numFields))
qval = [1.0] * self.numFields
task.putqobj(qsubi, qsubj, qval)
else:
# Bound keys for constraints, vars
bkc = [mosek.boundkey.fx]
bkx = [mosek.boundkey.ra] * numPnt
blc = [0.0]
buc = [0.0]
blx = [0.0] * numPnt
bux = [lam] * numPnt
numvar = numPnt
numcon = 1
task.appendvars(numvar)
task.appendcons(numcon)
# Set objective.
task.putclist(list(range(numPnt)), [-1.0] * numPnt)
task.putvarboundslice(0, numvar, bkx, blx, bux)
task.putconboundslice(0, numcon, bkc, blc, buc)
task.putarow(0, range(numPnt), 2 * rsp - 1)
qval = K * Y + 1e-6 * np.eye(len(K))
if self.conic:
self.QChol = la.cholesky(
qval,
lower=True) # such that K*Y = QChol.dot(QChol.T)
task.appendvars(self.QChol.shape[1] + 2)
task.appendcons(self.QChol.shape[1])
# c unbounded, t>=0, z==1
task.putvarboundslice(
numPnt, numPnt + self.QChol.shape[1],
[mosek.boundkey.fr] * self.QChol.shape[1],
[-math.inf] * self.QChol.shape[1],
[math.inf] * self.QChol.shape[1])
task.putvarbound(numPnt + self.QChol.shape[1],
mosek.boundkey.lo, 0.0, math.inf)
task.putvarbound(numPnt + self.QChol.shape[1] + 1,
mosek.boundkey.fx, 1.0, 1.0)
task.putconboundslice(1, self.QChol.shape[1] + 1,
[mosek.boundkey.fx] *
self.QChol.shape[1],
[0.] * self.QChol.shape[1],
[0.] * self.QChol.shape[1])
i, j = np.meshgrid(range(1, self.QChol.shape[1] + 1),
range(numPnt))
# QChol.T.dot(alpha)==c
task.putaijlist(i.flatten(), j.flatten(),
self.QChol.flatten())
task.putaijlist(
range(1, self.QChol.shape[1] + 1),
range(numPnt, numPnt + self.QChol.shape[1]),
[-1.] * self.QChol.shape[1])
# t*z>=sumsqr(c)==alpha.T.dot(K*Y).dot(alpha)
task.appendcone(
mosek.conetype.rquad, 0.0,
[task.getnumvar() - 2,
task.getnumvar() - 1] +
list(range(numPnt, numPnt + self.QChol.shape[1])))
task.putcj(task.getnumvar() - 2, 1.)
else:
qsubj, qsubi = np.meshgrid(range(numPnt),
range(numPnt))
idx = np.where(
np.tril(np.ones_like(qval)).flatten() != 0)
task.putqobj(qsubi.flatten()[idx],
qsubj.flatten()[idx],
qval.flatten()[idx])
task.putobjsense(mosek.objsense.minimize)
self.m = mosek.Task(task)