Conic optimization is a generalization of linear optimization, allowing constraints of the type

$x^t \in \K_t,$

where $$x^t$$ is a subset of the problem variables and $$\K_t$$ is a convex cone. Since the set $$\real^n$$ of real numbers is also a convex cone, we can simply write a compound conic constraint $$x\in \K$$ where $$\K=\K_1\times\cdots\times\K_l$$ is a product of smaller cones and $$x$$ is the full problem variable.

MOSEK can solve conic quadratic optimization problems of the form

$\begin{split}\begin{array}{lccccl} \mbox{minimize} & & & c^T x + c^f & & \\ \mbox{subject to} & l^c & \leq & A x & \leq & u^c, \\ & l^x & \leq & x & \leq & u^x, \\ & & & x \in \K, & & \end{array}\end{split}$

where the domain restriction, $$x \in \K$$, implies that all variables are partitioned into convex cones

$x = (x^0, x^1, \ldots , x^{p-1}),\quad \mbox{with } x^t \in \K_t \subseteq \real^{n_t}.$

For convenience, a user defining a conic quadratic problem only needs to specify subsets of variables $$x^t$$ belonging to quadratic cones. These are:

$\Q^n = \left\lbrace x \in \real^n: x_0 \geq \sqrt{\sum_{j=1}^{n-1} x_j^2} \right\rbrace.$

$\Qr^n = \left\lbrace x \in \real^n: 2 x_0 x_1 \geq \sum_{j=2}^{n-1} x_j^2,\quad x_0\geq 0,\quad x_1 \geq 0 \right\rbrace.$

For example, the following constraint:

$(x_4, x_0, x_2) \in \Q^3$

describes a convex cone in $$\real^3$$ given by the inequality:

$x_4 \geq \sqrt{x_0^2 + x_2^2}.$

Furthermore, each variable may belong to one cone at most. The constraint $$x_i - x_j = 0$$ would however allow $$x_i$$ and $$x_j$$ to belong to different cones with same effect.

## 6.3.1 Example CQO1¶

Consider the following conic quadratic problem which involves some linear constraints, a quadratic cone and a rotated quadratic cone.

(1)$\begin{split}\begin{array} {lccc} \mbox{minimize} & x_4 + x_5 + x_6 & & \\ \mbox{subject to} & x_1+x_2+ 2 x_3 & = & 1, \\ & x_1,x_2,x_3 & \geq & 0, \\ & x_4 \geq \sqrt{x_1^2 + x_2^2}, & & \\ & 2 x_5 x_6 \geq x_3^2 & & \end{array}\end{split}$

Setting up the linear part

The linear parts (constraints, variables, objective) are set up using exactly the same methods as for linear problems, and we refer to Sec. 6.1 (Linear Optimization) for all the details. The same applies to technical aspects such as defining an optimization task, retrieving the solution and so on.

Setting up the conic constraints

A cone is defined using the function Task.appendcone:

            task.appendcone(mosek.conetype.quad,
0.0,
[3, 0, 1])


The first argument selects the type of quadratic cone, in this case either conetype.quad for a quadratic cone or conetype.rquad for a rotated quadratic cone. The second parameter is currently ignored and passing 0.0 will work.

The last argument is a list of indexes of the variables appearing in the cone.

Variants of this method are available to append multiple cones at a time.

Source code

Listing 7 Source code solving problem (1). Click here to download.
import sys
import mosek

# Since the actual value of Infinity is ignores, we define it solely
# for symbolic purposes:
inf = 0.0

# Define a stream printer to grab output from MOSEK
def streamprinter(text):
sys.stdout.write(text)
sys.stdout.flush()

def main():
# Make a MOSEK environment
with mosek.Env() as env:
# Attach a printer to the environment
env.set_Stream(mosek.streamtype.log, streamprinter)

# Attach a printer to the task

bkc = [mosek.boundkey.fx]
blc = [1.0]
buc = [1.0]

c = [0.0, 0.0, 0.0,
1.0, 1.0, 1.0]
bkx = [mosek.boundkey.lo, mosek.boundkey.lo, mosek.boundkey.lo,
mosek.boundkey.fr, mosek.boundkey.fr, mosek.boundkey.fr]
blx = [0.0, 0.0, 0.0,
-inf, -inf, -inf]
bux = [inf, inf, inf,
inf, inf, inf]

asub = [[0], [0], [0]]
aval = [[1.0], [1.0], [2.0]]

numvar = len(bkx)
numcon = len(bkc)
NUMANZ = 4

# Append 'numcon' empty constraints.
# The constraints will initially have no bounds.

#Append 'numvar' variables.
# The variables will initially be fixed at zero (x=0).

for j in range(numvar):
# Set the linear term c_j in the objective.
# Set the bounds on variable j
# blx[j] <= x_j <= bux[j]

for j in range(len(aval)):
# Input column j of A
# Row index of non-zeros in column j.
asub[j],
aval[j])            # Non-zero Values of column j.
for i in range(numcon):

# Input the cones
0.0,
[3, 0, 1])
0.0,
[4, 5, 2])

# Input the objective sense (minimize/maximize)

# Print a summary containing information
# about the solution for debugging purposes

# Output a solution
xx = [0.] * numvar
xx)

if solsta == mosek.solsta.optimal or solsta == mosek.solsta.near_optimal:
print("Optimal solution: %s" % xx)
elif solsta == mosek.solsta.dual_infeas_cer:
print("Primal or dual infeasibility.\n")
elif solsta == mosek.solsta.prim_infeas_cer:
print("Primal or dual infeasibility.\n")
elif solsta == mosek.solsta.near_dual_infeas_cer:
print("Primal or dual infeasibility.\n")
elif solsta == mosek.solsta.near_prim_infeas_cer:
print("Primal or dual infeasibility.\n")
elif mosek.solsta.unknown:
print("Unknown solution status")
else:
print("Other solution status")

# call the main function
try:
main()
except mosek.MosekException as e:
print("ERROR: %s" % str(e.errno))
print("\t%s" % e.msg)
sys.exit(1)
except:
import traceback
traceback.print_exc()
sys.exit(1)