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:

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

In Fusion the coordinates of a cone are not restricted to single variables. They can be arbitrary linear expressions, and an auxiliary variable will be substituted by Fusion in a way transparent to the user.

## 7.2.1 Example CQO1¶

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

(1)$\begin{split} \begin{array}{ll} \minimize & y_1 + y_2 + y_3 \\ \st & x_1 + x_2 + 2.0 x_3 = 1.0,\\ & x_1,x_2,x_3 \geq 0.0,\\ & (y_1,x_1,x_2) \in \Q^3,\\ & (y_2,y_3,x_3) \in \Qr^3. \end{array}\end{split}$

We start by creating the optimization model:

with Model('cqo1') as M:


We then define variables x and y. Two logical variables (aliases) z1 and z2 are introduced to model the quadratic cones. These are not new variables, but map onto parts of x and y for the sake of convenience.

    x = M.variable('x', 3, Domain.greaterThan(0.0))
y = M.variable('y', 3, Domain.unbounded())

# Create the aliases
#      z1 = [ y[0],x[0],x[1] ]
#  and z2 = [ y[1],y[2],x[2] ]
z1 = Var.vstack(y.index(0), x.slice(0, 2))
z2 = Var.vstack(y.slice(1, 3), x.index(2))


The linear constraint is defined using the dot product:

    # Create the constraint
#      x[0] + x[1] + 2.0 x[2] = 1.0
M.constraint("lc", Expr.dot([1.0, 1.0, 2.0], x), Domain.equalsTo(1.0))


The conic constraints are defined using the logical views z1 and z2 created previously. Note that this is a basic way of defining conic constraints, and that in practice they would have more complicated structure.

    # Create the constraints
#      z1 belongs to C_3
#      z2 belongs to K_3
# where C_3 and K_3 are respectively the quadratic and
# rotated quadratic cone of size 3, i.e.
#                 z1[0] >= sqrt(z1[1]^2 + z1[2]^2)
#  and  2.0 z2[0] z2[1] >= z2[2]^2
qc1 = M.constraint("qc1", z1, Domain.inQCone())
qc2 = M.constraint("qc2", z2, Domain.inRotatedQCone())


We only need the objective function:

    # Set the objective function to (y[0] + y[1] + y[2])
M.objective("obj", ObjectiveSense.Minimize, Expr.sum(y))


Calling the Model.solve method invokes the solver:

    M.solve()


The primal and dual solution values can be retrieved using Variable.level, Constraint.level and Variable.dual, Constraint.dual, respectively:

    # Get the linear solution values
solx = x.level()
soly = y.level()

    # Get conic solution of qc1
qc1lvl = qc1.level()
qc1sn = qc1.dual()

Listing 4 Fusion implementation of model (1). Click here to download.
from mosek.fusion import *

with Model('cqo1') as M:

x = M.variable('x', 3, Domain.greaterThan(0.0))
y = M.variable('y', 3, Domain.unbounded())

# Create the aliases
#      z1 = [ y[0],x[0],x[1] ]
#  and z2 = [ y[1],y[2],x[2] ]
z1 = Var.vstack(y.index(0), x.slice(0, 2))
z2 = Var.vstack(y.slice(1, 3), x.index(2))

# Create the constraint
#      x[0] + x[1] + 2.0 x[2] = 1.0
M.constraint("lc", Expr.dot([1.0, 1.0, 2.0], x), Domain.equalsTo(1.0))

# Create the constraints
#      z1 belongs to C_3
#      z2 belongs to K_3
# where C_3 and K_3 are respectively the quadratic and
# rotated quadratic cone of size 3, i.e.
#                 z1[0] >= sqrt(z1[1]^2 + z1[2]^2)
#  and  2.0 z2[0] z2[1] >= z2[2]^2
qc1 = M.constraint("qc1", z1, Domain.inQCone())
qc2 = M.constraint("qc2", z2, Domain.inRotatedQCone())

# Set the objective function to (y[0] + y[1] + y[2])
M.objective("obj", ObjectiveSense.Minimize, Expr.sum(y))

# Solve the problem
M.solve()

# Get the linear solution values
solx = x.level()
soly = y.level()
print('x1,x2,x3 = %s' % str(solx))
print('y1,y2,y3 = %s' % str(soly))

# Get conic solution of qc1
qc1lvl = qc1.level()
qc1sn = qc1.dual()
print('qc1 levels                = %s' % str(qc1lvl))
print('qc1 dual conic var levels = %s' % str(qc1sn))