13.3 Conic Optimization  Interiorpoint optimizer¶
For conic optimization problems only an interiorpoint type optimizer is available.
13.3.1 The homogeneous primaldual problem¶
The interiorpoint optimizer is an implementation of the socalled homogeneous and selfdual algorithm. For a detailed description of the algorithm, please see [ART03]. In order to keep our discussion simple we will assume that MOSEK solves a conic optimization problem of the form:
where \(K\) is a convex cone. The corresponding dual problem is
where \(\K^*\) is the dual cone of \(\K\). See Sec. 12.2 (Conic Optimization) for definitions.
Since it is not known beforehand whether problem (13.6) has an optimal solution, is primal infeasible or is dual infeasible, the optimization algorithm must deal with all three situations. This is the reason that MOSEK solves the socalled homogeneous model
where \(y\) and \(s\) correspond to the dual variables in (13.6), and \(\tau\) and \(\kappa\) are two additional scalar variables. Note that the homogeneous model (13.8) always has a solution since
is a solution, although not a very interesting one. Any solution
to the homogeneous model (13.8) satisfies
i.e. complementarity. Observe that \(x^* \in \K\) and \(s^* \in \K^*\) implies
and therefore
since \(\tau^*, \kappa^* \geq 0\). Hence, at least one of \(\tau^*\) and \(\kappa^*\) is zero.
First, assume that \(\tau^*>0\) and hence \(\kappa^*=0\). It follows that
This shows that \(\frac{x^*}{\tau^*}\) is a primal optimal solution and \((\frac{y^*}{\tau^*},\frac{s^*}{\tau^*})\) is a dual optimal solution; this is reported as the optimal interiorpoint solution since
is a primaldual optimal solution.
On other hand, if \(\kappa^* >0\) then
This implies that at least one of
or
holds. If (13.9) is satisfied, then \(x^*\) is a certificate of dual infeasibility, whereas if (13.10) holds then \(y^*\) is a certificate of primal infeasibility.
In summary, by computing an appropriate solution to the homogeneous model, all information required for a solution to the original problem is obtained. A solution to the homogeneous model can be computed using a primaldual interiorpoint algorithm [And09].
13.3.2 Interiorpoint Termination Criterion¶
Since computations are performed in finite precision, and for efficiency reasons, it is not possible to solve the homogeneous model exactly in general. Hence, an exact optimal solution or an exact infeasibility certificate cannot be computed and a reasonable termination criterion has to be employed.
In every iteration \(k\) of the interiorpoint algorithm a trial solution
to the homogeneous model is generated, where
Therefore, it is possible to compute the values:
Note \(\varepsilon_p, \varepsilon_d, \varepsilon_g \mbox{ and } \varepsilon_i\) are nonnegative user specified tolerances.
Optimal Case
Observe \(\rho_p^k\) measures how far \(x^k/\tau^k\) is from being a good approximate primal feasible solution. Indeed if \(\rho_p^k \leq 1\), then
This shows the violations in the primal equality constraints for the solution \(x^k/\tau^k\) is small compared to the size of \(b\) given \(\varepsilon_p\) is small.
Similarly, if \(\rho_d^k \leq 1\), then \((y^k,s^k)/\tau^k\) is an approximate dual feasible solution. If in addition \(\rho_g \leq 1\), then the solution \((x^k,y^k,s^k)/\tau^k\) is approximate optimal because the associated primal and dual objective values are almost identical.
In other words if \(\max(\rho_p^k,\rho_d^k,\rho_g^k) \leq 1\), then
is an approximate optimal solution.
Dual Infeasibility Certificate
Next assume that \(\rho_{di}^k \leq 1\) and hence
holds. Now in this case the problem is declared dual infeasible and \(x^k\) is reported as a certificate of dual infeasibility. The motivation for this stopping criterion is as follows. Let
and it is easy to verify that
which shows \(\bar{x}\) is an approximate certificate of dual infeasibility, where \(\varepsilon_{i}\) controls the quality of the approximation.
Primal Infeasiblity Certificate
Next assume that \(\rho_{pi}^k \leq 1\) and hence
holds. Now in this case the problem is declared primal infeasible and \((y^k,s^k)\) is reported as a certificate of primal infeasibility. The motivation for this stopping criterion is as follows. Let
and it is easy to verify that
which shows \((y^k,s^k)\) is an approximate certificate of dual infeasibility, where \(\varepsilon_{i}\) controls the quality of the approximation.
13.3.3 Adjusting optimality criteria¶
It is possible to adjust the tolerances \(\varepsilon_{p}\), \(\varepsilon_{d}\), \(\varepsilon_{g}\) and \(\varepsilon_{i}\) using parameters; see the next table for details.
ToleranceParameter 
Name (for conic problems) 

\(\varepsilon_{p}\) 

\(\varepsilon_{d}\) 

\(\varepsilon_{g}\) 

\(\varepsilon_{i}\) 
The default values of the termination tolerances are chosen such that for a majority of problems appearing in practice it is not possible to achieve much better accuracy. Therefore, tightening the tolerances usually is not worthwhile. However, an inspection of (13.11) reveals that the quality of the solution depends on \(\left\ b \right\_{\infty}\) and \(\left\ c \right\_{\infty}\); the smaller the norms are, the better the solution accuracy.
The interiorpoint method as implemented by MOSEK will converge toward optimality and primal and dual feasibility at the same rate [And09]. This means that if the optimizer is stopped prematurely then it is very unlikely that either the primal or dual solution is feasible. Another consequence is that in most cases all the tolerances, \(\varepsilon_p\), \(\varepsilon_d\), \(\varepsilon_g\) and \(\varepsilon_i\), have to be relaxed together to achieve an effect.
If the optimizer terminates without locating a solution that satisfies the termination criteria, for example because of a stall or other numerical issues, then it will check if the solution found up to that point satisfies the same criteria with all tolerances multiplied by the value of intpntCoTolNearRel
. If this is the case, the solution is still declared as optimal.
To conclude the discussion in this section, relaxing the termination criterion is usually not worthwhile.
13.3.4 The Interiorpoint Log¶
Below is a typical log output from the interiorpoint optimizer:
Optimizer  threads : 20
Optimizer  solved problem : the primal
Optimizer  Constraints : 1
Optimizer  Cones : 2
Optimizer  Scalar variables : 6 conic : 6
Optimizer  Semidefinite variables: 0 scalarized : 0
Factor  setup time : 0.00 dense det. time : 0.00
Factor  ML order time : 0.00 GP order time : 0.00
Factor  nonzeros before factor : 1 after factor : 1
Factor  dense dim. : 0 flops : 1.70e+01
ITE PFEAS DFEAS GFEAS PRSTATUS POBJ DOBJ MU TIME
0 1.0e+00 2.9e01 3.4e+00 0.00e+00 2.414213562e+00 0.000000000e+00 1.0e+00 0.01
1 2.7e01 7.9e02 2.2e+00 8.83e01 6.969257574e01 9.685901771e03 2.7e01 0.01
2 6.5e02 1.9e02 1.2e+00 1.16e+00 7.606090061e01 6.046141322e01 6.5e02 0.01
3 1.7e03 5.0e04 2.2e01 1.12e+00 7.084385672e01 7.045122560e01 1.7e03 0.01
4 1.4e08 4.2e09 4.9e08 1.00e+00 7.071067941e01 7.071067599e01 1.4e08 0.01
The first line displays the number of threads used by the optimizer and the second line indicates if the optimizer chose to solve the primal or dual problem (see intpntSolveForm
). The next lines display the problem dimensions as seen by the optimizer, and the Factor...
lines show various statistics. This is followed by the iteration log.
Using the same notation as in Sec. 13.3.1 (The homogeneous primaldual problem) the columns of the iteration log have the following meaning:
ITE
: Iteration index \(k\).PFEAS
: \(\left\ Ax^k  b \tau^k \right\_{\infty}\) . The numbers in this column should converge monotonically towards zero but may stall at low level due to rounding errors.DFEAS
: \(\left\ A^T y^{k} + s^k  c \tau^k \right\_{\infty}\) . The numbers in this column should converge monotonically towards zero but may stall at low level due to rounding errors.GFEAS
: \(  c^T x^k + b^T y^{k}  \kappa^k\) . The numbers in this column should converge monotonically towards zero but may stall at low level due to rounding errors.PRSTATUS
: This number converges to \(1\) if the problem has an optimal solution whereas it converges to \(1\) if that is not the case.POBJ
: \(c^T x^k / \tau^k\). An estimate for the primal objective value.DOBJ
: \(b^T y^k / \tau^k\). An estimate for the dual objective value.MU
: \(\frac{ (x^k)^T s^k + \tau^k \kappa^k }{n+1}\) . The numbers in this column should always converge to zero.TIME
: Time spent since the optimization started (in seconds).