The MOSEK Python optimizer API manual Version 7.0 (Revision 141)

38 CHAPTER 5. BASIC API TUTORIAL
114 print("Primal or dual infeasibility.\n")
115 elif solsta == mosek.solsta.near dual infeas cer:
116 print("Primal or dual infeasibility.\n")
117 elif solsta == mosek.solsta.near prim infeas cer:
118 print("Primal or dual infeasibility.\n")
119 elif mosek.solsta.unknown:
120 print("Unknown solution status")
121 else:
122 print("Other solution status")
123
124
125 # call the main function
126 try:
127 main ()
128 except mosek.Exception as e:
129 print ("ERROR: %s" % str(e.code))
130 if msg is not None:
131 print ("\t%s" % e.msg)
132 sys.exit(1)
133 except:
134 import traceback
135 traceback.print exc()
136 sys.exit(1)
137 sys.exit(0)
5.3.1.2 Source code comments
The only new function introduced in the example is Task.appendcone, which is called here:
86 task.appendcone(mosek.conetype.quad,
87 0.0,
88 [ 3, 0, 1 ])
[ cqo1.py ]
The first argument selects the type of quadratic cone. Either conetype.quad for a quadratic cone
or conetype.rquad for a rotated quadratic cone. The cone parameter 0.0 is currently not used by
MOSEK — simply passing 0.0 will work.
The last argument is a list of indexes of the variables in the cone.
5.4 Semidefinite optimization
Semidefinite optimization is a generalization of conic quadratic optimization, allowing the use of matrix
variables belonging to the convex cone of positive semidefinite matrices
S + r = { X ∈ S r : z T Xz ≥ 0, ∀z ∈ R r} ,
where S r is the set of r×r real-valued symmetric matrices.
MOSEK can solve semidefinite optimization problems of the form