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

34 CHAPTER 5. BASIC API TUTORIAL
109 xx = zeros(numvar, float)
110 task.getxx(mosek.soltype.bas,
111 xx)
112
113 if solsta == mosek.solsta.optimal or solsta == mosek.solsta.near optimal:
114 print("Optimal solution: %s" % xx)
115 elif solsta == mosek.solsta.dual infeas cer:
116 print("Primal or dual infeasibility.\n")
117 elif solsta == mosek.solsta.prim infeas cer:
118 print("Primal or dual infeasibility.\n")
119 elif solsta == mosek.solsta.near dual infeas cer:
120 print("Primal or dual infeasibility.\n")
121 elif solsta == mosek.solsta.near prim infeas cer:
122 print("Primal or dual infeasibility.\n")
123 elif mosek.solsta.unknown:
124 print("Unknown solution status")
125 else:
126 print("Other solution status")
127
128 # call the main function
129 try:
130 main ()
131 except mosek.Exception as e:
132 print ("ERROR: %s" % str(e.code))
133 if msg is not None:
134 print ("\t%s" % e.msg)
135 sys.exit(1)
136 except:
137 import traceback
138 traceback.print exc()
139 sys.exit(1)
140 sys.exit(0)
5.3 Conic quadratic optimization
Conic optimization is a generalization of linear optimization, allowing constraints of the type
x t ∈ C t ,
where x t is a subset of the problem variables and C t is a convex cone. Actually, since the set R n of
real numbers is also a convex cone, all variables can in fact be partitioned into subsets belonging to
separate convex cones, simply stated x ∈ C.
MOSEK can solve conic quadratic optimization problems of the form
minimize
c T x + c f
subject to l c ≤ Ax ≤ u c ,
l x ≤ x ≤ u x ,
x ∈ C,
where the domain restriction, x ∈ C, implies that all variables are partitioned into convex cones
(5.4)