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

5.5. QUADRATIC OPTIMIZATION 51
81 asub[j], # Row index of non-zeros in column j.
82 aval[j]) # Non-zero Values of column j.
83
84 for i in range(numcon):
85 task.putbound(mosek.accmode.con,i,bkc[i],blc[i],buc[i])
86
87 # Set up and input quadratic objective
88
89 qsubi = [ 0, 1, 2, 2 ]
90 qsubj = [ 0, 1, 0, 2 ]
91 qval = [ 2.0, 0.2, -1.0, 2.0 ]
92
93 task.putqobj(qsubi,qsubj,qval)
94
95 # The lower triangular part of the Q^0
96 # matrix in the first constraint is specified.
97 # This corresponds to adding the term
98 # - x0^2 - x1^2 - 0.1 x2^2 + 0.2 x0 x2
99
100 qsubi = [ 0, 1, 2, 2 ]
101 qsubj = [ 0, 1, 2, 0 ]
102 qval = [ -2.0, -2.0, -0.2, 0.2 ]
103
104 # put Q^0 in constraint with index 0.
105
106 task.putqconk (0, qsubi,qsubj, qval);
107
108 # Input the objective sense (minimize/maximize)
109 task.putobjsense(mosek.objsense.minimize)
110
111 # Optimize the task
112 task.optimize()
113
114 # Print a summary containing information
115 # about the solution for debugging purposes
116 task.solutionsummary(mosek.streamtype.msg)
117
118 prosta = task.getprosta(mosek.soltype.itr)
119 solsta = task.getsolsta(mosek.soltype.itr)
120
121 # Output a solution
122 xx = zeros(numvar, float)
123 task.getxx(mosek.soltype.itr,
124 xx)
125
126 if solsta == mosek.solsta.optimal or solsta == mosek.solsta.near optimal:
127 print("Optimal solution: %s" % xx)
128 elif solsta == mosek.solsta.dual infeas cer:
129 print("Primal or dual infeasibility.\n")
130 elif solsta == mosek.solsta.prim infeas cer:
131 print("Primal or dual infeasibility.\n")
132 elif solsta == mosek.solsta.near dual infeas cer:
133 print("Primal or dual infeasibility.\n")
134 elif solsta == mosek.solsta.near prim infeas cer:
135 print("Primal or dual infeasibility.\n")
136 elif mosek.solsta.unknown:
137 print("Unknown solution status")
138 else: