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

5.10. PROBLEM MODIFICATION AND REOPTIMIZATION 63
Denoting the number of items of each type by x 0 , x 1 and x 2 , this problem can be formulated as the
linear optimization problem:
and
maximize 1.5x 0 + 2.5x 1 + 3.0x 2
subject to 2x 0 + 4x 1 + 3x 2 ≤ 100000,
3x 0 + 2x 1 + 3x 2 ≤ 50000,
2x 0 + 3x 1 + 2x 2 ≤ 60000,
x 0 , x 1 , x 2 ≥ 0.
The following code loads this problem into the optimization task.
35 # Create a MOSEK environment
36 env = mosek.Env ()
37
[ production.py ]
38 # Create a task
39 task = env.Task(0,0)
40 # Attach a printer to the task
41 task.set Stream (mosek.streamtype.log, streamprinter)
42
43 # Bound keys for constraints
44 bkc = [mosek.boundkey.up,
45 mosek.boundkey.up,
46 mosek.boundkey.up]
47 # Bound values for constraints
48 blc = array ([-inf, -inf, -inf])
49 buc = array ([100000.0 , 50000.0, 60000.0])
50
51 # Bound keys for variables
52 bkx = [mosek.boundkey.lo,
53 mosek.boundkey.lo,
54 mosek.boundkey.lo]
55 # Bound values for variables
56 blx = array ([ 0.0, 0.0, 0.0])
57 bux = array ([+inf, +inf, +inf])
58
59 # Objective coefficients
60 csub = array([ 0, 1, 2 ])
61 cval = array([ 1.5, 2.5, 3.0 ])
62
63 # We input the A matrix column-wise
64 # asub contains row indexes
65 asub = array([ 0, 1, 2,
66 0, 1, 2,
67 0, 1, 2])
68 # acof contains coefficients
69 acof = array([ 2.0, 3.0, 2.0,
70 4.0, 2.0, 3.0,
71 3.0, 3.0, 2.0 ])
72 # aptrb and aptre contains the offsets into asub and acof where
73 # columns start and end respectively
74 aptrb = array([ 0, 3, 6 ])
75 aptre = array([ 3, 6, 9 ])