##
#  Copyright : Copyright (c) MOSEK ApS, Denmark. All rights reserved.
#
#  File :      qo1.py
#
#  Purpose :   Demonstrate how to solve a quadratic
#              optimization problem using the MOSEK Python API.
##

import sys, os, mosek

# Since the actual value of Infinity is ignored, we define it solely
# for symbolic purposes:
inf = 0.0

# Define a stream printer to grab output from MOSEK
def streamprinter(text):
    sys.stdout.write(text)
    sys.stdout.flush()


def main():
    # Open MOSEK and create an environment and task
    # Make a MOSEK environment
    with mosek.Env() as env:
        # Attach a printer to the environment
        env.set_Stream(mosek.streamtype.log, streamprinter)
        # Create a task
        with env.Task() as task:
            task.set_Stream(mosek.streamtype.log, streamprinter)
            # Set up and input bounds and linear coefficients
            bkc = [mosek.boundkey.lo]
            blc = [1.0]
            buc = [inf]
            numvar = 3
            bkx = [mosek.boundkey.lo] * numvar
            blx = [0.0] * numvar
            bux = [inf] * numvar
            c = [0.0, -1.0, 0.0]
            asub = [[0], [0], [0]]
            aval = [[1.0], [1.0], [1.0]]

            numvar = len(bkx)
            numcon = len(bkc)

            # Append 'numcon' empty constraints.
            # The constraints will initially have no bounds.
            task.appendcons(numcon)

            # Append 'numvar' variables.
            # The variables will initially be fixed at zero (x=0).
            task.appendvars(numvar)

            for j in range(numvar):
                # Set the linear term c_j in the objective.
                task.putcj(j, c[j])
                # Set the bounds on variable j
                # blx[j] <= x_j <= bux[j]
                task.putvarbound(j, bkx[j], blx[j], bux[j])
                # Input column j of A
                task.putacol(j,                  # Variable (column) index.
                             # Row index of non-zeros in column j.
                             asub[j],
                             aval[j])            # Non-zero Values of column j.
            for i in range(numcon):
                task.putconbound(i, bkc[i], blc[i], buc[i])

            # Set up and input quadratic objective
            qsubi = [0, 1, 2, 2]
            qsubj = [0, 1, 0, 2]
            qval = [2.0, 0.2, -1.0, 2.0]

            task.putqobj(qsubi, qsubj, qval)

            # Input the objective sense (minimize/maximize)
            task.putobjsense(mosek.objsense.minimize)

            # Optimize
            task.optimize()
            # Print a summary containing information
            # about the solution for debugging purposes
            task.solutionsummary(mosek.streamtype.msg)

            prosta = task.getprosta(mosek.soltype.itr)
            solsta = task.getsolsta(mosek.soltype.itr)

            # Output a solution
            xx = task.getxx(mosek.soltype.itr)

            if solsta == mosek.solsta.optimal:
                print("Optimal solution: %s" % xx)
            elif solsta == mosek.solsta.dual_infeas_cer:
                print("Primal or dual infeasibility.\n")
            elif solsta == mosek.solsta.prim_infeas_cer:
                print("Primal or dual infeasibility.\n")
            elif mosek.solsta.unknown:
                print("Unknown solution status")
            else:
                print("Other solution status")


# call the main function
try:
    main()
except mosek.MosekException as e:
    print("ERROR: %s" % str(e.errno))
    if e.msg is not None:
        import traceback
        traceback.print_exc()
        print("\t%s" % e.msg)
    sys.exit(1)
except:
    import traceback
    traceback.print_exc()
    sys.exit(1)