##
#  Copyright : Copyright (c) MOSEK ApS, Denmark. All rights reserved.
#
#  File :      sdo1.py
#
#  Purpose :   Demonstrates how to solve a small mixed semidefinite and conic quadratic
#              optimization problem using the MOSEK Python API.
##
import sys
import mosek

# Since the 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():
    # Create a task object and attach log stream printer
    with mosek.Task() as task:
        task.set_Stream(mosek.streamtype.log, streamprinter)

        # Bound keys for constraints
        bkc = [mosek.boundkey.fx,
               mosek.boundkey.fx]

        # Bound values for constraints
        blc = [1.0, 0.5]
        buc = [1.0, 0.5]

        # Below is the sparse representation of the A
        # matrix stored by row.
        asub = [[0],
                [1, 2]]
        aval = [[1.0],
                [1.0, 1.0]]

        barci = [0, 1, 1, 2, 2]
        barcj = [0, 0, 1, 1, 2]
        barcval = [2.0, 1.0, 2.0, 1.0, 2.0]

        barai = [[0, 1, 2],
                 [0, 1, 2, 1, 2, 2]]
        baraj = [[0, 1, 2],
                 [0, 0, 0, 1, 1, 2]]
        baraval = [[1.0, 1.0, 1.0],
                   [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]

        numvar = 3
        numcon = len(bkc)
        BARVARDIM = [3]

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

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

        # Append matrix variables of sizes in 'BARVARDIM'.
        # The variables will initially be fixed at zero.
        task.appendbarvars(BARVARDIM)

        # Set the linear term c_0 in the objective.
        task.putcj(0, 1.0)

        for j in range(numvar):
            # Set the bounds on variable j
            # blx[j] <= x_j <= bux[j]
            task.putvarbound(j, mosek.boundkey.fr, -inf, +inf)

        for i in range(numcon):
            # Set the bounds on constraints.
            # blc[i] <= constraint_i <= buc[i]
            task.putconbound(i, bkc[i], blc[i], buc[i])

            # Input row i of A
            task.putarow(i,                  # Constraint (row) index.
                         asub[i],            # Column index of non-zeros in constraint i.
                         aval[i])            # Non-zero values of row i.

        # Add the quadratic cone constraint
        task.appendafes(3)
        # Diagonal F matrix
        task.putafefentrylist(range(3), range(3), [1.0]*3)
        task.appendaccseq(task.appendquadraticconedomain(3), 0, None)
        
        symc = task.appendsparsesymmat(BARVARDIM[0],
                                       barci,
                                       barcj,
                                       barcval)

        syma0 = task.appendsparsesymmat(BARVARDIM[0],
                                        barai[0],
                                        baraj[0],
                                        baraval[0])

        syma1 = task.appendsparsesymmat(BARVARDIM[0],
                                        barai[1],
                                        baraj[1],
                                        baraval[1])

        task.putbarcj(0, [symc], [1.0])

        task.putbaraij(0, 0, [syma0], [1.0])
        task.putbaraij(1, 0, [syma1], [1.0])

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

        # Solve the problem and print summary
        task.optimize()
        task.solutionsummary(mosek.streamtype.msg)

        # Get status information about the solution
        prosta = task.getprosta(mosek.soltype.itr)
        solsta = task.getsolsta(mosek.soltype.itr)

        if (solsta == mosek.solsta.optimal):
            xx = task.getxx(mosek.soltype.itr)
            barx = task.getbarxj(mosek.soltype.itr, 0)

            print("Optimal solution:\nx=%s\nbarx=%s" % (xx, barx))
        elif (solsta == mosek.solsta.dual_infeas_cer or
              solsta == mosek.solsta.prim_infeas_cer):
            print("Primal or dual infeasibility certificate found.\n")
        elif solsta == 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:
        print("\t%s" % e.msg)
        sys.exit(1)
except:
    import traceback
    traceback.print_exc()
    sys.exit(1)