##
#  Copyright : Copyright (c) MOSEK ApS, Denmark. All rights reserved.
#
#  File :      ceo1.py
#
#  Purpose :   Demonstrates how to solve small conic exponential
#              optimization problem using the MOSEK Python API.
##
import sys
import mosek

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

def main():

    # Only a symbolic constant
    inf = 0.0

    # Create a task
    with mosek.Task() as task:
        # Attach a printer to the task
        task.set_Stream(mosek.streamtype.log, streamprinter)

        c = [1.0, 1.0, 0.0]
        a = [1.0, 1.0, 1.0]
        numvar, numcon = 3, 1

        # 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)

        # Set up the linear part of the problem
        task.putcslice(0, numvar, c)
        task.putarow(0, [0, 1, 2], a)
        task.putvarboundslice(0, numvar, [mosek.boundkey.fr] * numvar, [inf] * numvar, [inf] * numvar)
        task.putconbound(0, mosek.boundkey.fx, 1.0, 1.0)

        # Add a conic constraint
        # Create a 3x3 identity matrix F
        task.appendafes(3)
        task.putafefentrylist([0, 1, 2],         # Rows 
                              [0, 1, 2],         # Columns 
                              [1.0] * 3)

        # Exponential cone (x(0),x(1),x(2)) \in EXP 
        expdomain  = task.appendprimalexpconedomain()
        task.appendacc(expdomain,               # Domain
                       [0, 1, 2],               # Rows from F 
                       None)                    # Unused 

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

        # Optimize the task
        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.code))
    if msg is not None:
        print("\t%s" % e.msg)
        sys.exit(1)
except:
    import traceback
    traceback.print_exc()
    sys.exit(1)