for CasADi 3.0

par_dgb.py

import casadi as ca
import pylab as pl

N = 50

WORKING_EXAMPLE = False

if WORKING_EXAMPLE:

    x = ca.MX.sym("x", 2)

    x0 = x[0]
    x1 = x[1]

else:

    x0 = ca.MX.sym("x0", 1)
    x1 = ca.MX.sym("x1", 1)

    x = ca.veccat(x0, x1)


y = x0

for k in range(N):
    y = pl.sin(y)

z = x1

for i in range(N):
  z = pl.cos(z)

# Serial NLP solving

f_s = y + z

nlp_s = {"x" :x, "f": f_s}
nlpsolver_s = ca.nlpsol("nlpsolver", "ipopt", nlp_s)
sol_s = nlpsolver_s(x0 = [4, 8])


# Parallel NLP solving

F0 = ca.Function("F0",[x],[y])
F1 = ca.Function("F1",[x],[z])
s = ca.Function.conditional('s',[F0,F1],F0)
mapres = s.map([ca.DM(range(2)).T,ca.repmat(x,1,2)],"openmp")
fun_p = ca.Function("fun2",[x],[ca.sum2(mapres[0])])

f_p = fun_p(x)

nlp_p = {"x" : x, "f" : f_p}
nlpsolver_p = ca.nlpsol("nlpsolver", "ipopt", nlp_p)
sol_p = nlpsolver_p(x0 = [4, 8])


# Print results

print "x_opt_s = " + str(sol_s["x"])
print "f_opt_s = " + str(sol_s["f"])

print "x_opt_p = " + str(sol_p["x"])
print "f_opt_p = " + str(sol_p["f"])