###############################################################
lines, results = [], {}
for j, solver in enumerate(solvers):
p = NLP(ff, startPoint, xlabel = Xlabel, gtol=gtol, diffInt = diffInt, ftol = ftol, maxIter = 1390, plot = PLOT, color = colors[j], iprint = 10, df_iter = 4, legend = solver, show=False, contol = contol, maxTime = maxTime, maxFunEvals = maxFunEvals, name='NLP_bench_1')
p.constraints = [c1<0, c2<0, h1.eq(0), h2.eq(0), x > lb, x< ub]
#p.constraints = h1.eq(0)
#p._Prepare()
#print p.dc(p.x0)
#print h1.D(startPoint)
#print h2.D(startPoint)
#continue
if solver =='algencan':
p.gtol = 1e-2
elif solver == 'ralg':
pass
#p.debug = 1
#p.debug = 1
r = p.solve(solver)
for fn in ('h','c'):
if not r.evals.has_key(fn): r.evals[fn]=0 # if no c or h are used in problem
results[solver] = (r.ff, p.getMaxResidual(p.xk), r.elapsed['solver_time'], r.elapsed['solver_cputime'], r.evals['f'], r.evals['c'], r.evals['h'])
if PLOT:
subplot(2,1,1)
F0 = ff(startPoint)
lines.append(plot([0, 1e-15], [F0, F0], color= colors[j]))
contol=contol,
maxTime=maxTime,
maxFunEvals=maxFunEvals,
name="NLP_bench_1",
)
p.constraints = [c1 < 0, c2 < 0, h1.eq(0), h2.eq(0), x > lb, x < ub]
# p.constraints = h1.eq(0)
# p._Prepare()
# print p.dc(p.x0)
# print h1.D(startPoint)
# print h2.D(startPoint)
# continue
if solver == "algencan":
p.gtol = 1e-2
elif solver == "ralg":
pass
# p.debug = 1
# p.debug = 1
r = p.solve(solver)
for fn in ("h", "c"):
if not r.evals.has_key(fn):
r.evals[fn] = 0 # if no c or h are used in problem
results[solver] = (
r.ff,
p.getMaxResidual(p.xk),
r.elapsed["solver_time"],
r.elapsed["solver_cputime"],
p.h = lambda x: (1e4*(x[-1]-1)**4, (x[-2]-1.5)**4)
# dh(x)/dx: non-lin eq constraints gradients (optional):
def DH(x):
r = zeros((2, p.n))
r[0, -1] = 1e4*4 * (x[-1]-1)**3
r[1, -2] = 4 * (x[-2]-1.5)**3
return r
p.dh = DH
p.contol = 1e-3 # required constraints tolerance, default for NLP is 1e-6
# for ALGENCAN solver gtol is the only one stop criterium connected to openopt
# (except maxfun, maxiter)
# Note that in ALGENCAN gtol means norm of projected gradient of the Augmented Lagrangian
# so it should be something like 1e-3...1e-5
p.gtol = 1e-5 # gradient stop criterium (default for NLP is 1e-6)
# see also: help(NLP) -> maxTime, maxCPUTime, ftol and xtol
# that are connected to / used in lincher and some other solvers
# optional: check of user-supplied derivatives
p.checkdf()
p.checkdc()
p.checkdh()
# last but not least:
# please don't forget,
# Python indexing starts from ZERO!!
p.plot = 0