#optional: gradient
def df(x):
r = zeros((3,2))
r[0,0] = 2*x[0]
r[0,1] = 2*x[1]
r[1,0] = 4*x[0]**3
r[1,1] = 4*x[1]**3
r[2,0] = 1
return r
# init esimation of solution - sometimes rather pricise one is very important
x0 = [1.5, 8]
#p = NLLSP(f, x0, diffInt = 1.5e-8, xtol = 1.5e-8, ftol = 1.5e-8)
# or
# p = NLLSP(f, x0)
# or
p = NLLSP(f, x0, df = df, xtol = 1.5e-8, ftol = 1.5e-8)
#optional: user-supplied gradient check:
p.checkdf()
#r = p.solve('scipy_leastsq', plot=1, iprint = -1)
#or using converter lsp2nlp:
r = p.solve('nlp:ralg', iprint = 1, plot=1)
#r = p.solve('nlp:ipopt',plot=1), r = p.solve('nlp:algencan'), r = p.solve('nlp:ralg'), etc
#(some NLP solvers require additional installation)
print 'x_opt:', r.xf # 2.74930862, +/-2.5597651
print 'funcs Values:', p.f(r.xf) # [-0.888904734668, 0.0678251418575, -0.750691380965]
print 'f_opt:', r.ff, '; sum of squares (should be same value):', (p.f(r.xf) ** 2).sum() # 1.35828942657
