(tnc is scipy.optimize NLP solver for box-bounded problems, requires scipy installed)

example of tnc usage:

----------------------------

from scikits.openopt import NLP

from numpy import *

N = 15

ff = lambda x: ((x-15)**4).sum() + 64*((x-1)**2).sum()

p = NLP(ff, cos(arange(N)))

p.lb = arange(N)

p.ub = 5 + arange(N)

r = p.solve('scipy_tnc')

----------------------------

So it should yield something like

istop: 4 (|| F[k] - F[k-1] || < funtol)

Solver: Time elapsed = 0.12 CPU Time Elapsed = 0.12

objFunValue: 10370.1398294 (feasible)

r.xf should be equal to

array([ 5. , 6. , 7. , 8. ,

8.72421634, 8.72421837, 8.72421093, 8.72420843,

8.72420744, 9. , 10. , 11. ,

12. , 13. , 14. ])

if N = 150

lincher:

itn 37 : Fk= 887272370.281 maxResidual= 5.24576648786e-09

Solver: Time elapsed = 4.16 CPU Time Elapsed = 4.01

objFunValue: 887272370.281 (feasible)

scipy_tnc:

istop: 4 (|| F[k] - F[k-1] || < funtol)

Solver: Time elapsed = 3.02 CPU Time Elapsed = 2.93

objFunValue: 887273034.135 (feasible)

maxResidual = 0

So, despite tnc has a little bit worst objFun value, it solve the example much quicker and is more suitable for box-bounded problems - of course, because it's intended for that ones and only for that ones, while lincher handles all constraints.

On the other hand, I'm not fond of CVXOPT QP solver, using better one could significantly increase lincher speed.

However, this is the only one example I had tested.

Maybe tomorrow I'll connect and publish lbfgsb and cobyla results.

----------------------------

+ today some examples to lincher have been written, I'll commit them tomorrow after minor changes.

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