thanks for this great project, but I have one question:

When will it be possible to use OpenOpt for optimal control problems? I have seen, there is one empty side for OCP at the project homepage so hopefully it will be released soon ;-)

Best Regards,

Qasi

if you would like to get feedback faster and in detailed properties, first of all please provide in your first post and your forum entry detailed information about yourself and where/how our software is used, e.g. "Dr.-Ing. Nils Wagner, institute of mechanics, Stuttgart, Germany, use OpenOpt for eigenvalue problems". We intend put some of them to Applications webpage, provided you don't directly state otherwise of course.

Would you do something from our Appeal, it would also improve quality of our answer.

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

Regards, Dmitrey.

Thanks very much for your help!]]>

Given that the ASA C code is available, is there any chance that you would consider adding ASA into the list of available GLP solver?

]]>I am using OpenOpt with the *ralg* and *scipy_lbfgsb* solvers for some relatively large problems with 200—2500 parameters. Every evaluation of the objective function (or derivative) takes a couple of seconds, so that the whole optimisation runs for a week or two. Part of the calculations run on a Linux cluster, which unfortunately limits the job runtime to two days.

I would like to restart an optimisation from where it was stopped. Using the best previous solution as a starting point for a new run works fine for *scipy_lbfgsb*, but *ralg* takes very many function and derivative evaluations before it gets back on track (up to a day's worth of calculations!).

Is there a way to save the internal state of the solver and use that to restart an optimisation run from where it was stopped?

Your help would be greatly appreciated.

Ingo

I am dealing with an optimization problem where lb and ub are always available. I'd like to compare smart GLP solvers (galileo, pswarm, de) against a plain grid search within the given bounds. Is grid search available somewhere in OpenOpt? Shall I implement it myself?

Kind Regards,

Emanuele

]]>have you any QCQP solver available or any plans for it?

By the way, scipy.optimize docstrings point to deprecated scikits.openopt yet.

Cheers, Alex.

]]>I wrote an NLP in OpenOpt, which was before the discretization an optimal control problem. Everything worked nice, but it is a little bit difficult to have all the indices of x in mind when writing your OCP.

So is there a possibility to write something like:

**x = [y,u]** ?

Maybe the question is difficult to understand, so I paste the important parts of the NLP i have written:

```
# Optimal Control Problem
# minimize integral_from_0_to_1 of 0.5*u(t)**2 - y(t)
# subject to y_dot(t) = u(t) and y(0) = 1
N = 200
TN = 100
T = 1.0
ht = T / TN
# objective function:
f = lambda x: ht*((0.5*x[TN:N-1]**2).sum() - x[0:TN-1].sum()) # difficult to define cost functional
# h(x) = 0 constraints
def h(x):
r = []
r.append(x[0] - 1)
for i in range(0,TN-1):
r.append(x[i+1] - x[i] - ht*x[i+TN]) # difficult to look at the problem
return r
# now i have to get back the real state and control for plotting
y = res.xf[0:TN-1] # this is the state
u = res.xf[TN:N-1] # this is the control
t = arange(0, T-ht, ht)
```

So you can see, in the cost functional and in the constraints it is difficult to handle with the indices of x.

Can you help me with this?

]]>I've got a simple question. I've just found python-glpk on the web site,

http://www.dcc.fc.up.pt/~jpp/code/python-glpk/

My question is how does it relate to the version interfaced through cvxopt?

Maybe simpler use and installation? Thank you, --Evgeni.]]>

cheers

]]>I have been working in an Portfolio Optimization Toolbox; currently I'm using as optimization engines the MOSEK, CPLEX and GAMS tools but, I want to use it to teach, so I need to switch into a "free optimization tool" like OpenOpt...

My toolbox takes some "raw data" and writes an standard MPS Format File (or LP Format) and this File is introduced into the Optimization Model, solved and then the solution file must be read in order to show the results in a GUI...

So, in order to implement OpenOpt as the "free optimization engine" what I need to know is how to modelate a tipical MILP problem so this would help me to write a kind of "filter" **lp2OpenOpt** and **mps2OpenOpt** to be included in the toolbox.

Because I'm new in this Forum and my knowledge of Phyton is limited, I need some help in order to convert a typical LP format (I choose LP format because it is more intuitive than the MPS) into OpenOpt format to be ready-to-run whit Python...

Can some member of the Forum give me a help?

Thank you in advance!!!!!

==================================================================

LP Format:

(All the variables are binary).

\ A typical example

minimize

obj: + 0 b1 + 0 b2 + 0 b3 + 0 b4 + 0 b5 + 0 b6 + x7

subject to

c1: - 5e+8 b1 - 5e+8 b2 - 5e+8 b3 - 2.3e+1 b4 - 2.2e+1 b5 - 1.9e+1 b6 + x7 = 0

c2: + b1 + b4 = 1

c3: + b2 + b5 = 1

c4: + b3 + b6 = 1

bounds

0 <= b1 <= 1

0 <= b2 <= 1

0 <= b3 <= 1

0 <= b4 <= 1

0 <= b5 <= 1

0 <= b6 <= 1

x7 free

general

b1 b2 b3 b4 b5 b6

end

==================================================================

Equivalent MPS Format:

* MPS File wrote by Grid2MPS from CombBids_Server_V102Beta

*

* Version: V.1.0.0.

* Date: June 9, 2009

* Time: 22:00 hrs (GMT-6)

* File generated at: Sun Jun 21 21:33:43 2009

*

NAME Generic_MPS_File

*

* original model was minimizing

*

ROWS

N obj

E c1

E c2

E c3

E c4

COLUMNS

MARK1 'MARKER' 'INTORG'

b1 c1 -500000000.00

b1 c2 1

b2 c1 -500000000.00

b2 c3 1

b3 c1 -500000000.00

b3 c4 1

b4 c1 -23.00

b4 c2 1

b5 c1 -22.00

b5 c3 1

b6 c1 -19.00

b6 c4 1

MARK1 'MARKER' 'INTEND'

x7 obj 1

x7 c1 1

RHS

rhs c2 1

rhs c3 1

rhs c4 1

BOUNDS

UP bnd b1 1

UP bnd b2 1

UP bnd b3 1

UP bnd b4 1

UP bnd b5 1

UP bnd b6 1

FR bnd x7

ENDATA

A new version of OpenOffice.org (free MS Office alternative) has been recently released, this one has Python 2.6 included.

From what I know, the only one free nonlinear solver connected to OOo Calc is unconstrained and premature (as it is mentioned here: "Currently a draft implementation of Quasi-Newton with BFGS update is included to optimize unconstrained non-linear models. But this algorithm is still not very robust and needs massive improvement"), while lots of users and I would be interested in general constrained local and other NonLinear solvers, as well as in Matrix Problems.

Thus, has anybody willling to connect OpenOpt to OOo Calc?

I had published some letters like this one in OOo mail lists (I'm not affiliated with OOo), but they say there will be hardly any volunteers revealed. Since those lists require registration, I guess it's better to discuss it here.

Cheers,

JKR.