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Frequently Asked Questions


How to turn text output (all those "starting solver ...", "time elapsed ...", etc) off?

Use iprint = 0 (final output only) or iprint < 0 (btw it's mentioned in NLP docstring). In future warnings are intended to be shown if iprint >= -1. However, some solvers like ALGENCAN have their own text output system, that's hard to suppress, it requires using different approach like, for example, the one.

Does it have MATLAB fmincon equivalent?

Yes, you should take a look at the solvers from NLP

How to handle maximization problems?

Use p.goal = 'max' or 'maximum' (since v 0.27 you can also use r = p.maximize(...)). However, this is valid only for some problem types (LP, MILP, NLP, NSP, some other).

Can I try ralg or other nonlinear solvers with AMPL models?

If you have AMPL and nlpy installed, you can use its module amplpy to get objective and constraints values and derivatives. However, currently OpenOpt-connected solvers can't handle 2nd derivatives info (w/o some tricks). IIRC pyomo also can deal with AMPL models.

How can I handle fixed variables?

  • Some mature solvers like ALGENCAN, IPOPT can handle it rather effectively by themselves
  • Currently for ralg and some others only FuncDesigner can provide effective handling (neither lb<=x<=ub nor Aeq x = beq constraints can't do it so effectively).

I have same parts of code involved in objective function and/or nonlinear constraints and/or their derivatives. Can I somehow avoid same calculations?

You can do it very effectively if your model is coded in FuncDesigner.

Why sometimes some r.evals fields are non-integer, like r.evals['c'] = 150.5?

Some solvers (ralg, algencan) in some points along trajectory call derivatives for only several non-linear constraints (those that are active, i.e. more than zero or contol, or moreover, only maximum violated constraint). So the number is sum of (number of required constraints to be calculated)/(number of all constraints of the type(eq or ineq)). So, if you have a function that calculates all constraints together and that one is costly, non-integer value means the solver could take some benefits would you divide those ones into separate funcs (see also NLP, constrained solvers last column).
Let me also note that using FuncDesigner provides much more precise evaluations number control, each oofun has counters "evals" (how many times the func has been called with new input) and "same" (how many times previous value has been substituted, because oofun has been called with same input, for example from f and df, or from f and c). So you can observe my_oofun.evals and my_oofun.same fields.

Are there any plans for providing 2nd derivatives in FuncDesigner?

It will be hardly done, at least in nearest future (first of all - lack of financial support). As you probably know, there are software packages that allows getting any-order derivatives, and I decided to move into this direction, but it's not that easy to implement, and it's beyond my current needs.

Are there any ralg parameters available for modification?

Yes, but they are undocumented yet.

Are you interested in collaboration with our project(s) or the paid work?

Send us more info, we will take a look.

(to be continued)

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