[Dip] dippy.DecompVar and dippy.Solve Options

[Manser, Mark]

Hello Dip/Dippy Community,

I am new to implementing decomposition algorithms and Dip/Dippy and am trying to figure out how the platform works.  I have a couple of general questions:

1)  On pg. 15 of http://coral.ie.lehigh.edu/~ted/files/papers/RAMP12.pdf it states "The return value of relaxed solver is a list of DecompVar objects having negative reduced cost."  This appears to be the case for the cutting_stock.py and bin_pack_decomp_func.py for example, but not the case for facility_location.py nor gap.py.  When is it necessary to return a list of dippy.DecompVar instances, and when does it suffice to return a regular sparse dictionary of variables?  Also, why isn't the reduced cost being returned in gap.py when the dippy.Solve option is:    'doPriceCut': '1'.

In cutting_stock.py, the function 'relaxed_solver' ends by returning a list containing a dippy.DecompVar instance:
    dv = dippy.DecompVar(var_values, rc - convexDual, 1)
    return [dv]

In bin_pack_decomp_func.py, the function 'solve_subproblem' ends by returning a list containing a dippy.DecompVar instance:
    dv = dippy.DecompVar(var_values, rc - convexDual, waste)
    return [dv]

In facility_location.py, the function 'solve_subproblem' ends by returning a list containing a regular dictionary:
    var_values = dict([(avars[i], 1) for i in solution])
    var_values[use_vars[loc]] = 1
    ...
    return [var_values]

In gap.py, the function 'solve_subproblem' ends by returning a list containing a regular dictionary:
    var_values = dict([(var[i], 1) for i in solution])
    ...
    return [var_values]

2)  I am confused about the effect of the following dippy.Solve options:

    'doCut',
   'generateCuts',
    'generateInitVars',
    'doPriceCut',
    'CutCGL'

For some context, in facility_location.py the following code exists:

    dippyOpts = {}
    algo = 'PriceCut'
    if len(sys.argv) > 1:
        algo = sys.argv[1]
    if algo == 'PriceCut':
        dippyOpts['doPriceCut'] = '1'
        dippyOpts['CutCGL'] = '1'
    elif algo == 'Price':
        dippyOpts['doPriceCut'] = '1'
        dippyOpts['CutCGL'] = '0'
    else:
        dippyOpts['doCut'] = '1'

Are the last 3 conditionals how one would specify 'Branch-and-price-and-cut', 'branch-and-price', and 'branch-and-cut' respectively?
Would setting just dippyOpts['doCut'] = '0' result in 'Branch-and-Bound'?

3)  I have a model built for a real-world application, but it's not behaving the way I expected it would.  There are potentially a number of things wrong and I would like to discuss it with someone knowledgeable.  If one of the devs has time for a quick review, could you reply to this message?

Also, I am unable to access the online doxygen documentation at http://www.coin-or.org/Doxygen/Dip/ as listed on the github page.  I am receiving "403 Forbidden You don't have permission to access /Doxygen/Dip/ on this server."  I am not sure if this is on my end or if it's affecting everyone.

- Mark


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