![]() Each constraint also must be evaluated many times as well for each model parameter. The key is to remember that nonlinear optimizers rely on evaluating the objective function to minimize many times for each iteration to calculate numerical partial derivatives for each model parameter. Res = minimize(profit_model, x0, constraints=cons)Īdw_bids = pd.DataFrame(data=bids, index=None, columns=, dtype=None, copy=False).fillna(0)` X0 = np.arange(len(combined_campaign_tables)) cost_constraint = 2000įor i in range(0, len(combined_campaign_tables), 1):Ĭlicks = (x * float(combined_campaign_tables.ix)) + float(Ĭombined_campaign_tables.ix)Ĭombined_campaign_tables.ix) * float(Ĭombined_campaign_tables.ix)) - (clicks * x))Ĭlicks = ((x * float(combined_campaign_tables.ix)) + float(Ĭombined_campaign_tables.ix))Ĭons = () The last thing I want to do is take the lazy way out and post my code but I am hoping it answers questions rather than raising more. Thanks for reading.Įdit: Thank you for all your comments. Any reading materials on where to go from where would be greatly appreciated. Any dataframe over 200 rows takes several hours. ![]() ![]() We're using scipy.optimize (minimize) currently to optimize our Cost-per-Click bids in Adwords but as we add more campaigns the optimization problem essentially never finishes.
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