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1.How to calculate the global optimum p ∗ in the relative suboptimality plots and regret plots?
2.During your model training, do you use training data mini-batch or one by one?
The text was updated successfully, but these errors were encountered:
You can run some known algorithm such as Newton method or LBFGS or Gradient Descent for a long time then obtain p*, if the problem is convex. You can then use that p* in relative suboptimality plots or regret plots. For nonconvex problems, as a heuristic you can use the least function value attained by any of the algorithms (if you use sufficiently large number of iterations).
We used mini-batching in general, however, for regret plots we use one by one.
1.How to calculate the global optimum p ∗ in the relative suboptimality plots and regret plots?
2.During your model training, do you use training data mini-batch or one by one?
The text was updated successfully, but these errors were encountered: