packages/libraries required to run the code are listed in requirements.txt.

runMaxSAT.ipynb notebook shows a way to run the code for MaxSAT synthetic function (60 binary variables)

bodi/run_experiment.py is the main entry point for the code

BODi algorithm is run by calling run_experiment method from bodi/run_experiment.py

main parameters required for run_experiment are:

• n_prototype_vectors: no of dictionary elements/anchor points used in the surrogate model, we recommend keeping it to 64 or 128.
• evalfn: name of the objective-function/problem to optimize. For a candidate definition, please see LABS/Ackley53/MaxSAT60/SVM in bodi/test_functions.py.
• max_evals: maximum number of evaluations/calls to the objective function.
• n_initial_points: no. of input points to initialize the BO surrogate model, we recommended keeping it to 20 unless the evaluation budget is more expensive.
• n_binary: no of binary variables in the input for the given objective, for e.g. MaxSAT has 60 binary variables.
• n_continuous: no of continuous/ variables in the input for the given objective, for e.g. Ackley53 has 3 continuous variables.
• n_replications: number of times to rerun the code, this is mostly useful for generating multiple runs and getting mean and error bars of the BO performance.
• batch_size: no of batch evaluations in each BO round.

simple way to add a new objective-function/problem is by creating a class similar to LABS or Ackley53 in bodi/test_functions.py and then call it in the run_experiment method of run_experiment.py (line 98-113).

acquisition function optimization routines for both binary/mixed spaces are defined in bodi/optimize.py.

dictionary kernel with both diverse random and binary wavelet dictionaries are defined in bodi/dictionary_kernel.py.