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Table 4. Search space, priors, and expert configuration for the MD Grid application. The default value for each parameter is shown in bold.

From: Bayesian Optimization with a Prior for the Optimum

Parameter name

Type

Values

Expert

Prior

loop_grid0_z

Ordinal

[1, 2, \(\ldots \), 15, 16]

1

[0.2, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]

loop_q

Ordinal

[1, 2, \(\ldots \), 31, 32]

8

[0.08, 0.08, 0.02, 0.1, 0.02, 0.02, 0.02, 0.1, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.1, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]

par_load

Ordinal

[1, 2, 4]

1

[0.45, 0.1, 0.45]

loop_p

Ordinal

[1, 2, \(\ldots \), 31, 32]

2

[0.1, 0.1, 0.1, 0.1, 0.05, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]

loop_grid0_x

Ordinal

[1, 2, \(\ldots \), 15, 16]

1

[0.2, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]

loop_grid1_z

Ordinal

[1, 2, \(\ldots \), 15, 16]

1

[0.2, 0.2, 0.1, 0.1, 0.07, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03, 0.03]

loop_grid0_y

Ordinal

[1, 2, \(\ldots \), 15, 16]

1

[0.2, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]

ATOM1LOOP

Categorical

[false, true]

true

[0.1, 0.9]

ATOM2LOOP

Categorical

[false, true]

true

[0.1, 0.9]

PLOOP

Categorical

[false, true]

true

[0.1, 0.9]