Speed up HSSP 4D+ by using a decremental approach#6234
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y0z merged 3 commits intooptuna:masterfrom Aug 6, 2025
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(The unit in the table is second. 400 Trials per study.) Note The tri-objective problem took 3.9 seconds with the decremental approach, so I decided to use the incremental approach for the tri-objective problem. Benchmarking Codeimport optuna
def multi_objective(trial: optuna.Trial) -> tuple[float, float, float, float, float]:
x = trial.suggest_float("x", -5, 5)
y = trial.suggest_float("y", -5, 5)
return x**2 + y**2, (x - 2)**2 + (y - 2)**2, (x + 2)**2 + (y + 2)**2, (x + 2)**2 + (y - 2)**2 , (x - 2)**2 + (y + 2)**2
def objective(trial: optuna.Trial, n_objectives: int) -> tuple[float, ...]:
values = multi_objective(trial)
return values[:n_objectives]
n_objectives = 5
sampler = optuna.samplers.TPESampler(seed=0, multivariate=True)
study = optuna.create_study(sampler=sampler, directions=["minimize"]*n_objectives)
study.optimize(lambda trial: objective(trial, n_objectives), n_trials=400)
print((study.trials[-1].datetime_complete - study.trials[0].datetime_start).total_seconds()) |
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@not522 Could you review this PR? |
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Motivation
This PR is inspired by:
plot_hypervolume_history#6232Description of the changes
H(S ^ {i})instead ofH(S v {i})for 4+ objectives