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Calculate hypervolume in HSSP using sum of contributions #6130

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Calculate hypervolume in HSSP using sum of contributions #6130

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@not522 not522 commented Jun 5, 2025

Motivation

The hypervolume determined by the selected points can be calculated as the sum of their contributions. This allows skipping the initial hypervolume calculation for each iteration in HSSP.

Description of the changes

  • Change to calculate hypervolume in HSSP using sum of contributions

Benchmark

  • master: 26.196007 sec
  • PR: 25.605701 sec
import optuna


def objective(trial: optuna.Trial) -> tuple[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


sampler = optuna.samplers.TPESampler(seed=42)
study = optuna.create_study(sampler=sampler, directions=["minimize"]*3)
study.optimize(objective, n_trials=1000)
trials = study.trials
print((trials[-1].datetime_complete - trials[0].datetime_start).total_seconds())

@not522 not522 added the enhancement Change that does not break compatibility and not affect public interfaces, but improves performance. label Jun 5, 2025
nabenabe0928
nabenabe0928 reviewed Jun 5, 2025
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Could you change here to np.maximum(0.0, contribs) so that any contribs will not be lower than max_contrib and add an inline note about this?
Such a situation can happen due to numerical instability.

optuna/optuna/_hypervolume/hssp.py

Line 75 in 5b6348d

return contribs

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@sawa3030 Could you check this PR?

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not522 commented Jun 6, 2025

Could you change here to np.maximum(0.0, contribs) so that any contribs will not be lower than max_contrib and add an inline note about this? Such a situation can happen due to numerical instability.

optuna/optuna/_hypervolume/hssp.py

Line 75 in 5b6348d

return contribs

It's OK, but does that situation cause any errors?

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Could you change here to np.maximum(0.0, contribs) so that any contribs will not be lower than max_contrib and add an inline note about this? Such a situation can happen due to numerical instability.

optuna/optuna/_hypervolume/hssp.py

Line 75 in 5b6348d

return contribs

It's OK, but does that situation cause any errors?

I am not sure how likely it is, but it may trigger some unwanted behavior.
For example, if all(contribs < 0) becomes True at some point, contribs will not be updated anymore due to the check if contribs[i] < max_contrib: continue.
Yet, there should (or can) still be rankings among contribs.
By removing np.maximum, we may lose such information, resulting in picking up indices based on the contribs right before all(contribs < 0) holds.

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@not522 @nabenabe0928 @sawa3030