Manuel Raimann
47b5f9cec8
- Add blanket `impl Objective<V> for Fn(&mut Trial) -> Result<V, E>` so closures work directly with `optimize` - Rewrite optimize, optimize_async, optimize_parallel to accept `impl Objective<V>` with before_trial/after_trial hooks - Remove optimize_with, optimize_with_async, optimize_with_parallel - Remove max_retries and retry logic from Objective trait - Add explicit closure type annotations for HRTB inference - Convert FnMut test closures to Fn via RefCell/Cell
50 lines
1.5 KiB
Rust
50 lines
1.5 KiB
Rust
//! Multi-objective optimization — optimize competing objectives simultaneously.
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//!
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//! `MultiObjectiveStudy` returns the Pareto front: the set of solutions where
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//! no objective can be improved without worsening another.
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//!
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//! Run with: `cargo run --example multi_objective`
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use optimizer::multi_objective::MultiObjectiveStudy;
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use optimizer::prelude::*;
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fn main() -> optimizer::Result<()> {
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let study = MultiObjectiveStudy::new(vec![Direction::Minimize, Direction::Minimize]);
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let x = FloatParam::new(0.0, 1.0).name("x");
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// Classic bi-objective: f1(x) = x², f2(x) = (x-1)²
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// The Pareto front is the curve where improving f1 worsens f2.
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study.optimize(50, |trial: &mut optimizer::Trial| {
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let xv = x.suggest(trial)?;
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let f1 = xv * xv;
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let f2 = (xv - 1.0) * (xv - 1.0);
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Ok::<_, optimizer::Error>(vec![f1, f2])
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})?;
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let front = study.pareto_front();
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println!(
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"Ran {} trials, Pareto front has {} solutions:",
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study.n_trials(),
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front.len(),
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);
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let mut sorted = front.clone();
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sorted.sort_by(|a, b| a.values[0].partial_cmp(&b.values[0]).unwrap());
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for (i, trial) in sorted.iter().take(5).enumerate() {
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println!(
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" {}: x={:.3}, f1={:.4}, f2={:.4}",
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i + 1,
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trial.get(&x).unwrap(),
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trial.values[0],
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trial.values[1],
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);
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}
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if sorted.len() > 5 {
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println!(" ... and {} more", sorted.len() - 5);
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}
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Ok(())
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}
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