feat: add constraint handling with feasibility-aware trial ranking
Add constraint support so that optimization problems with constraints (e.g., "model size < 100MB") prefer feasible solutions. Convention: constraint value <= 0.0 means feasible. - Add constraint_values field to Trial with set_constraints/getter - Add constraints field to CompletedTrial with is_feasible() method - Propagate constraints through complete_trial/prune_trial - Make best_trial() and top_trials() constraint-aware: feasible trials rank above infeasible, infeasible ranked by total violation
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@@ -2141,3 +2141,107 @@ fn test_into_iterator_preserves_insertion_order() {
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let ids: Vec<u64> = (&study).into_iter().map(|t| t.id).collect();
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assert_eq!(ids, vec![0, 1, 2]);
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}
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// =============================================================================
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// Tests: Constraint handling
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// =============================================================================
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#[test]
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fn test_is_feasible_all_satisfied() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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let mut trial = study.create_trial();
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trial.set_constraints(vec![-1.0, 0.0, -0.5]);
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study.complete_trial(trial, 1.0);
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let completed = study.best_trial().unwrap();
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assert!(completed.is_feasible());
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}
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#[test]
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fn test_is_feasible_one_violated() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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let mut trial = study.create_trial();
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trial.set_constraints(vec![-1.0, 0.5, -0.5]);
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study.complete_trial(trial, 1.0);
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let completed = study.best_trial().unwrap();
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assert!(!completed.is_feasible());
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}
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#[test]
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fn test_is_feasible_empty_constraints() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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let trial = study.create_trial();
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study.complete_trial(trial, 1.0);
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let completed = study.best_trial().unwrap();
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assert!(completed.is_feasible());
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}
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#[test]
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fn test_best_trial_prefers_feasible() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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// Infeasible trial with better objective
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let mut trial1 = study.create_trial();
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trial1.set_constraints(vec![1.0]);
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study.complete_trial(trial1, 0.1);
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// Feasible trial with worse objective
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let mut trial2 = study.create_trial();
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trial2.set_constraints(vec![-1.0]);
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study.complete_trial(trial2, 100.0);
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let best = study.best_trial().unwrap();
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assert_eq!(best.id, 1); // feasible trial wins
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assert_eq!(best.value, 100.0);
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}
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#[test]
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fn test_best_trial_feasible_by_objective() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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// Feasible, worse objective
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let mut trial1 = study.create_trial();
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trial1.set_constraints(vec![-1.0]);
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study.complete_trial(trial1, 10.0);
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// Feasible, better objective
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let mut trial2 = study.create_trial();
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trial2.set_constraints(vec![-0.5]);
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study.complete_trial(trial2, 2.0);
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let best = study.best_trial().unwrap();
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assert_eq!(best.id, 1); // lower objective wins among feasible
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assert_eq!(best.value, 2.0);
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}
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#[test]
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fn test_top_trials_ranks_feasible_above_infeasible() {
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let study: Study<f64> = Study::new(Direction::Minimize);
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// Infeasible, low violation
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let mut t0 = study.create_trial();
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t0.set_constraints(vec![0.5]);
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study.complete_trial(t0, 1.0);
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// Feasible, worst objective among feasible
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let mut t1 = study.create_trial();
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t1.set_constraints(vec![-1.0]);
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study.complete_trial(t1, 50.0);
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// Feasible, best objective among feasible
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let mut t2 = study.create_trial();
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t2.set_constraints(vec![-0.1]);
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study.complete_trial(t2, 5.0);
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// Infeasible, high violation
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let mut t3 = study.create_trial();
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t3.set_constraints(vec![3.0]);
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study.complete_trial(t3, 0.5);
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let top = study.top_trials(4);
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let ids: Vec<u64> = top.iter().map(|t| t.id).collect();
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// Feasible sorted by objective first (5.0, 50.0), then infeasible by violation (0.5, 3.0)
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assert_eq!(ids, vec![2, 1, 0, 3]);
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}
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