Noisy Continuous Black-Box Optimizer Design

Your task is to design a reproducible, noise-aware optimizer that minimizes 10-dimensional continuous black-box objectives with smooth periodic, multimodal structure. You inherit a weak uniform-random baseline, and every run uses the box [-5, 5]^10 with a strict budget of 120 objective queries. Your submitted optimizer is re-run on disjoint sealed instances.

Hard Constraints

• Edit only /app/methods/main/; solver.py must define Optimizer.
• Use only the Python standard library and NumPy 2.2.6.
• The required constructor is Optimizer(dim, lower, upper, budget, seed, rng). No task-information argument is supplied.
• ask(n) must return a finite NumPy-compatible matrix with 1 through n rows, exactly dim columns, and all coordinates within the supplied bounds.
• The verifier owns the objective and query counter. Extra returned points do not increase the 120-query budget.
• Import failures, crashes, malformed output, non-finite values, and out-of-bounds proposals invalidate the complete submission.
• The submitted process cannot read or modify trusted evaluator assets and has no verifier network access.

Runtime budget.

Submitted optimizer code receives a 120-second aggregate soft budget across the complete sealed suite, not a separate 120 seconds for every run. The suite contains 400 independent optimizer runs, so each run must average about 0.3 seconds. There is no five-second scoring cutoff on every individual ask or tell response; when the aggregate soft budget expires, the verifier stops requesting new work and evaluates the best completed state. A stalled process can still be terminated at a sealed safety cap. Use bounded, vectorized per-query work; repeated dense refits or hundreds-wide candidate scans at every observation are unlikely to fit. This budget applies to sealed execution, not to your research time.

What You Have

• /app/data/visible.json contains twelve public development instances from the same noisy continuous family; sealed instances are distinct.
• /app/methods/main/solver.py is a uniform-random baseline.
• /app/selfcheck.py evaluates the same higher-is-better normalized anytime/final metric family used by the sealed evaluator on 20 deterministic runs per visible instance. It also reports diagnostic components and latent final-objective summaries.
• The supplied rng is np.random.default_rng(seed) and should drive all randomness for deterministic replay.
• The verifier repeatedly calls ask(n), evaluates the returned points, and calls tell(X, y) (or tell(X, y, metadata) if accepted). Each value in y is an observed noisy loss, so lower is better even though the aggregate self-check score is higher-is-better.
• A positive integer self.batch may request a preferred batch size; the verifier negotiates and caps it to the remaining budget.

What You Submit

Submit optimizer code, not a one-shot point or precomputed answer. The entire submitted optimizer must be self-contained in /app/methods/main/solver.py, which must define the required Optimizer class; sibling modules are not copied to the trusted verifier.

How It Is Judged

The trusted parent evaluates your optimizer on sealed instances and fixed seeds. It records authoritative latent best-so-far traces, aggregates them robustly across seeds, and combines anytime quality with final-query quality under the same metric definition exposed by the visible self-check. Higher normalized quality is better; sealed instances, calibration assets, and evaluator internals remain hidden.