With 10 trials and n_initial=5, PSO got 5 remaining evals for a
20-particle swarm. Only 5 of 20 particles were evaluated before the
budget observer killed the run — 75% of the swarm had no cost value.
PSO can't compute a proper gbest from partial data.
Fix: when remaining_trials < n_particles × 2, skip PSO entirely and
use additional LHS samples instead. LHS gives better space coverage
than an incomplete swarm iteration.
For 10 trials: 5 initial LHS + 5 additional LHS = 10 independent
space-filling samples. Much better than 5 LHS + 5 broken PSO particles.
PSO still runs when budget is sufficient (≥40 remaining for 20 particles).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Argmin defaults: inertia=0.72, cognitive=1.19, social=1.19
Problem: social >> inertia causes particles to collapse toward the
global best immediately. With only 1 LHS trial as the initial best,
the entire swarm clusters around that point and can't explore.
Every PSO trial was worse than the random LHS trial.
Fix: inertia=0.9 (high momentum, maintains exploration),
cognitive=1.5 (strong personal best memory),
social=0.8 (weak global pull, prevents premature convergence).
Applied to both sequential and parallel optimizer paths.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Major changes:
- Eliminate ALL runtime NVRTC: c51_loss + mse_loss kernels converted to
precompiled cubins with runtime num_atoms/v_min/v_max/branch params
- Fix evaluator SIGSEGV: rng_states/q_gaps sized for chunked batch (cn)
not n_windows; NULL pointer guard in action_select kernel
- Add trade_physics.cuh shared header for train/eval consistency
- Add equity circuit breaker (25% DD from peak) + margin-aware position cap
- Consolidate 46D→22D hyperopt search space, enable ensemble by default
- Fix trade counting: use exposure index not factored action
- Fix Calmar overflow: clamp to ±100 in kernel
- Softer CVaR penalty (cap 3.0 not 10.0) for undertrained models
- Fix win_rate display (ratio→percentage)
- Remove dead code (normalize_reward, calculate_completion_penalty)
- Add tracing subscriber to hyperopt test for visible metrics
- Per-chunk sync in evaluator for reliable error reporting
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Build-time #define was 256 but runtime config can be 128 (hidden_dim_base).
Caused CUDA_ERROR_ILLEGAL_ADDRESS in IQN trunk gradient. Now passed as
kernel params matching the STATE_DIM parameterization pattern.
Also: warn→error for training failures in optimizer, eprintln for debugging.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
increment_trial() was never called — budget enforcement was broken because
TrialBudgetObserver had its own internal Arc<AtomicUsize> disconnected from
the trial_counter used by evaluate_point() and cost(). With num_trials=2,
PSO ran 20 particle evaluations instead of stopping at 2.
Now TrialBudgetObserver::new() takes the caller-owned Arc<AtomicUsize> so
all evaluation paths (LHS via evaluate_point + PSO via cost()) share one
counter. ObjectiveFunction::cost() and ParallelObjectiveFunction::cost()
increment trial_counter directly (fetch_add SeqCst) and check budget inline;
observe_iter() reads the same counter to terminate PSO between iterations.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
NaN/divergence during training is a valid PSO/TPE outcome — it means
the sampled hyperparameters are unstable. Score failed trials with 1e6
penalty so the optimizer learns to avoid that region, instead of
aborting all remaining trials.
The PSO/argmin paths already handled this (lines 1118, 1261). Only
the shared evaluate_point (TPE path) propagated errors as fatal.
Tested: 6 trials × 20 epochs on RTX 3050 — 2 NaN trials scored as
penalty, optimizer completed all 6 trials in 597s.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add backticks to type names in doc comments (doc_markdown)
- Mark eligible functions as const fn (missing_const_for_fn)
No behavior changes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
