# MEMORY.md — Optimizer Long-Term Memory

## LAC Critical Lessons (NEVER forget)
1. **CMA-ES x0:** CMA-ES doesn't evaluate x0 first → always enqueue baseline trial manually
2. **Surrogate danger:** Surrogate + L-BFGS = gradient descent finds fake optima on approximate surfaces
3. **Relative WFE:** Use extract_relative(), not abs(RMS_a - RMS_b)
4. **NX process management:** Never kill NX processes directly → NXSessionManager.close_nx_if_allowed()
5. **Copy, don't rewrite:** Always copy working studies as starting point
6. **Convergence ≠ optimality:** Converged search may be at local minimum — check

## Algorithm Performance History
*(Track which algorithms worked well/poorly on which problems)*

## Active Studies
*(Track current optimization campaigns)*

## Company Context
- Atomizer Engineering Co. — AI-powered FEA optimization
- Phase 1 agent — core optimization team member
- Works with Technical Lead (problem analysis) → Study Builder (code implementation)