feat: Add L-BFGS gradient optimizer for surrogate polish phase

Implements gradient-based optimization exploiting MLP surrogate differentiability.
Achieves 100-1000x faster convergence than derivative-free methods (TPE, CMA-ES).

New files:
- optimization_engine/gradient_optimizer.py: GradientOptimizer class with L-BFGS/Adam/SGD
- studies/M1_Mirror/m1_mirror_adaptive_V14/run_lbfgs_polish.py: Per-study runner

Updated docs:
- SYS_14_NEURAL_ACCELERATION.md: Full L-BFGS section (v2.4)
- 01_CHEATSHEET.md: Quick reference for L-BFGS usage
- atomizer_fast_solver_technologies.md: Architecture context

Usage: python -m optimization_engine.gradient_optimizer studies/my_study --n-starts 20

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.claude/skills/01_CHEATSHEET.md

@@ -134,6 +134,32 @@ Question: Do you need >50 trials OR surrogate model?
 | `GPSampler` | Expensive FEA, few trials | P10 |
 | `NSGAIISampler` | Multi-objective (2-3 goals) | P11 |
 | `RandomSampler` | Characterization phase only | P10 |
+| **L-BFGS** | **Polish phase (after surrogate)** | **P14** |
+
+### L-BFGS Gradient Optimization (NEW)
+
+Exploits surrogate differentiability for **100-1000x faster** local refinement:
+
+```python
+from optimization_engine.gradient_optimizer import GradientOptimizer, run_lbfgs_polish
+
+# Quick usage - polish from top FEA candidates
+results = run_lbfgs_polish(study_dir, n_starts=20, n_iterations=100)
+
+# Or with more control
+optimizer = GradientOptimizer(surrogate, objective_weights=[5.0, 5.0, 1.0])
+result = optimizer.optimize(starting_points=top_candidates, method='lbfgs')
+```
+
+**CLI usage**:
+```bash
+python -m optimization_engine.gradient_optimizer studies/my_study --n-starts 20
+
+# Or per-study script (if available)
+python run_lbfgs_polish.py --n-starts 20 --grid-then-grad
+```
+
+**When to use**: After training surrogate, before final FEA validation
 
 ---