- New docs/CPU_DATA_ANALYSIS_REFINEMENT.md (§15): the ML amplitude target and its FFT gradient,
scale/robust fit/fine solvent mask, D and the variance (work/free), the embedded Monomer Library
subset and topology, geometry/metal/KL-B restraints, the minimiser, the weight trials, refined maps,
blobs, GPU/CPU, and a "Not yet" list; with its own References. Linked from TECHNICAL.md and
CPU_DATA_ANALYSIS.md; §14 no longer says the structure is never refined.
- RUGNUX_REPORT.md: a Refinement (REFINE_*) section - every REFINE_ and REFINE_SCORE_ key, the
--mode refine input keys and the _blobs.json fields.
- RUGNUX_MODEL_AND_REFERENCE.md: "What it does not do yet"; RUGNUX_ADVANCED.md: --model mentions the
refinement, --no-refine as in the usage, a --mode refine options table; GPU_DATA_ANALYSIS.md: the
refinement's CUDA engines and the missing Metal path.
- ACKNOWLEDGEMENT.md: Brunger, Karplus & Petsko 1989 (gradient-balanced starting weight), Nocedal 1980
(two-loop recursion), Charbonnier et al. 1997 (pseudo-Huber), Jiang & Brunger 1994 (flat solvent
mask), Kullback & Leibler 1951; all DOIs checked against Crossref.
- In-source credit lines: the weight balancing (replacing a "Brunger (1992)-style" line that named the
R-free paper), Nocedal 1980, Charbonnier 1997, Jiang & Brunger 1994, Emsley et al. 2010 at
FindDensityBlobs, Read 1986 at the refined maps. Comments only.
- docs/THIRD_PARTY_NOTICES.md regenerated from the root file with update_version.sh's own step (it
lacked the CCP4 Monomer Library row).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit 09375dba351bae0cc73e9a6f0f79455dc92b3a10)
BulkSolventMask::MaskGrid, documented as "sizes and spacing set, values not allocated", allocated and zeroed the
whole fine mask grid (up to 2^26 floats) - in BulkSolventMask's constructor and in every GPU engine's, i.e. once
more for each weight-trial slot, only for the grid's size. It now takes good_grid_size() as set_size_from_spacing()
does, and the CPU engine's Fmask() allocates its copy. rugnux_refine_test, RTX 5080, -N 8: 9yl4 8.6 -> 7.8 s,
7d98 4.85 -> 4.0 s (engine 0.66 -> 0.16 s, slots 0.60 -> 0.23 s on 9yl4); the high-resolution sets, whose mask
grid is small, are unchanged.
RefinementMinimiser keeps s.y of every pair it stores and Direction() takes rho and gamma from it instead of two
more dot products per pair per call (the same Dot() of the same vectors): Direction's CPU time -22% on 9yl4; no
wall change with the trials side by side, where other slots fill the card meanwhile.
Bitwise: refined CIF identical on 5reo, 5f6m, 9yl4, 7d98, a 5f6m MD start (GPU) and 6o2h (CPU engine).
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit 6f5caf5e1b1183a752acc5cca78bfb7ef002b12a)
Each macro-cycle of a weight trial used to start a fresh Ceres solve: the L-BFGS memory was dropped every
10 (positions) or 5 (B) iterations and every cycle began with a steepest-descent step. RefinementMinimiser
keeps one memory for the positions and one for the B factors across a trial's macro-cycles (the target's
scale and likelihood parameters are refitted in between, which the memory tolerates), with a backtracking
Armijo line search and Oren-Shanno scaling of a diagonal initial inverse Hessian.
The diagonal: for B, the X-ray curvature per atom - the likelihood's curvature in |F_model| per resolution
shell against the atom's (s^2/4)^2 f^2 exp(-B s^2/2), calibrated once per trial by a central-difference
probe along a random direction - plus the B restraints'. For the positions none: tried with the restraints'
Gauss-Newton diagonal (bonds and angles, ~1e4 per axis against the X-ray's ~1e2) it slowed the minimiser,
since that stiffness is in directions that move bonded atoms together.
Same budget (10 cycles x 10 + 5 iterations, same ladder), median R-free (REFMAC 0-cycle scorer) from the
refinement battery's MD starts, base -> this: 5f6m (8 starts) 0.250 -> 0.210, 8dqb (8) 0.387 -> 0.345,
7arr 0.383 -> 0.356, 11if 0.325 -> 0.295, 5reo 0.316 -> 0.326 (CA RMSD 0.48 -> 0.43), 7d98 0.416 -> 0.393;
REFMAC converged: 0.168 / 0.284 / 0.311 / 0.251 / 0.261 / 0.361. Deposited models (14 parity sets): R-work
lower, R-free +0.0035 on average (their free sets were fitted to; the minimiser now moves the bottom rung
further). Time unchanged.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit dfba4c1bd3c9345f31b64fd1cadc805810960825)
leonarski_f