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* Rugnux: Processes serial crystallography data from XFELs - SACLA (MPCCD), PAL-XFEL and European XFEL (CXI), LCLS (XTC) and SwissFEL Alvra (JUNGFRAU with a CrystFEL module geometry) - with several files or Jungfraujoch master files merged in one run (`rugnux *_master.h5`). * Rugnux: Stills processing takes a known unit cell and space group as a starting point, finds the beam centre (and, with a cell, the detector distance) from the images, detects the saturation level in other facilities' files and refines the placement of individual detector panels. * Rugnux: The stills report gives R_split and CC1/2 from frame half-sets, comparable with CrystFEL. * jfjoch_viewer: Opens the same XFEL file formats. * Validation: A stills battery checks Rugnux against deposited structures from SACLA, PAL-XFEL, LCLS, European XFEL and SwissFEL. * Rugnux: More reliable convergence of rotation scaling, and live molecular replacement starts as soon as the model is uploaded. * jfjoch_broker: Initialize clears the previous dataset's bearer tokens, image buffer and live Rugnux status, and a slow CUDA JIT cache is reported at start. Reviewed-on: #88 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
52 lines
2.0 KiB
C++
52 lines
2.0 KiB
C++
// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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#include "AndersonMixing.h"
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std::vector<double> AndersonMixing::Next(const std::vector<double> &x, const std::vector<double> &fx,
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const std::vector<double> &w) {
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// Anderson acceleration, type II, after Walker & Ni (2011) SIAM J. Numer. Anal. 49, 1715-1735
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// (Anderson (1965) J. ACM 12, 547-560): with dX and dR the differences of the states and of the
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// residuals of consecutive rounds, gamma = argmin |r - dR gamma|_w and the next state is
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// F(x) - (dX + dR) gamma.
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const int n = static_cast<int>(x.size());
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const Eigen::Map<const Eigen::VectorXd> xv(x.data(), n), fxv(fx.data(), n), wv(w.data(), n);
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const Eigen::VectorXd r = fxv - xv;
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std::vector<uint8_t> support(n);
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for (int i = 0; i < n; ++i) support[i] = w[i] > 0.0 ? 1 : 0;
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const double r_norm = (wv.array() * r.array().square()).sum();
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if (stopped || (x_last.size() == n && r_norm > r_norm_last)) {
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stopped = true;
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return fx;
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}
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if (x_last.size() == n && support == support_last) {
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dx.push_back(xv - x_last);
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dr.push_back(r - r_last);
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if (static_cast<int>(dx.size()) > depth) {
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dx.pop_front();
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dr.pop_front();
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}
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} else if (!dx.empty()) {
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dx.clear();
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dr.clear();
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++restarts;
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}
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x_last = xv;
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r_last = r;
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support_last = std::move(support);
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r_norm_last = r_norm;
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std::vector<double> out = fx;
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const int m = static_cast<int>(dr.size());
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if (m == 0) return out;
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const Eigen::VectorXd sqrt_w = wv.cwiseMax(0.0).cwiseSqrt();
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Eigen::MatrixXd M(n, m);
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for (int j = 0; j < m; ++j) M.col(j) = sqrt_w.cwiseProduct(dr[j]);
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const Eigen::VectorXd gamma = M.colPivHouseholderQr().solve(sqrt_w.cwiseProduct(r));
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for (int j = 0; j < m; ++j)
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for (int i = 0; i < n; ++i)
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if (w[i] > 0.0) out[i] -= gamma[j] * (dx[j][i] + dr[j][i]);
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return out;
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}
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