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13 results for “numerical optimization”
Optimal Displacement Increment for Numerical Frequencies (Dataset)
<p>1<em>H</em>-pyrrolo[3,2-<em>h</em>]quinoline [Gorski, 2012] was optimized in ORCA v3.0.3 [Neese, 2012; http://orcaforum.cec.mpg.de] using RPBE [Perdew, 1992 and 1996] with the def2-TZVP basis sets [Weigend, 1998], and the def2-TZVP/J auxiliary bases [Weigend, 2006] for the RI approximation [Vahtras, 1992]. The nuclear Hessian, normal modes, and harmonic vibrational frequencies were then computed using analytical (ANFREQ) and numerical (NUMFREQ) methodologies. The numerical Hessians were computed with nuclear (Cartesian) displacement increments ranging from 0.0001 Bohr to 0.1 Bohr. The geometry optimization was conducted using the parameters of the TIGHTOPT simple input keyword; KS-SCF and CP-SCF calculations used VERYTIGHTSCF thresholds.</p> <p>An analysis of the deviation of normal modes and harmonic frequencies for each numerical Hessian computation from the analytical Hessian results was presented as a single-figure presentation (SFP) at the 2016 Virtual Winterschool on Computational Chemistry (http://winterschool.cc). This SFP can be found at doi:10.5281/zenodo.44807.</p> <p>For the initial OPT and ANFREQ, the following files are provided:<br /> PQ_OPT_AFQ.engrad -- Gradient data<br /> PQ_OPT_AFQ.gbw -- Wavefunction <br /> PQ_OPT_AFQ.hess -- Hessian data<br /> PQ_OPT_AFQ.out -- Computation output<br /> PQ_OPT_AFQ.trj -- Optimization trajectory (multi-frame OpenBabel XYZ)<br /> PQ_OPT_AFQ.txt -- ORCA input file<br /> PQ_OPT_AFQ.xyz -- Optimized geometry</p> <p>For each following NUMFREQ, the following files are provided, where the number at the end of the filename indicates the nuclear displacement increment in Bohrs:<br /> PQ_NFQ_0.####.hess -- Hessian data<br /> PQ_NFQ_0.####.out -- Computation output<br /> PQ_NFQ_0.####.txt -- ORCA input file</p> <p>Since ORCA does not report non-mass-weighted normal modes, these are provided separately for each calculation as modes_0.####.csv (modes_A.csv for the analytical Hessian.)</p> <p>The dot products of each normal mode from the numerical Hessian computations with the corresponding mode in the analytical Hessian calculation (modes ordered as presented in the ORCA output) are provided in modes_dot_products.csv. The MAD of these data are plotted in the LH figure of the above-referenced SFP.</p> <p>For those numerical Hessian computations with normal modes out of sequence relative to the analytical calculation, permutation matrices to bring them back in accord with the analytical Hessian modes are included as swaps_0.####.csv.</p> <p>A table of the calculated vibrational frequencies for each computation, re-ordered as necessary to bring the normal modes in accord with the analytical Hessian run, is included as freqs_swapped.csv. The MAD and maximum absolute deviation of these data are plotted in the RH figure of the above-referenced SFP.</p>
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
Visualization of the numerical pose optimization with the JAMDA scoring function using the BFGS and the LSL-BFGS algorithm
<p>These videos demonstrate the behavior of two different optimization algorithms (BFGS and LSL-BFGS) during pose optimization using the JAMDA protein-ligand scoring function.</p> <p>Flachsenberg et al. (2020) (<a href="http://doi.org/10.1021/acs.jcim.0c01095" target="_blank" rel="noopener">10.1021/acs.jcim.0c01095</a>) describes the JAMDA protein-ligand scoring function and the LSL-BFGS algorithm.<br>The data for these videos stems from Experiment 5 in Flachsenberg et al. (2020). In this experiment, the crystal structure of a ligand was numerically optimized in the binding site with respect to the JAMDA scoring function to create the JAMDA-minimized crystal structure. The JAMDA-minimized crystal structure was randomly deflected to generate various starting poses for the numerical optimization.</p> <p>These videos demonstrate the behavior of two optimization algorithms (BFGS and LSL-BFGS) when optimizing one of the generated starting poses. The chosen example for the videos is a structure of ribonuclease A with a 5'-deoxy-5'-N-piperidinouridine inhibitor (PDB code 3d6q, <a href="https://doi.org/10.2210/pdb3D6Q/pdb" target="_blank" rel="noopener">10.2210/pdb3D6Q/pdb</a>, <a href="https://doi.org/10.1021/jm800724t" target="_blank" rel="noopener">10.1021/jm800724t</a>). Each of the videos shows all the intermediate steps the optimization algorithm takes until convergence.</p> <p>The main observation (that is discussed in detail in Flachsenberg et al. (2020)) is that the BFGS algorithm tends to take inappropriately large steps when clashes are present in the structure, resulting in unwanted binding mode changes. This is <em>not</em> the case for the LSL-BFGS algorithm.</p> <h3>Legend</h3> <p>For each iteration, the JAMDA score value, the RMSD to the JAMDA-minimized crystal structure (yellow), and the RMSD to the optimization's starting point (blue) are given. Furthermore, the gradient's norm (representing the main convergence criterion) is shown. In addition to the optimized ligand, also the JAMDA-minimized crystal structure (yellow) and the optimization's starting structure (blue) are shown.</p> <p><br>Each optimization algorithm is shown in two videos: In one video, the optimized ligand is colored by elements. Here, atoms with clashes (positive JAMDA scores) are marked with orange balls. In the other video, the atoms and bonds of the optimized ligand are colored by their individual JAMDA score.</p> <h3>Used Software</h3> <p>The snapshots of the optimization algorithms were rendered using PyMOL 3.0 (<a href="https://pymol.org/" target="_blank" rel="noopener">https://pymol.org/</a>) and further processed using the Pillow 10.4 Python library (<a href="https://doi.org/10.5281/zenodo.12606429" target="_blank" rel="noopener">10.5281/zenodo.12606429</a>). Videos were created from the individual snapshots using FFmpeg 7.0 (<a href="https://www.ffmpeg.org/" target="_blank" rel="noopener">https://www.ffmpeg.org/</a>).</p>
Fig. 4. Geometric parameters for the numerical optimization process. a in Scientific Note Proposal of a Sluice-type Fish Pass
Fig. 4. Geometric parameters for the numerical optimization process. a) water level in the pool, b) intermediate wall height c) level difference between the pools, d) opening under the sluice, e) the dimension of the pool and f) position of the wall (see Fig. 3 for legend).
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
Mixed Precision Optimization Method acceleration time and numerical simulation results
<p>The stored data are the speedup ratios of the MPFlow method and other acceleration algorithms, as well as the elastic wave response data obtained by numerical simulation using the MPFlow method.</p>
H2020 ENODISE: VKI Experimental dataset of configuration C for numerical optimization
<p>The database includes acoustic and load measurements performed in the ALCOVES anechoic facility of the von Karman Institute on co-rotating coaxial propellers. This configuration corresponds to Config. C of the ENODISE project. This database has been used for numerical optimization. </p>
Switching between Numerical Black-box Optimization Algorithms with Warm-starting Policies - Reproduction artifacts
<p># Reproducibility instructions</p> <p>This document details the steps to reproduce the results presented in the paper 'Chaining of Numerical Black-box Algorithms: Investigating the Impact of Warm-Starting and the Switching Point'</p> <p>## Algorithm code<br> The code for the individual algorithms and the switching routines is included in the 'dynas.zip' folder. </p> <p>## Data collection<br> To collect the performance data for the static algorithms, the file 'data_collection.py' can be used. This file runs all 5 static algorithm on the function suite used in the paper, using the 'ioh' package. To run it, please modify the 'ioh_dir' variable to a place where the data should be stored. <br> The resulting data is made available under the 'static.zip' file.</p> <p>## Processing code<br> The code which processes the performance data into the list of usecases is part of the 'Processing_R' notebook. <br> This results in a set of rds-files with the data in IOHanalyzer's internal format, and in a csv file ('Split_performances_relative.csv') with the selected usecases. </p> <p>This csv file can then be read in using the 'visualization' notebook, which can generate Figure 1 from the paper. This notebook can then be used to count the specific usecases, which is the data shown in Table 1. The csv-file containing the selected usecases ('usecases.csv') is also generated here.</p> <p>The 'usecases' csv file can be used in the 'Data_collection_switch' notebook to run the dynamic configurations. This results in the 'Dynamic.zip' data, which can again be read using IOHanalyzer in the 'Processing_R' notebook. This then generates several csv-files, for both the static and dynamic case, containing the per-run hitting times ('RT_samples.zip'). These are then used in the 'Visualization' notebook to generate figure 3 and 4, as well as table 2. </p> <p>## Countour plots<br> The code for generating Figure 5 is included in the end of the 'Visualization' notebook.</p> <p>## Performance comparison plot<br> Figure 6 has been generated using IOHanalyzer web-interface (iohanalyzer.liacs.nl), by uploading the files in 'cma_bfgs.zip'. </p> <p>## Switch point impact plot<br> Figure 7 has been generated using the 'split_experiment' notebook</p>
Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Computing Star Discrepancies with Numerical Black-Box<br> Optimization Algorithms'. </p> <p>The following files are included:</p> <p>- TA.zip and DEM.zip: The code used for the TA and DEM algorithms respectively.</p> <p>- experiment_runner: Python file which was used to run the black-box optimization algorithms on the discrepancy problems from IOHexperimenter (requires package 'ioh', version 0.3.6 or higher). This generates data in IOH-format, which is included in 'raw_data.zip'</p> <p>- process_stardicr.R: R script which uses IOHanalyzer to extract the performance from the raw data into csv files for visualization. The resulting csvs are included in 'csv_with_pos' for the final results including the corresponding coordinates and 'csv_perf.zip', which contains the convergence information.</p> <p>- Found_Values: The discrepancy values found by TA and DEM, separated by sampler.</p> <p>- A csv file of the relative performance of each of the optimizers compared to the values found by TA is included in 'final_precision_table.csv'</p> <p>- Plot_StarDiscr: the python notebook used to generate all figures, except figure 3 which was created using the IOHanalyzer GUI (iohanalyzer.liacs.nl). The full dataset is available on the website under the source 'star_discrepancy'</p> <p>- Figures: some additional figures which were not included in the paper because of space constraints + higher quality versions of some of the landscape plots.</p>
Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization -- Reproducibility Files
<p>This repository contains the files to reproduce the results from the paper "Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization".</p> <p>Data Collection</p> <p>In this folder, all files used to generate the raw performance data for the set of algorithms are included, as well as the code used to generate the ELA features. For the performance, the packages 'ioh', 'nevergrad', 'modde' and 'modcma' are essential, while 'pflacco' is used for ELA. For all scripts in this folder, the number of parallel threads and the folders for reading function settings (included as 3 csv files in this folder) and storing data should be set before execution. </p> <p>Note that the full performance data exceeds 50GB, so it is not included in this repository. Instead, the results of processing it (using the aocc_extraction script) are included in the 'auc_MABBOB' folder (spread across multiple csv-files, with a version using a different budget factor included as well).</p> <p>The ELA data is included as 'ELA' and 'ELA_BBOB' for the affine combinations and component functions respectively. </p> <p>Data Processing, Analysis and Visualization</p> <p>The remaining reproducibility files can be found in the Reproducibility folder. Within this folder are several notebooks which handle various steps in the pipeline, starting with preprocessing the data collected in the previous steps. This results in some csv-files, which are also included for convenience. Afterwards, the remaining notebooks deal with correlation analysis, instance selection methods, and all included plots from the paper. To match the environment used during our execution of these scripts. a yml-file (to be used with conda or mamba) is available as well. </p>
A combination technique for optimal control problems constrained by random PDEs - numerical tests
<p>This dataset contains the codes used and data analyzed in the manuscript ``A combination technique for optimal control problems under uncertainty".</p> <p> </p>
Numerical data supporting the publication "Accurate and gate-efficient quantum Ansätze for electronic states without adaptive optimization"
<p>Numerical data and plotting scripts for regenerating figures in the article "Accurate and gate-efficient quantum ansätze for electronic states without adaptive optimisation".</p> <p>See README for more details.</p>
Preconditioners for robust optimal control problems under uncertainty - numerical tests
<p>Codes and data of the numerical experiments described in the manuscript "Preconditioners for optimal control problems under uncertainty".</p>
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OpenNeuro
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