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6 results for “linear matrix”
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Paper: "Sensitivity analysis for linear changes of the constraint matrix of a linear program" output
<p>Output generated from the experiments in the paper "Sensitivity analysis for linear changes of the constraint matrix of a linear program"</p>
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output. in The species Severe acute respiratory syndromerelated coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2
Pairwise distance demarcation of species in the family Coronaviridae. a, Diagonal matrix of PPDs of 2,505 viruses clustered according to 49 coronavirus species, 39 established and 10 pending or tentative, and ordered from the most to least populous species, from left to right; green and white, PPDs smaller and larger than the inter-species threshold, respectively. Areas of the green squares along the diagonal are proportional to the virus sampling of the respective species, and virus prototypes of the five most sampled species are specified to the left; asterisks indicate species that include viruses whose intra-species PPDs crossed the inter-species threshold (threshold 'violators'). b, Maximal intra-species PPDs (x axis, linear scale) plotted against virus sampling (y axis, log scale) for 49 species (green dots) of the Coronaviridae. Indicated are the acronyms of virus prototypes of the seven most sampled species. Green and blue plot sections represent intra-species and intra-subgenera PPD ranges. The vertical black line indicates the inter-species threshold. c, Shown are the PDs of non-identical residues (y axis) for four viruses representing three major phylogenetic lineages (clades) of the species Severe acute respiratorysyndrome-related coronavirus (panel b) and all pairs of the 256 viruses of this species ('all pairs'). The PD values were derived from pairwise distances in the MSA that were calculated using an identity matrix. Panels a and b were adopted from the DEmARC v.1.4 output.
Data of Rosenbrock method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"
<p>This deposit contains the datasets generated by the Rosenbrock method for the paper:</p> <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> <p>Download the individual files and store them inside the directory <code>data/rosenbrock/</code>.</p> <p>The file names are structured as follows.</p> <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution at the previous time step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=200</code>: maximum number of iterations</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>nsteps=45|150</code>: number of Rosenbrock steps</li> <li><code>tspan=(4500.0, 0.0)</code>: global time span of DRE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> <p>Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries:</p> <ul> <li><code>file["rosenbrock_metrics"]</code>: data frame containing execution metrics of Rosenbrock iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Rosenbrock iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed (always <code>false</code>) </li> </ul> <p>All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>.</p> <p>Generating this dataset took ~22h and consumed ~2.72kWh of electricity.</p>
Data of Newton method of "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences"
<p>This deposit contains the datasets generated by the Newton method for the paper:</p> <ul> <li>J. Schulze, J. Saak: "A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences".</li> </ul> <p>Download the individual files and store them inside the directory <code>data/newton-adi/</code>.</p> <p>The file names are structured as follows.</p> <ul> <li><code>Rail5177</code>: <a href="https://morwiki.mpi-magdeburg.mpg.de/morwiki/index.php/Steel_Profile">Steel Profile</a> benchmark problem of dimension 5177</li> <li><code>adi_initprev=true|false</code>: whether the initial ADI iterate was set to the solution of the previous Newton step (or zero)</li> <li><code>adi_kwargs=...</code>: keyword arguments passed to ADI method <ul> <li><code>maxiters=1000</code>: maximum number of iterations</li> <li><code>shifts=...</code>: shift strategy</li> </ul> </li> <li><code>newton_kwargs=...</code>: keyword arguments passed to Newton method <ul> <li><code>inexact=true|false</code>: whether to use inexact Newton method</li> <li><code>inexact_hybrid=true|false</code>: whether to switch back to classical Newton method in later iterations (only present if <code>inexact=true</code>)</li> <li><code>linesearch=true|false</code>: whether to employ line search</li> <li><code>reltol=1e-10</code>: relative tolerance to reason about convergence</li> </ul> </li> <li><code>β=1000</code>: scaling of the quadratic term in the ARE</li> <li><code>.jld2</code>: file suffix. All file have been generated with <a href="https://github.com/JuliaIO/JLD2.jl">JLD2.jl</a> version 0.4.38</li> </ul> <p>Load the dataset via <code>using JLD2</code> and <code>file = load(FILENAME)</code>. This will yield a dictionary having the following entries:</p> <ul> <li><code>file["newton_metrics"]</code>: data frame containing execution metrics of Newton iterations</li> <li><code>file["adi_metrics"]</code>: data frame containing execution metrocs of ADI iterations of all Newton iterations</li> <li><code>file["timer"]</code>: isolated runtime metrics generated with <a href="https://github.com/KristofferC/TimerOutputs.jl">TimerOutputs.jl</a> version 0.5.23</li> <li><code>file["timer_metrics"]</code>: runtime metrics of seperate run with additional data observers enabled</li> <li><code>file["config"]</code>: internal configuration object that led to this dataset (information also embedded in file name)</li> <li><code>file["failed"]</code>: Boolean on whether configuration has failed</li> </ul> <p>All data frames were generated with <a href="https://github.com/JuliaData/DataFrames.jl">DataFrames.jl</a> version 1.6.1 and have their columns documented <a href="https://dataframes.juliadata.org/stable/lib/metadata/">using metadata</a>.</p> <p>Generating this dataset took ~3h and consumed ~0.25kWh of electricity.</p>
Supplementary material 1 from: Cirino DW, Lupinetti-Cunha A, Freitas CH, de Freitas SR (2022) Do the roadkills of different mammal species respond the same way to habitat and matrix? In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 65-85. https://doi.org/10.3897/natureconservation.47.73010
Correlation plot and R script for building and selecting best models
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