Support data for the paper "Addressing Large Rank-Deficient Linear Least-Squares Problems on Shared-Memory CPU and GPU Architectures"
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2026
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| author | Quintana-Ortí, Gregorio Chillarón, Mónica Vidal, Vicente Per-Gunnar, Martinsson |
| author_facet | Quintana-Ortí, Gregorio Chillarón, Mónica Vidal, Vicente Per-Gunnar, Martinsson |
| contents | <p>These files correspond to the supporting data used in Section 4.1 of the paper <em>"Addressing Large Rank-Deficient Linear Least-Squares Problems on Shared-Memory CPU and GPU Architectures"</em>.<br>We provide three test cases generated by the authors:</p> <h3><strong>1. Toeplitz.mat</strong></h3> <p>A Toeplitz matrix constructed to study a case of approximately low rank.</p> <ul> <li>Format: MATLAB sparse matrix</li> <li>Dimensions: <span class="katex"><span class="katex-mathml">m=15,000, n=14,000</span></span></li> <li>Numerical rank: <span class="katex"><span class="katex-mathml">14,000</span></span></li> </ul> <p>This matrix exhibits rapidly decaying singular values and is intended to represent a structured, approximately low-rank scenario.</p> <h3><strong>2. Tomography1.mat</strong></h3> <p><strong><em>A</em> </strong>system matrix (stored as sparse to save space) and right-hand side vector <strong><em>b</em> </strong>corresponding to a 2D Computed Tomography (CT) forward model.</p> <ul> <li>Matrix format: MATLAB sparse matrix</li> <li>Dimensions: <span class="katex"><span class="katex-mathml">m=148,672, n=147,456</span></span></li> <li>Numerical rank: <span class="katex"><span class="katex-mathml">141,270</span></span></li> <li><span class="katex"><span class="katex-mathml">b</span></span>: projection vector (sinogram) for one slice of the<strong> </strong>Forbild Head Phantom</li> </ul> <p>The data corresponds to a simulated CT scan with:</p> <ul> <li>736 detectors</li> <li>202 projections</li> <li>Reconstruction grid of <span class="katex"><span class="katex-mathml">384×384 pixels</span></span></li> <li>Forward model generated using Joseph’s method</li> </ul> <h3><strong>3. Tomography2.mat</strong></h3> <p>A larger tomography test case.</p> <ul> <li>Matrix format: MATLAB sparse matrix</li> <li>Dimensions: <span class="katex"><span class="katex-mathml">m=590,272, n=589,824</span></span></li> <li>Numerical rank: <span class="katex"><span class="katex-mathml">560,526</span></span></li> <li><span class="katex"><span class="katex-mathml">b:</span></span> sinogram for one slice of the Forbild Head Phantom</li> </ul> <p>Associated with:</p> <ul> <li>736 detectors</li> <li>802 projections</li> <li>Reconstruction grid of <span class="katex"><span class="katex-mathml">768×768</span></span></li> <li>Forward model generated using Joseph’s method</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19568208 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Support data for the paper "Addressing Large Rank-Deficient Linear Least-Squares Problems on Shared-Memory CPU and GPU Architectures" Quintana-Ortí, Gregorio Chillarón, Mónica Vidal, Vicente Per-Gunnar, Martinsson <p>These files correspond to the supporting data used in Section 4.1 of the paper <em>"Addressing Large Rank-Deficient Linear Least-Squares Problems on Shared-Memory CPU and GPU Architectures"</em>.<br>We provide three test cases generated by the authors:</p> <h3><strong>1. Toeplitz.mat</strong></h3> <p>A Toeplitz matrix constructed to study a case of approximately low rank.</p> <ul> <li>Format: MATLAB sparse matrix</li> <li>Dimensions: <span class="katex"><span class="katex-mathml">m=15,000, n=14,000</span></span></li> <li>Numerical rank: <span class="katex"><span class="katex-mathml">14,000</span></span></li> </ul> <p>This matrix exhibits rapidly decaying singular values and is intended to represent a structured, approximately low-rank scenario.</p> <h3><strong>2. Tomography1.mat</strong></h3> <p><strong><em>A</em> </strong>system matrix (stored as sparse to save space) and right-hand side vector <strong><em>b</em> </strong>corresponding to a 2D Computed Tomography (CT) forward model.</p> <ul> <li>Matrix format: MATLAB sparse matrix</li> <li>Dimensions: <span class="katex"><span class="katex-mathml">m=148,672, n=147,456</span></span></li> <li>Numerical rank: <span class="katex"><span class="katex-mathml">141,270</span></span></li> <li><span class="katex"><span class="katex-mathml">b</span></span>: projection vector (sinogram) for one slice of the<strong> </strong>Forbild Head Phantom</li> </ul> <p>The data corresponds to a simulated CT scan with:</p> <ul> <li>736 detectors</li> <li>202 projections</li> <li>Reconstruction grid of <span class="katex"><span class="katex-mathml">384×384 pixels</span></span></li> <li>Forward model generated using Joseph’s method</li> </ul> <h3><strong>3. Tomography2.mat</strong></h3> <p>A larger tomography test case.</p> <ul> <li>Matrix format: MATLAB sparse matrix</li> <li>Dimensions: <span class="katex"><span class="katex-mathml">m=590,272, n=589,824</span></span></li> <li>Numerical rank: <span class="katex"><span class="katex-mathml">560,526</span></span></li> <li><span class="katex"><span class="katex-mathml">b:</span></span> sinogram for one slice of the Forbild Head Phantom</li> </ul> <p>Associated with:</p> <ul> <li>736 detectors</li> <li>802 projections</li> <li>Reconstruction grid of <span class="katex"><span class="katex-mathml">768×768</span></span></li> <li>Forward model generated using Joseph’s method</li> </ul> |
| title | Support data for the paper "Addressing Large Rank-Deficient Linear Least-Squares Problems on Shared-Memory CPU and GPU Architectures" |
| url | https://doi.org/10.5281/zenodo.19568208 |