build based on 655bee5

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-08-23T09:35:44","documenter_version":"1.6.0"}}
+{"documenter":{"julia_version":"1.10.4","generation_timestamp":"2024-08-27T12:58:00","documenter_version":"1.6.0"}}

dev/generated/examples/compressed_sensing/index.html

@@ -5,26 +5,26 @@
 size(image)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">(256, 256)</code></pre><p>This produces a 256x256 image of a Shepp-Logan phantom.</p><p>In this example, we consider a problem in which we randomly sample a third of the pixels in the image. Such a problem and the corresponding measurement can be constructed with the packages LinearOperatorCollection and Random:</p><p>We first randomly shuffle the indices of the image and then select the first third of the indices to sample.</p><pre><code class="language-julia hljs">using Random, LinearOperatorCollection
 randomIndices = shuffle(eachindex(image))
 sampledIndices = sort(randomIndices[1:div(end, 3)])</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">21845-element Vector{Int64}:
-     1
-     3
-     4
-     5
-     7
+     2
+     6
+     9
     12
     13
     14
-    20
-    26
+    22
+    30
+    37
+    40
      ⋮
- 65519
- 65521
- 65522
- 65524
+ 65514
+ 65515
+ 65517
+ 65525
+ 65526
  65528
  65529
- 65532
- 65535
- 65536</code></pre><p>Afterwards we build a sampling operator which samples the image at the selected indices.</p><pre><code class="language-julia hljs">A = SamplingOp(eltype(image), pattern = sampledIndices , shape = size(image));</code></pre><p>Then we apply the sampling operator to the vectorized image to obtain the sampled measurement vector</p><pre><code class="language-julia hljs">b = A*vec(image);</code></pre><p>To visualize our image we can use CairoMakie:</p><pre><code class="language-julia hljs">using CairoMakie
+ 65531
+ 65532</code></pre><p>Afterwards we build a sampling operator which samples the image at the selected indices.</p><pre><code class="language-julia hljs">A = SamplingOp(eltype(image), pattern = sampledIndices , shape = size(image));</code></pre><p>Then we apply the sampling operator to the vectorized image to obtain the sampled measurement vector</p><pre><code class="language-julia hljs">b = A*vec(image);</code></pre><p>To visualize our image we can use CairoMakie:</p><pre><code class="language-julia hljs">using CairoMakie
 function plot_image(figPos, img; title = &quot;&quot;, width = 150, height = 150)
   ax = CairoMakie.Axis(figPos; yreversed=true, title, width, height)
   hidedecorations!(ax)
@@ -36,30 +36,30 @@
 samplingMask[sampledIndices] .= true
 plot_image(fig[1,2], image .* samplingMask, title = &quot;Sampled Image&quot;)
 resize_to_layout!(fig)
-fig</code></pre><img src="a03acae3.png" alt="Example block output"/><p>As we can see in the right image, only a third of the pixels are sampled. The goal of the inverse problem is to recover the original image from this measurement vector.</p><h2 id="Solving-the-Inverse-Problem"><a class="docs-heading-anchor" href="#Solving-the-Inverse-Problem">Solving the Inverse Problem</a><a id="Solving-the-Inverse-Problem-1"></a><a class="docs-heading-anchor-permalink" href="#Solving-the-Inverse-Problem" title="Permalink"></a></h2><p>To recover the image from the measurement vector, we solve the TV-regularized least squares problem:</p><p class="math-container">\[\begin{equation}
+fig</code></pre><img src="25bc3631.png" alt="Example block output"/><p>As we can see in the right image, only a third of the pixels are sampled. The goal of the inverse problem is to recover the original image from this measurement vector.</p><h2 id="Solving-the-Inverse-Problem"><a class="docs-heading-anchor" href="#Solving-the-Inverse-Problem">Solving the Inverse Problem</a><a id="Solving-the-Inverse-Problem-1"></a><a class="docs-heading-anchor-permalink" href="#Solving-the-Inverse-Problem" title="Permalink"></a></h2><p>To recover the image from the measurement vector, we solve the TV-regularized least squares problem:</p><p class="math-container">\[\begin{equation}
   \underset{\mathbf{x}}{argmin} \frac{1}{2}\vert\vert \mathbf{A}\mathbf{x}-\mathbf{b} \vert\vert_2^2 + \lambda\vert\vert\mathbf{x}\vert\vert_{\text{TV}} .
 \end{equation}\]</p><p>For this purpose we build a TV regularizer with regularization parameter <span>$λ=0.01$</span></p><pre><code class="language-julia hljs">using RegularizedLeastSquares
 reg = TVRegularization(0.01; shape=size(image));</code></pre><p>We will use the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to solve our inverse problem. Thus, we build the corresponding solver</p><pre><code class="language-julia hljs">solver = createLinearSolver(FISTA, A; reg=reg, iterations=20);</code></pre><p>and apply it to our measurement vector</p><pre><code class="language-julia hljs">img_approx = solve!(solver,b)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">65536-element Vector{Float32}:
- 5.479949f-19
- 1.0197881f-18
- 2.3187864f-18
- 5.21343f-18
- 1.11894665f-17
- 2.3435446f-17
- 4.9866213f-17
- 1.07909926f-16
- 2.3241126f-16
- 5.017118f-16
+ 8.034031f-19
+ 1.406842f-18
+ 3.1647723f-18
+ 7.2798826f-18
+ 1.5588576f-17
+ 3.2338676f-17
+ 7.091761f-17
+ 1.6379288f-16
+ 3.7461387f-16
+ 8.232712f-16
  ⋮
- 4.608231f-16
- 2.1284327f-16
- 9.824584f-17
- 4.5602487f-17
- 2.132212f-17
- 1.01756814f-17
- 4.889565f-18
- 2.3502145f-18
- 1.3239986f-18</code></pre><p>To visualize the reconstructed image, we need to reshape the result vector to the correct shape. Afterwards we can use CairoMakie again:</p><pre><code class="language-julia hljs">img_approx = reshape(img_approx,size(image));
+ 3.6059794f-16
+ 1.7738818f-16
+ 8.813207f-17
+ 4.2510688f-17
+ 1.969455f-17
+ 8.702172f-18
+ 3.742035f-18
+ 1.7658197f-18
+ 1.1275466f-18</code></pre><p>To visualize the reconstructed image, we need to reshape the result vector to the correct shape. Afterwards we can use CairoMakie again:</p><pre><code class="language-julia hljs">img_approx = reshape(img_approx,size(image));
 plot_image(fig[1,3], img_approx, title = &quot;Reconstructed Image&quot;)
 resize_to_layout!(fig)
-fig</code></pre><img src="3073c051.png" alt="Example block output"/><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../getting_started/">« Getting Started</a><a class="docs-footer-nextpage" href="../computed_tomography/">Computed Tomography »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.6.0 on <span class="colophon-date" title="Friday 23 August 2024 09:35">Friday 23 August 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fig</code></pre><img src="6384fc55.png" alt="Example block output"/><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../getting_started/">« Getting Started</a><a class="docs-footer-nextpage" href="../computed_tomography/">Computed Tomography »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.6.0 on <span class="colophon-date" title="Tuesday 27 August 2024 12:58">Tuesday 27 August 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

dev/generated/examples/computed_tomography/index.html

@@ -62,4 +62,4 @@
  0.0</code></pre><p>To visualize the reconstructed image, we need to reshape the result vector to the correct shape. Afterwards we can use CairoMakie again:</p><pre><code class="language-julia hljs">img_approx = reshape(img_approx,size(image));
 plot_image(fig[1,4], img_approx, title = &quot;Reconstructed Image&quot;)
 resize_to_layout!(fig)
-fig</code></pre><img src="97715265.png" alt="Example block output"/><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../compressed_sensing/">« Compressed Sensing</a><a class="docs-footer-nextpage" href="../../../solvers/">Solvers »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.6.0 on <span class="colophon-date" title="Friday 23 August 2024 09:35">Friday 23 August 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+fig</code></pre><img src="97715265.png" alt="Example block output"/><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../compressed_sensing/">« Compressed Sensing</a><a class="docs-footer-nextpage" href="../../../solvers/">Solvers »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.6.0 on <span class="colophon-date" title="Tuesday 27 August 2024 12:57">Tuesday 27 August 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

dev/generated/examples/getting_started/index.html

@@ -9,4 +9,4 @@
   \underset{\mathbf{x}}{argmin} \frac{1}{2}\vert\vert \mathbf{A}\mathbf{x}-\mathbf{b} \vert\vert_2^2 + \lambda\vert\vert\mathbf{x}\vert\vert^2_2 .
 \end{equation}\]</p><p>The corresponding solver can be built with the <span>$l^2_2$</span>-regularization term:</p><pre><code class="language-julia hljs">solver = createLinearSolver(CGNR, A; reg = L2Regularization(0.0001), iterations=32);
 x_approx = solve!(solver, b)
-isapprox(x, x_approx, rtol = 0.001)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">true</code></pre><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../../">« Home</a><a class="docs-footer-nextpage" href="../compressed_sensing/">Compressed Sensing »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.6.0 on <span class="colophon-date" title="Friday 23 August 2024 09:35">Friday 23 August 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+isapprox(x, x_approx, rtol = 0.001)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">true</code></pre><hr/><p><em>This page was generated using <a href="https://github.com/fredrikekre/Literate.jl">Literate.jl</a>.</em></p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../../../">« Home</a><a class="docs-footer-nextpage" href="../compressed_sensing/">Compressed Sensing »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.6.0 on <span class="colophon-date" title="Tuesday 27 August 2024 12:58">Tuesday 27 August 2024</span>. Using Julia version 1.10.4.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>