Reviewing the Digraph3 tutorials documentation

docSphinx/_build/html/_modules/index.html

@@ -128,7 +128,7 @@ <h1>All modules for which code is available</h1>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2012-2024, Raymond Bisdorff.
-      <span class="lastupdated">Last updated on Apr 14, 2024.
+      <span class="lastupdated">Last updated on Apr 15, 2024.
       </span></p>
   </div>
 

docSphinx/_build/html/_sources/index.rst.txt

@@ -132,11 +132,11 @@ Introduction
 
 The *Digraph3 documentation*, available on the `Read The Docs <https://readthedocs.org/>`_ site: |location_link1|, describes the Python3 resources for implementing decision algorithms via **bipolar-valued outranking** digraphs [:raw-html:`<a class="reference internal" href="#Bisdorff-2022" id="id1"><span>1</span></a>`]. These computing resources are useful in the field of `Algorithmic Decision Theory <https://www.lamsade.dauphine.fr/~projet_cost/ALGORITHMIC_DECISION_THEORY/ALGORITHMIC_DECISION_THEORY.html>`_ and more specifically in the field of **Multiple-Criteria Decision Aiding** [:raw-html:`<a class="reference internal" href="#Bisdorff-2015" id="id2"><span>2</span></a>`]. They provide practical tools for a Master Course on |location_link4| taught at the University of Luxembourg.
 
-The documentation contains, first, a set of tutorials introducing the main objects like **digraphs**, **outranking digraphs** and **performance tableaux**. There is also a tutorial provided on **undirected graphs**. Some tutorials are problem oriented and show how to compute the **winner of an election**, how to build a **best choice recommendation**, or **how to linearly rank or rate** with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing **maximal independent sets** (MISs) and **kernels** in graphs and digraphs. The tutorial about **split**, **interval** and **permutation graphs** is inspired by *Martin Golumbic* 's book on *Algorithmic Graph Theory and Perfect Graphs* [:raw-html:`<a class="reference internal" href="#Golumbic-2004" id="id3"><span>3</span></a>`]. We also provide a tutorial on **tree graphs** and **spanning forests**. Recently added, the reader may find two tutorials on **fairly** solving **inter**-, respectively **intragroup pairing** problems. 
+The documentation contains, first, a set of tutorials introducing the main objects like **digraphs**, **outranking digraphs** and **performance tableaux**. There is also a tutorial provided on **undirected graphs**. Some tutorials are problem oriented and show how to compute the **winner of an election**, how to build a **best choice recommendation**, or **how to linearly rank or rate** with multiple incommensurable performance criteria. The tutorial about **split**, **interval** and **permutation graphs** is inspired by *Martin Golumbic* 's book on *Algorithmic Graph Theory and Perfect Graphs* [:raw-html:`<a class="reference internal" href="#Golumbic-2004" id="id3"><span>3</span></a>`]. We also provide a tutorial on **tree graphs** and **spanning forests**. Recently added, the reader may find two tutorials on **fairly** solving **inter**-, respectively **intragroup pairing** problems. 
 
 The second Section concerns the **extensive reference manual** of the collection of provided Python3 modules, classes and methods. The main classes in this collection are the :py:class:`digraphs.Digraph` overall root class, the :py:class:`perfTabs.PerformanceTableau` class and the :py:class:`outrankingDigraphs.BipolarOutrankingDigraph` class. The technical documentation also provides insight into the complete source code of all modules, classes and methods.
 
-The third Section exhibits some pearls of **bipolar-valued epistemic logic** that enrich the Digraph3 resources. These short texts illustrate well the very computational benefit one may get when working in a bipolar-valued logical framework. And, more specifically, the essential part the *logically neutral* **undeterminate** value is judiciously playing therein.  
+The third Section exhibits some pearls of **bipolar-valued epistemic logic** that enrich the Digraph3 resources. These short topics illustrate well the very computational benefit one may get when working in a bipolar-valued logical framework. And, more specifically, the essential part the *logically neutral* **undeterminate** value is judiciously playing therein.  
 
 The fourth and fifth sections provide 2x2-reduced notes of the author's lectures on **Algorithmic Decision Theory** and **Computational Statistics** given at the University of Luxembourg in Autumn 2019 and Spring 2020.
 

docSphinx/_build/html/_sources/tutorial.rst.txt

@@ -109,7 +109,7 @@
       \href{https://digraph3.readthedocs.io/en/latest/index.html}{HTML Version}
       \vspace{5mm}
 
-   The tutorials in this document describe the practical usage of our *Digraph3* Python3 software resources in the field of *Algorithmic Decision Theory* and more specifically in **outranking** based *Multiple Criteria Decision Aid* (MCDA). They mainly illustrate practical tools for a Master Course |location_link4| at the University of Luxembourg. The document contains first a set of tutorials introducing the main objects available in the Digraph3 collection of Python3 modules, like **digraphs**, **outranking digraphs**, **performance tableaux** and **voting profiles**. Some of the tutorials are decision problem oriented and show how to compute the potential **winner(s)** of an election, how to build a **best choice recommendation**, or how to **rate** or **linearly rank** with multiple incommensurable performance criteria. More graph theoretical tutorials follow. One on working with **undirected graphs**, followed by a tutorial on how to compute **non isomorphic maximal independent sets** (kernels) in the n-cycle graph. Finally, special tutorials are devoted to *perfect* graphs, like *split*, *interval* and *permutation* graphs, and to *tree-graphs* and *forests*.
+   The tutorials in this document describe the practical usage of our *Digraph3* Python3 software resources in the field of *Algorithmic Decision Theory* and more specifically in **outranking** based *Multiple Criteria Decision Aid* (MCDA). They mainly illustrate practical tools for a Master Course |location_link4| at the University of Luxembourg. The document contains first a set of tutorials introducing the main objects available in the Digraph3 collection of Python3 modules, like **digraphs**, **outranking digraphs**, **performance tableaux** and **voting profiles**. Some of the tutorials are decision problem oriented and show how to compute the potential **winner(s)** of an election, how to build a **best choice recommendation**, or how to **rate** or **linearly rank** with multiple incommensurable performance criteria. More graph theoretical tutorials follow. One on working with **undirected graphs**, followed by tutorials on how to tackle big outranking digraphs. Finally, special tutorials are devoted to *perfect* graphs, like *split*, *interval* and *permutation* graphs, and to *tree-graphs* and *forests*.
 
    .. raw:: latex
 

docSphinx/_build/html/genindex.html

@@ -2684,7 +2684,7 @@ <h2 id="Z">Z</h2>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2012-2024, Raymond Bisdorff.
-      <span class="lastupdated">Last updated on Apr 14, 2024.
+      <span class="lastupdated">Last updated on Apr 15, 2024.
       </span></p>
   </div>
 

docSphinx/_build/html/index.html

@@ -191,9 +191,9 @@ <h1>Python resources for Algorithmic Decision Theory<a class="headerlink" href="
 </div>
 </div></blockquote>
 <p>The <em>Digraph3 documentation</em>, available on the <a class="reference external" href="https://readthedocs.org/">Read The Docs</a> site: <a href="https://digraph3.readthedocs.io/en/latest/" target="_blank">https://digraph3.readthedocs.io/en/latest/</a>, describes the Python3 resources for implementing decision algorithms via <strong>bipolar-valued outranking</strong> digraphs [<span class="raw-html"><a class="reference internal" href="#Bisdorff-2022" id="id1"><span>1</span></a></span>]. These computing resources are useful in the field of <a class="reference external" href="https://www.lamsade.dauphine.fr/~projet_cost/ALGORITHMIC_DECISION_THEORY/ALGORITHMIC_DECISION_THEORY.html">Algorithmic Decision Theory</a> and more specifically in the field of <strong>Multiple-Criteria Decision Aiding</strong> [<span class="raw-html"><a class="reference internal" href="#Bisdorff-2015" id="id2"><span>2</span></a></span>]. They provide practical tools for a Master Course on <a href="http://hdl.handle.net/10993/37933" target="_blank">Algorithmic Decision Theory</a> taught at the University of Luxembourg.</p>
-<p>The documentation contains, first, a set of tutorials introducing the main objects like <strong>digraphs</strong>, <strong>outranking digraphs</strong> and <strong>performance tableaux</strong>. There is also a tutorial provided on <strong>undirected graphs</strong>. Some tutorials are problem oriented and show how to compute the <strong>winner of an election</strong>, how to build a <strong>best choice recommendation</strong>, or <strong>how to linearly rank or rate</strong> with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing <strong>maximal independent sets</strong> (MISs) and <strong>kernels</strong> in graphs and digraphs. The tutorial about <strong>split</strong>, <strong>interval</strong> and <strong>permutation graphs</strong> is inspired by <em>Martin Golumbic</em> ‘s book on <em>Algorithmic Graph Theory and Perfect Graphs</em> [<span class="raw-html"><a class="reference internal" href="#Golumbic-2004" id="id3"><span>3</span></a></span>]. We also provide a tutorial on <strong>tree graphs</strong> and <strong>spanning forests</strong>. Recently added, the reader may find two tutorials on <strong>fairly</strong> solving <strong>inter</strong>-, respectively <strong>intragroup pairing</strong> problems.</p>
+<p>The documentation contains, first, a set of tutorials introducing the main objects like <strong>digraphs</strong>, <strong>outranking digraphs</strong> and <strong>performance tableaux</strong>. There is also a tutorial provided on <strong>undirected graphs</strong>. Some tutorials are problem oriented and show how to compute the <strong>winner of an election</strong>, how to build a <strong>best choice recommendation</strong>, or <strong>how to linearly rank or rate</strong> with multiple incommensurable performance criteria. The tutorial about <strong>split</strong>, <strong>interval</strong> and <strong>permutation graphs</strong> is inspired by <em>Martin Golumbic</em> ‘s book on <em>Algorithmic Graph Theory and Perfect Graphs</em> [<span class="raw-html"><a class="reference internal" href="#Golumbic-2004" id="id3"><span>3</span></a></span>]. We also provide a tutorial on <strong>tree graphs</strong> and <strong>spanning forests</strong>. Recently added, the reader may find two tutorials on <strong>fairly</strong> solving <strong>inter</strong>-, respectively <strong>intragroup pairing</strong> problems.</p>
 <p>The second Section concerns the <strong>extensive reference manual</strong> of the collection of provided Python3 modules, classes and methods. The main classes in this collection are the <a class="reference internal" href="techDoc.html#digraphs.Digraph" title="digraphs.Digraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">digraphs.Digraph</span></code></a> overall root class, the <a class="reference internal" href="techDoc.html#perfTabs.PerformanceTableau" title="perfTabs.PerformanceTableau"><code class="xref py py-class docutils literal notranslate"><span class="pre">perfTabs.PerformanceTableau</span></code></a> class and the <a class="reference internal" href="techDoc.html#outrankingDigraphs.BipolarOutrankingDigraph" title="outrankingDigraphs.BipolarOutrankingDigraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">outrankingDigraphs.BipolarOutrankingDigraph</span></code></a> class. The technical documentation also provides insight into the complete source code of all modules, classes and methods.</p>
-<p>The third Section exhibits some pearls of <strong>bipolar-valued epistemic logic</strong> that enrich the Digraph3 resources. These short texts illustrate well the very computational benefit one may get when working in a bipolar-valued logical framework. And, more specifically, the essential part the <em>logically neutral</em> <strong>undeterminate</strong> value is judiciously playing therein.</p>
+<p>The third Section exhibits some pearls of <strong>bipolar-valued epistemic logic</strong> that enrich the Digraph3 resources. These short topics illustrate well the very computational benefit one may get when working in a bipolar-valued logical framework. And, more specifically, the essential part the <em>logically neutral</em> <strong>undeterminate</strong> value is judiciously playing therein.</p>
 <p>The fourth and fifth sections provide 2x2-reduced notes of the author’s lectures on <strong>Algorithmic Decision Theory</strong> and <strong>Computational Statistics</strong> given at the University of Luxembourg in Autumn 2019 and Spring 2020.</p>
 <p>The last section gathers <strong>historical case studies</strong> with example digraphs compiled before 2006 and concerning the early development of tools and methods for enumerating <em>non isomorphic maximal independent sets</em> in undirected graphs and computing <em>digraph kernels</em>.</p>
 <span class="target" id="bibliography-label"></span><p class="rubric" id="bibliography-label">References</p>
@@ -224,7 +224,7 @@ <h1>Python resources for Algorithmic Decision Theory<a class="headerlink" href="
 
   <div role="contentinfo">
     <p>&#169; Copyright 2012-2024, Raymond Bisdorff.
-      <span class="lastupdated">Last updated on Apr 14, 2024.
+      <span class="lastupdated">Last updated on Apr 15, 2024.
       </span></p>
   </div>
 

docSphinx/_build/html/py-modindex.html

@@ -290,7 +290,7 @@ <h1>Python Module Index</h1>
 
   <div role="contentinfo">
     <p>&#169; Copyright 2012-2024, Raymond Bisdorff.
-      <span class="lastupdated">Last updated on Apr 14, 2024.
+      <span class="lastupdated">Last updated on Apr 15, 2024.
       </span></p>
   </div>
 

docSphinx/_build/html/search.html

@@ -117,7 +117,7 @@
 
   <div role="contentinfo">
     <p>&#169; Copyright 2012-2024, Raymond Bisdorff.
-      <span class="lastupdated">Last updated on Apr 14, 2024.
+      <span class="lastupdated">Last updated on Apr 15, 2024.
       </span></p>
   </div>
 

docSphinx/_build/html/tutorial.html

@@ -10108,7 +10108,7 @@ <h3><span class="section-number">1.7.1. </span>Bibliography<a class="headerlink"
 
   <div role="contentinfo">
     <p>&#169; Copyright 2012-2024, Raymond Bisdorff.
-      <span class="lastupdated">Last updated on Apr 14, 2024.
+      <span class="lastupdated">Last updated on Apr 15, 2024.
       </span></p>
   </div>
 

docSphinx/index.rst

@@ -132,11 +132,11 @@ Introduction
 
 The *Digraph3 documentation*, available on the `Read The Docs <https://readthedocs.org/>`_ site: |location_link1|, describes the Python3 resources for implementing decision algorithms via **bipolar-valued outranking** digraphs [:raw-html:`<a class="reference internal" href="#Bisdorff-2022" id="id1"><span>1</span></a>`]. These computing resources are useful in the field of `Algorithmic Decision Theory <https://www.lamsade.dauphine.fr/~projet_cost/ALGORITHMIC_DECISION_THEORY/ALGORITHMIC_DECISION_THEORY.html>`_ and more specifically in the field of **Multiple-Criteria Decision Aiding** [:raw-html:`<a class="reference internal" href="#Bisdorff-2015" id="id2"><span>2</span></a>`]. They provide practical tools for a Master Course on |location_link4| taught at the University of Luxembourg.
 
-The documentation contains, first, a set of tutorials introducing the main objects like **digraphs**, **outranking digraphs** and **performance tableaux**. There is also a tutorial provided on **undirected graphs**. Some tutorials are problem oriented and show how to compute the **winner of an election**, how to build a **best choice recommendation**, or **how to linearly rank or rate** with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing **maximal independent sets** (MISs) and **kernels** in graphs and digraphs. The tutorial about **split**, **interval** and **permutation graphs** is inspired by *Martin Golumbic* 's book on *Algorithmic Graph Theory and Perfect Graphs* [:raw-html:`<a class="reference internal" href="#Golumbic-2004" id="id3"><span>3</span></a>`]. We also provide a tutorial on **tree graphs** and **spanning forests**. Recently added, the reader may find two tutorials on **fairly** solving **inter**-, respectively **intragroup pairing** problems. 
+The documentation contains, first, a set of tutorials introducing the main objects like **digraphs**, **outranking digraphs** and **performance tableaux**. There is also a tutorial provided on **undirected graphs**. Some tutorials are problem oriented and show how to compute the **winner of an election**, how to build a **best choice recommendation**, or **how to linearly rank or rate** with multiple incommensurable performance criteria. The tutorial about **split**, **interval** and **permutation graphs** is inspired by *Martin Golumbic* 's book on *Algorithmic Graph Theory and Perfect Graphs* [:raw-html:`<a class="reference internal" href="#Golumbic-2004" id="id3"><span>3</span></a>`]. We also provide a tutorial on **tree graphs** and **spanning forests**. Recently added, the reader may find two tutorials on **fairly** solving **inter**-, respectively **intragroup pairing** problems. 
 
 The second Section concerns the **extensive reference manual** of the collection of provided Python3 modules, classes and methods. The main classes in this collection are the :py:class:`digraphs.Digraph` overall root class, the :py:class:`perfTabs.PerformanceTableau` class and the :py:class:`outrankingDigraphs.BipolarOutrankingDigraph` class. The technical documentation also provides insight into the complete source code of all modules, classes and methods.
 
-The third Section exhibits some pearls of **bipolar-valued epistemic logic** that enrich the Digraph3 resources. These short texts illustrate well the very computational benefit one may get when working in a bipolar-valued logical framework. And, more specifically, the essential part the *logically neutral* **undeterminate** value is judiciously playing therein.  
+The third Section exhibits some pearls of **bipolar-valued epistemic logic** that enrich the Digraph3 resources. These short topics illustrate well the very computational benefit one may get when working in a bipolar-valued logical framework. And, more specifically, the essential part the *logically neutral* **undeterminate** value is judiciously playing therein.  
 
 The fourth and fifth sections provide 2x2-reduced notes of the author's lectures on **Algorithmic Decision Theory** and **Computational Statistics** given at the University of Luxembourg in Autumn 2019 and Spring 2020.