Wikipedia prefers https over http links

kiegroup · Feb 8, 2018 · 09cbccf · 09cbccf
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diff --git a/blog/2014-03-03-CanMapReduceSolvePlanningProblems.adoc b/blog/2014-03-03-CanMapReduceSolvePlanningProblems.adoc
@@ -12,7 +12,7 @@ Let's see what happens if we MapReduce a planning problem, such as the _Travelin
 
 == About MapReduce
 
-http://en.wikipedia.org/wiki/MapReduce[MapReduce] is programming model
+https://en.wikipedia.org/wiki/MapReduce[MapReduce] is programming model
 which has proven to be very effective to run a _query_ on big data.
 Generally speaking, it works like this:
 
@@ -28,7 +28,7 @@ For example, suppose we need to find the most expensive invoice record in a data
 
 == About the Traveling Salesman Problem
 
-The http://en.wikipedia.org/wiki/Travelling_salesman_problem[Traveling Salesman Problem] (TSP) is a very basic planning problem.
+The https://en.wikipedia.org/wiki/Travelling_salesman_problem[Traveling Salesman Problem] (TSP) is a very basic planning problem.
 Given a list of cities, find the shortest path to visit all cities.
 
 For example, here's a dataset with `68` cities and its optimal tour with a distance of `674`:

diff --git a/blog/2014-03-27-IsTheSearchSpaceOfAnOptimizationProblemReallyThatBig.adoc b/blog/2014-03-27-IsTheSearchSpaceOfAnOptimizationProblemReallyThatBig.adoc
@@ -73,8 +73,8 @@ An infeasible solution is a solution that breaks at least 1 hard constraint.
 image::feasibleSearchSpaceSizeCalculation.png[]
 
 * *Cloud Balancing*: The feasible search space is impossible to calculate.
-The capacity hard constraints are a form of http://en.wikipedia.org/wiki/Bin_packing_problem[bin packing],
-which is http://en.wikipedia.org/wiki/NP-complete[NP-complete].
+The capacity hard constraints are a form of https://en.wikipedia.org/wiki/Bin_packing_problem[bin packing],
+which is https://en.wikipedia.org/wiki/NP-complete[NP-complete].
 That implies that even the smartest computer scientists fail to calculate it in reasonable time
 for medium or big datasets.
 

diff --git a/blog/2014-05-12-CheatingOnTheNQueensBenchmark.adoc b/blog/2014-05-12-CheatingOnTheNQueensBenchmark.adoc
@@ -29,7 +29,7 @@ INFO  Solving ended: time spent (2981), best score (0), ...
 
 The N Queens problem is not NP-complete, nor NP-hard.
 That is _math speak_ for stating that _there's a perfect algorithm to solve this problem_:
-the http://en.wikipedia.org/wiki/Eight_queens_puzzle#Explicit_solutions[Explicits Solutions algorithm].
+the https://en.wikipedia.org/wiki/Eight_queens_puzzle#Explicit_solutions[Explicits Solutions algorithm].
 Implemented with a `CustomPhaseCommand` in OptaPlanner it looks like this:
 
 [source,java]

diff --git a/learn/slides/optaplanner-presentation/index.html b/learn/slides/optaplanner-presentation/index.html
@@ -700,7 +700,7 @@ <h3>
                         <li>⇔ <b>Impossible to find optimal solution and scale out</b></li>
                     </ul>
                 </li>
-                <li>3000+ known NP-complete problems (<a href="http://en.wikipedia.org/wiki/List_of_NP-complete_problems">wikipedia</a>)</li>
+                <li>3000+ known NP-complete problems (<a href="https://en.wikipedia.org/wiki/List_of_NP-complete_problems">wikipedia</a>)</li>
             </ul>
         </section>
         <section>
@@ -727,7 +727,7 @@ <h3>
                 difficult to solve!
             </h3>
             <p>And human aren't good at it</p>
-            <!-- Public domain from http://en.wikipedia.org/wiki/File:Ishihara_9.png -->
+            <!-- Public domain from https://en.wikipedia.org/wiki/File:Ishihara_9.png -->
             <img src="extra/Presentation/colorBlind.png">
             <p>But they don't realize it<br/>(nor does their manager)</p>
         </section>

diff --git a/learn/slides/optaplanner-presentation/training.html b/learn/slides/optaplanner-presentation/training.html
@@ -363,7 +363,7 @@ <h3>
                         <li>⇔ <b>Impossible to find optimal solution and scale out</b></li>
                     </ul>
                 </li>
-                <li>3000+ known NP-complete problems (<a href="http://en.wikipedia.org/wiki/List_of_NP-complete_problems">wikipedia</a>)</li>
+                <li>3000+ known NP-complete problems (<a href="https://en.wikipedia.org/wiki/List_of_NP-complete_problems">wikipedia</a>)</li>
             </ul>
         </section>
         <section>
@@ -387,7 +387,7 @@ <h2>
                 difficult to solve!
             </h2>
             <p>And human aren't good at it</p>
-            <!-- Public domain from http://en.wikipedia.org/wiki/File:Ishihara_9.png -->
+            <!-- Public domain from https://en.wikipedia.org/wiki/File:Ishihara_9.png -->
             <img src="extra/Presentation/colorBlind.png">
             <p>But they don't realize it<br/>(nor does their manager)</p>
         </section>

diff --git a/localized/ja/UserGuide-chapter1.adoc b/localized/ja/UserGuide-chapter1.adoc
@@ -85,15 +85,15 @@ image::https://docs.optaplanner.org/6.4.0.Final/optaplanner-docs/html_single/ima
 [[aPlanningProblemIsNPCompleteOrNPHard]]
 === 1.3.1. 計画問題は NP 完全または NP 困難
 
-上に挙げたユースケースはすべて、_おそらく_ 、link:http://en.wikipedia.org/wiki/NP-complete[NP 完全]もしくはそれよりも困難な問題です。NP 完全とは、普通の言葉で表現すると次のような意味です。
+上に挙げたユースケースはすべて、_おそらく_ 、link:https://en.wikipedia.org/wiki/NP-complete[NP 完全]もしくはそれよりも困難な問題です。NP 完全とは、普通の言葉で表現すると次のような意味です。
 
 * 問題の解を、簡単に、適度な時間内に検証できる。
 * 問題の最適解を適度な時間内に見つける確実な方法がない(*)。
 
 .注意
 ****
 (*) 少なくとも、世界中の頭の切れるコンピュータ科学者でさえ、まだ誰もそのような確実な方法を見出せていません。しかし、誰かが 1 つの NP 完全問題について解を一つ見つけたとしたら、その解はすべての NP 完全問題に当てはまることになります。
-実際、link:http://en.wikipedia.org/wiki/P_%3D_NP_problem[そのような確実な方法が実在するかどうか]を証明した人には 100 万ドルの懸賞金が与えられることになっています。
+実際、link:https://en.wikipedia.org/wiki/P_%3D_NP_problem[そのような確実な方法が実在するかどうか]を証明した人には 100 万ドルの懸賞金が与えられることになっています。
 ****
 
 これは実に恐ろしいことを示唆しています。つまり、次の 2 つの一般的な方法が力不足であるために、自分の抱える問題を解くのが、おそらくは想像以上に困難だということです。