TheAlgorithms · vil02 · Jun 18, 2024 · Jun 18, 2024
@@ -99,7 +99,7 @@
 
     <!-- Checks for Javadoc comments.                     -->
     <!-- See https://checkstyle.org/checks/javadoc/index.html -->
-    <!-- TODO <module name="InvalidJavadocPosition"/> -->
+    <module name="InvalidJavadocPosition"/>
     <!-- TODO <module name="JavadocMethod"/> -->
     <!-- TODO <module name="JavadocType"/> -->
     <!-- TODO <module name="JavadocVariable"/> -->

@@ -1,16 +1,14 @@
-/**
- * Author : Siddhant Swarup Mallick
- * Github : https://github.com/siddhant2002
- */
-
-/** Program description - To find all possible paths from source to destination*/
-
-/**Wikipedia link -> https://en.wikipedia.org/wiki/Shortest_path_problem */
 package com.thealgorithms.backtracking;
 
 import java.util.ArrayList;
 import java.util.List;
 
+/**
+ * Program description - To find all possible paths from source to destination
+ * <a href="https://en.wikipedia.org/wiki/Shortest_path_problem">Wikipedia</a>
+ *
+ * @author <a href="https://github.com/siddhant2002">Siddhant Swarup Mallick</a>
+ */
 public class AllPathsFromSourceToTarget {
 
     // No. of vertices in graph

@@ -2,9 +2,9 @@
 
 /**
  * Java program for Hamiltonian Cycle
- * (https://en.wikipedia.org/wiki/Hamiltonian_path)
+ * <a href="https://en.wikipedia.org/wiki/Hamiltonian_path">wikipedia</a>
  *
- * @author Akshay Dubey (https://github.com/itsAkshayDubey)
+ * @author  <a href="https://github.com/itsAkshayDubey">Akshay Dubey</a>
  */
 public class HamiltonianCycle {
 
@@ -58,31 +58,31 @@ public boolean isPathFound(int vertex) {
             return true;
         }
 
-        /** all vertices selected but last vertex not linked to 0 **/
+        /* all vertices selected but last vertex not linked to 0 **/
         if (this.pathCount == this.vertex) {
             return false;
         }
 
         for (int v = 0; v < this.vertex; v++) {
-            /** if connected **/
+            /* if connected **/
             if (this.graph[vertex][v] == 1) {
-                /** add to path **/
+                /* add to path **/
                 this.cycle[this.pathCount++] = v;
 
-                /** remove connection **/
+                /* remove connection **/
                 this.graph[vertex][v] = 0;
                 this.graph[v][vertex] = 0;
 
-                /** if vertex not already selected solve recursively **/
+                /* if vertex not already selected solve recursively **/
                 if (!isPresent(v)) {
                     return isPathFound(v);
                 }
 
-                /** restore connection **/
+                /* restore connection **/
                 this.graph[vertex][v] = 1;
                 this.graph[v][vertex] = 1;
 
-                /** remove path **/
+                /* remove path **/
                 this.cycle[--this.pathCount] = -1;
             }
         }

@@ -8,9 +8,6 @@
 import java.util.Queue;
 import java.util.Set;
 
-/**
- * An algorithm that sorts a graph in toplogical order.
- */
 /**
  * A class that represents the adjaceny list of a graph
  */

@@ -6,53 +6,50 @@
 
 /**
  * Java program that implements Kosaraju Algorithm.
- * @author Shivanagouda S A (https://github.com/shivu2002a)
- *
- */
-
-/**
+ * @author <a href="https://github.com/shivu2002a">Shivanagouda S A</a>
+ * <p>
  * Kosaraju algorithm is a linear time algorithm to find the strongly connected components of a
-   directed graph, which, from here onwards will be referred by SCC. It leverages the fact that the
- transpose graph (same graph with all the edges reversed) has exactly the same SCCs as the original
- graph.
+directed graph, which, from here onwards will be referred by SCC. It leverages the fact that the
+transpose graph (same graph with all the edges reversed) has exactly the same SCCs as the original
+graph.
 
  * A graph is said to be strongly connected if every vertex is reachable from every other vertex.
-   The SCCs of a directed graph form a partition into subgraphs that are themselves strongly
- connected. Single node is always a SCC.
+The SCCs of a directed graph form a partition into subgraphs that are themselves strongly
+connected. Single node is always a SCC.
 
  * Example:
 
-    0 <--- 2 -------> 3 -------- > 4 ---- > 7
-    |     ^                      | ^       ^
-    |    /                       |  \     /
-    |   /                        |   \   /
-    v  /                         v    \ /
-    1                            5 --> 6
+0 <--- 2 -------> 3 -------- > 4 ---- > 7
+|     ^                      | ^       ^
+|    /                       |  \     /
+|   /                        |   \   /
+v  /                         v    \ /
+1                            5 --> 6
 
-    For the above graph, the SCC list goes as follows:
-    0, 1, 2
-    3
-    4, 5, 6
-    7
+For the above graph, the SCC list goes as follows:
+0, 1, 2
+3
+4, 5, 6
+7
 
-    We can also see that order of the nodes in an SCC doesn't matter since they are in cycle.
+We can also see that order of the nodes in an SCC doesn't matter since they are in cycle.
 
- {@summary}
+{@summary}
  * Kosaraju Algorithm:
-    1. Perform DFS traversal of the graph. Push node to stack before returning. This gives edges
- sorted by lowest finish time.
-    2. Find the transpose graph by reversing the edges.
-    3. Pop nodes one by one from the stack and again to DFS on the modified graph.
-
-    The transpose graph of the above graph:
-     0 ---> 2 <------- 3 <------- 4 <------ 7
-    ^     /                      ^ \       /
-    |    /                       |  \     /
-    |   /                        |   \   /
-    |  v                         |    v v
-    1                            5 <--- 6
-
-    We can observe that this graph has the same SCC as that of original graph.
+1. Perform DFS traversal of the graph. Push node to stack before returning. This gives edges
+sorted by lowest finish time.
+2. Find the transpose graph by reversing the edges.
+3. Pop nodes one by one from the stack and again to DFS on the modified graph.
+
+The transpose graph of the above graph:
+0 ---> 2 <------- 3 <------- 4 <------ 7
+^     /                      ^ \       /
+|    /                       |  \     /
+|   /                        |   \   /
+|  v                         |    v v
+1                            5 <--- 6
+
+We can observe that this graph has the same SCC as that of original graph.
 
  */
 

@@ -6,59 +6,56 @@
 
 /**
  * Java program that implements Tarjan's Algorithm.
- * @author Shivanagouda S A (https://github.com/shivu2002a)
- *
- */
-
-/**
+ * @author <a href="https://github.com/shivu2002a">Shivanagouda S A</a>
+ * <p>
  * Tarjan's algorithm is a linear time algorithm to find the strongly connected components of a
-   directed graph, which, from here onwards will be referred as SCC.
+directed graph, which, from here onwards will be referred as SCC.
 
  * A graph is said to be strongly connected if every vertex is reachable from every other vertex.
-   The SCCs of a directed graph form a partition into subgraphs that are themselves strongly
- connected. Single node is always a SCC.
+The SCCs of a directed graph form a partition into subgraphs that are themselves strongly
+connected. Single node is always a SCC.
 
  * Example:
-    0 --------> 1 -------> 3 --------> 4
-    ^          /
-    |         /
-    |        /
-    |       /
-    |      /
-    |     /
-    |    /
-    |   /
-    |  /
-    | /
-    |V
-    2
-
-    For the above graph, the SCC list goes as follows:
-    1, 2, 0
-    3
-    4
-
-    We can also see that order of the nodes in an SCC doesn't matter since they are in cycle.
-
- {@summary}
-    Tarjan's Algorithm:
-    * DFS search produces a DFS tree
-    * Strongly Connected Components form subtrees of the DFS tree.
-    * If we can find the head of these subtrees, we can get all the nodes in that subtree (including
- the head) and that will be one SCC.
-    * There is no back edge from one SCC to another (here can be cross edges, but they will not be
- used).
-
-    * Kosaraju Algorithm aims at doing the same but uses two DFS traversalse whereas Tarjan’s
- algorithm does the same in a single DFS, which leads to much lower constant factors in the latter.
+0 --------> 1 -------> 3 --------> 4
+^          /
+|         /
+|        /
+|       /
+|      /
+|     /
+|    /
+|   /
+|  /
+| /
+|V
+2
+
+For the above graph, the SCC list goes as follows:
+1, 2, 0
+3
+4
+
+We can also see that order of the nodes in an SCC doesn't matter since they are in cycle.
+
+{@summary}
+Tarjan's Algorithm:
+ * DFS search produces a DFS tree
+ * Strongly Connected Components form subtrees of the DFS tree.
+ * If we can find the head of these subtrees, we can get all the nodes in that subtree (including
+the head) and that will be one SCC.
+ * There is no back edge from one SCC to another (here can be cross edges, but they will not be
+used).
+
+ * Kosaraju Algorithm aims at doing the same but uses two DFS traversalse whereas Tarjan’s
+algorithm does the same in a single DFS, which leads to much lower constant factors in the latter.
 
  */
 public class TarjansAlgorithm {
 
     // Timer for tracking lowtime and insertion time
     private int time;
 
-    private List<List<Integer>> sccList = new ArrayList<List<Integer>>();
+    private final List<List<Integer>> sccList = new ArrayList<List<Integer>>();
 
     public List<List<Integer>> stronglyConnectedComponents(int v, List<List<Integer>> graph) {
 

@@ -1,43 +1,37 @@
-/**
- * Author : Suraj Kumar
- * Github : https://github.com/skmodi649
- */
+package com.thealgorithms.datastructures.lists;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
 
 /**
+ * @author <a href="https://github.com/skmodi649">Suraj Kumar</a>
+ * <p>
  * PROBLEM DESCRIPTION :
  * There is a single linked list and we are supposed to find a random node in the given linked list
- */
-
-/**
+ * <p>
  * ALGORITHM :
  * Step 1 : START
  * Step 2 : Create an arraylist of type integer
  * Step 3 : Declare an integer type variable for size and linked list type for head
  * Step 4 : We will use two methods, one for traversing through the linked list using while loop and
  * also increase the size by 1
- *
+ * <p>
  * (a) RandomNode(head)
  * (b) run a while loop till null;
  * (c) add the value to arraylist;
  * (d) increase the size;
- *
+ * <p>
  * Step 5 : Now use another method for getting random values using Math.random() and return the
  * value present in arraylist for the calculated index Step 6 : Now in main() method we will simply
  * insert nodes in the linked list and then call the appropriate method and then print the random
  * node generated Step 7 : STOP
  */
-
-package com.thealgorithms.datastructures.lists;
-
-import java.util.ArrayList;
-import java.util.List;
-import java.util.Random;
-
 public class RandomNode {
 
-    private List<Integer> list;
+    private final List<Integer> list;
     private int size;
-    private static Random rand = new Random();
+    private static final Random RAND = new Random();
 
     static class ListNode {
 
@@ -63,10 +57,23 @@ public RandomNode(ListNode head) {
     }
 
     public int getRandom() {
-        int index = rand.nextInt(size);
+        int index = RAND.nextInt(size);
         return list.get(index);
     }
 
+    /**
+     * OUTPUT :
+     * First output :
+     * Random Node : 25
+     * Second output :
+     * Random Node : 78
+     * Time Complexity : O(n)
+     * Auxiliary Space Complexity : O(1)
+     * Time Complexity : O(n)
+     * Auxiliary Space Complexity : O(1)
+     * Time Complexity : O(n)
+     * Auxiliary Space Complexity : O(1)
+     */
     // Driver program to test above functions
     public static void main(String[] args) {
         ListNode head = new ListNode(15);
@@ -80,18 +87,3 @@ public static void main(String[] args) {
         System.out.println("Random Node : " + randomNum);
     }
 }
-/**
- * OUTPUT :
- * First output :
- * Random Node : 25
- * Second output :
- * Random Node : 78
- * Time Complexity : O(n)
- * Auxiliary Space Complexity : O(1)
- * Time Complexity : O(n)
- * Auxiliary Space Complexity : O(1)
- */
-/**
- * Time Complexity : O(n)
- * Auxiliary Space Complexity : O(1)
- */