Merge branch 'master' of https://github.com/torgeek/learnxinyminutes-docs

1 files changed, 19 insertions, 7 deletions

diff --git a/dynamic-programming.html.markdown b/dynamic-programming.html.markdown
index 94be22e9..3e3c0413 100644
--- a/dynamic-programming.html.markdown
+++ b/dynamic-programming.html.markdown
@@ -3,13 +3,14 @@ category: Algorithms & Data Structures
 name: Dynamic Programming
 contributors:
     - ["Akashdeep Goel", "http://github.com/akashdeepgoel"]
+    - ["Miltiadis Stouras", "https://github.com/mstou"]
 ---
 
 # Dynamic Programming
 
 ## Introduction
 
-Dynamic Programming is a powerful technique used for solving a particular class of problems as we will see.The idea is very simple, If you have solved a problem with the given input, then save the result for future reference, so as to avoid solving the same problem again.
+Dynamic Programming is a powerful technique used for solving a particular class of problems as we will see. The idea is very simple, If you have solved a problem with the given input, then save the result for future reference, so as to avoid solving the same problem again.
 
 Always remember!
 "Those who can't remember the past are condemned to repeat it"
@@ -22,11 +23,11 @@ Always remember!
 
 ## Example of Dynamic Programming
 
-The Longest Increasing Subsequence problem is to find the longest increasing subsequence of a given sequence. Given a sequence `S= {a1 , a2 , a3, a4, ............., an-1, an }` we have to find a longest subset such that for all `j` and `i`,  `j<i` in the subset `aj<ai`.
+The Longest Increasing Subsequence problem is to find the longest increasing subsequence of a given sequence. Given a sequence `S={ a1, a2, a3, a4, ............., an-1, an }` we have to find a longest subset such that for all `j` and `i`,  `j<i` in the subset `aj<ai`.
 First of all we have to find the value of the longest subsequences(LSi) at every index i with last element of sequence being ai. Then largest LSi would be the longest subsequence in the given sequence. To begin LSi is assigned to be one since ai is element of the sequence(Last element). Then for all `j` such that `j<i` and `aj<ai`, we find Largest LSj and add it to LSi. Then algorithm take *O(n2)* time.
 
 Pseudo-code for finding the length of the longest increasing subsequence:
-This algorithms complexity could be reduced by using better data structure rather than array. Storing predecessor array and variable like largest_sequences_so_far and its index would save a lot time.
+This algorithms complexity could be reduced by using better data structure rather than array. Storing predecessor array and variable like `largest_sequences_so_far` and its index would save a lot time.
 
 Similar concept could be applied in finding longest path in Directed acyclic graph.
 
@@ -42,10 +43,21 @@ for i=0 to n-1
 
 ### Some Famous DP Problems
 
-- Floyd Warshall Algorithm - Tutorial and C Program source code:http://www.thelearningpoint.net/computer-science/algorithms-all-to-all-shortest-paths-in-graphs---floyd-warshall-algorithm-with-c-program-source-code 
-- Integer Knapsack Problem - Tutorial and C Program source code: http://www.thelearningpoint.net/computer-science/algorithms-dynamic-programming---the-integer-knapsack-problem 
-- Longest Common Subsequence - Tutorial and C Program source code : http://www.thelearningpoint.net/computer-science/algorithms-dynamic-programming---longest-common-subsequence 
+- [Floyd Warshall Algorithm - Tutorial and C Program source code](http://www.thelearningpoint.net/computer-science/algorithms-all-to-all-shortest-paths-in-graphs---floyd-warshall-algorithm-with-c-program-source-code)
+- [Integer Knapsack Problem - Tutorial and C Program source code](http://www.thelearningpoint.net/computer-science/algorithms-dynamic-programming---the-integer-knapsack-problem)
+- [Longest Common Subsequence - Tutorial and C Program source code](http://www.thelearningpoint.net/computer-science/algorithms-dynamic-programming---longest-common-subsequence)
 
 ## Online Resources
 
-* [codechef](https://www.codechef.com/wiki/tutorial-dynamic-programming)
+* MIT 6.006: [Lessons 19,20,21,22](https://www.youtube.com/playlist?list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)
+* TopCoder: [Dynamic Programming from Novice to Advanced](https://www.topcoder.com/community/data-science/data-science-tutorials/dynamic-programming-from-novice-to-advanced/)
+* [CodeChef](https://www.codechef.com/wiki/tutorial-dynamic-programming)
+* [InterviewBit](https://www.interviewbit.com/courses/programming/topics/dynamic-programming/)
+* GeeksForGeeks:
+  * [Overlapping Subproblems](https://www.geeksforgeeks.org/dynamic-programming-set-1/)
+  * [Tabulation vs Memoization](https://www.geeksforgeeks.org/tabulation-vs-memoizatation/)
+  * [Optimal Substructure Property](https://www.geeksforgeeks.org/dynamic-programming-set-2-optimal-substructure-property/)
+  * [How to solve a DP problem](https://www.geeksforgeeks.org/solve-dynamic-programming-problem/)
+* [How to write DP solutions](https://www.quora.com/Are-there-any-good-resources-or-tutorials-for-dynamic-programming-DP-besides-the-TopCoder-tutorial/answer/Michal-Danilák)
+
+And a [quiz](https://www.commonlounge.com/discussion/cdbbfe83bcd64281964b788969247253) to test your knowledge.