Dynamic Programming Interview Questions | Practice Problems

 
A Dynamic programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions using a memory-based data structure (array, map,etc). Each of the subproblem solutions is indexed in some way, typically based on the values of its input parameters, so as to facilitate its lookup. So the next time the same subproblem occurs, instead of recomputing its solution, one simply looks up the previously computed solution, thereby saving computation time. This technique of storing solutions to subproblems instead of recomputing them is called memoization.

Here’s brilliant explanation given by Jonathan Paulson on Quora on concept of Dynamic Programming to a kid.

*writes down “1+1+1+1+1+1+1+1 =” on a sheet of paper*
“What’s that equal to?”
*counting* “Eight!”
*writes down another “1+” on the left*
“What about that?”
*quickly* “Nine!”
“How’d you know it was nine so fast?”
“You just added one more”
“So you didn’t need to recount because you remembered there were eight! Dynamic
Programming is just a fancy way to say ‘remembering stuff to save time later'”

 
Below is the list of commonly asked interview questions that can be solved using Dynamic programming –

 

  1. Longest Common Subsequence | Introduction & LCS Length
     
  2. Longest Common Subsequence | Space optimized version
     
  3. Longest Common Subsequence of K-sequences
     
  4. Longest Common Subsequence | Finding all LCS
     
  5. Longest Common Substring problem
     
  6. Longest Palindromic Subsequence using Dynamic Programming
     
  7. Longest Repeated Subsequence problem
     
  8. Implement Diff Utility
     
  9. Shortest Common Supersequence | Introduction & SCS Length
     
  10. Shortest Common Supersequence | Finding all SCS
     
  11. Shortest Common Supersequence | Using LCS
     
  12. Longest Increasing Subsequence using Dynamic Programming
     
  13. Longest Bitonic Subsequence
     
  14. Increasing Subsequence with Maximum Sum
     
  15. Longest Decreasing Subsequence Problem
     
  16. The Levenshtein distance (Edit distance) problem
     
  17. Find size of largest square sub-matrix of 1’s present in given binary matrix
     
  18. Matrix Chain Multiplication
     
  19. Find the minimum cost to reach last cell of the matrix from its first cell
     
  20. Find longest sequence formed by adjacent numbers in the matrix
     
  21. Count number of paths in a matrix with given cost to reach destination cell
     
  22. 0-1 Knapsack problem
     
  23. Maximize value of the expression
     
  24. Partition problem
     
  25. Subset sum problem
     
  26. Minimum Sum Partition problem
     
  27. Find all N-digit binary strings without any consecutive 1’s
     
  28. Rod Cutting
     
  29. Maximum Product Rod Cutting
     
  30. Coin change-making problem (unlimited supply of coins)
     
  31. Coin Change Problem – Find total number of ways to get the denomination of coins
     
  32. Longest alternating subsequence
     
  33. Count number of times a pattern appears in given string as a subsequence
     
  34. Collect maximum points in a matrix by satisfying given constraints
     
  35. Count total possible combinations of N-digit numbers in a mobile keypad
     
  36. Find optimal cost to construct binary search tree
     
  37. Word Break Problem
     
  38. Word Break Problem | Using Trie Data Structure
     
  39. Total possible solutions to linear equation of k variables
     
  40. Wildcard Pattern Matching
     
  41. Find probability that a person is alive after taking N steps on the island
     
  42. Calculate sum of all elements in a sub-matrix in constant time
     
  43. Find maximum sum K x K sub-matrix in a given M x N matrix
     
  44. Find maximum sum submatrix present in a given matrix
     
  45. Find maximum sum of subsequence with no adjacent elements
     
  46. Maximum subarray problem (Kadane’s algorithm)
     
  47. Single-Source Shortest Paths – Bellman Ford Algorithm
     
  48. All-Pairs Shortest Paths – Floyd Warshall Algorithm
     



 
 

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