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The purpose of LeetCode is to provide you hands-on training on coding questions. The Online Judge gives you immediate feedback on the correctness and efficiency of your algorithm which facilitates a great learning experience.

Primary LanguageJava

LeetCode

The purpose of LeetCode is to provide you hands-on training on real coding interview questions. The Online Judge gives you immediate feedback on the correctness and efficiency of your algorithm which facilitates a great learning experience.

The mission of LeetCode is to help software engineers enhance their skills, expand their knowledge, and prepare for technical interviews. They have over 1800 questions broken up into 3 skill levels: easy, medium, and hard. In addition, Premium members get access to problem sets based on company questions.

Approach to Solving Questions

Thoughts Before Coding

  • Finding different approaches for the problem
    • Brute force approach
    • Optimization to the brute force approach
    • Optimized approach
  • Finding the right data structure
    • We need to map a value to another value? HashMap!
    • We need a linear set of information? Array
    • We need to keep track of a count of different elements? HashMap!
    • We need to have quick lookup for individual elements? HashSet!
    • We need to model a network of people? Graph!
    • Matching Brackets? Stack!
    • We need to cache results? Array, Matrix, or Map
  • Any algorithms that can be used
    • Finding cycles? 2 POINTER APPROACH
    • Searching in sorted array? BINARY SEARCH
    • Finding shortest path between 2 nodes in a graph? BREATH FIRST SEARCH
    • Need a way to produce a path between 2 nodes? HASHMAP TO KEEP TRACK OF PREVIOUS ELEMENTS
    • Need to prevent overlapping recursive calls? MEMOIZATION
    • Save memory from our recursive stack? BOTTOM UP APPROACH
    • 2 Sorted Arrays? 2 POINTER APPROACH
    • BFS or DFS?
    • Sliding window?
    • Prefix sum?
  • Optimizing time and space complexity
    • Can we use a different data structure to improve our runtime?
    • Should we use a different algorithm to improve our runtime?
    • Can we use a different data structure use less space?
  • How do we approach dynamic programming problems?
    • First find the brute force recursive approach
    • Then look for overlapping recursive calls,
      • Provide the top down memoization solution
    • Lastly, flip the solution around and find the bottom up approach
      • The bottom up approach is the iterative approach
    • Can the bottom up approach be optimized?
      • 3D -> 2D
      • 2D -> 1D
      • 1D -> Constant Space
  • Trade-offs between time and space?
    • Should we use HashMap/HashSet for quicker lookup?
    • Can we save previously seen values in a data structure?
    • Instead of using data structure, can we use a single value to represent the information we need?
    • Can we find an iterative approach instead of a recursive approach?
    • Can we use array to replace our data structures?
      • This will allow a faster runtime
  • Side Cases to Consider
    • Are there negative values?
    • Will the constraints effect our answer?
    • Will our additions overflow?
    • Off by one errors?
    • Is the value inside the boundary of allowed values?

Answer

  • Write our a semi-pseudocode explaining our solution to the problem
    • Validate our input
      • What if our input is null?
      • What if our input is empty?
      • What if our input is negative or equal to 0?
    • What algorithm are we using?
    • What data structure will we be instantiating?
      • Stack
      • Queue
      • List
      • Map
      • Set
      • Priority Queue
    • Any side cases to consider when implementing the code
      • Look out for off-by 1 errors
      • What are the terminating conditions of loops
    • For recursive implementations
      • What are the parameters?
      • What are the base cases?
      • What do we do in each of the recursive call?
    • For Tree Problems
      • What can we do at each of the nodes?
      • What do we do if the current node is null?
      • What do we do at leaf nodes?
      • What kind of traversal?
        • Preorder
        • Inorder
        • Postorder
    • For Graph Problems
      • Do we need to create a representation of the graph?
      • BFS or DFS?
      • Accounting for cycles?
    • For Linked List Problems
      • Accounting for cycles?
      • What do we need to perform at each node?
      • What if we reached the last node?
      • Singly Linked List or Doubly Linked List?
    • For list problems
      • Which data structure is more optimized for this question?
        • LinkedList or ArrayList?
      • Can the list be replaced by an array?
    • For array problems
      • What should we do at each of the index?
      • Does the value at the current index rely on the value at another index?
  • Is the code optimized?
    • What can we do to optimize to code?
    • Can we restructure the code?
    • Are there vague variable naming?
    • Can we create methods to create cleaner code?

Time and Space Complexity

  • Average case?
  • Worst case?
  • Is the answer optimized?
    • Can you get a better time and space complexity?
  • Are you accounting for the time complexity in all of the methods inside the class?