Introduction to Algorithms And DataStructure
- 1. Data Structures and Algorithms By Prasanna Jatla Sr Software Engineer American Express
- 2. Basic Data Structures • Overview of Arrays • Overview of Linked Lists • Overview of Stacks and Queues
- 3. What is Recursion • Recursionis a programming technique where a function calls itself directly or indirectly. • The function keeps calling itselfuntil a base case is reached, which stops the recursion. • Each recursive call typically creates a newactivation recordon the call stack. • Recursionallows us to break down complex problems into smaller, self-similar subproblems. Advantages ofRecursion: • Elegant solutions: Recursioncan provide elegantandconcise solutions to problems that might be cumbersome with iterative approaches (using loops). • Self-similar problems: Recursionshines when dealing with problems that exhibitself-similarity, where the solution involves solving smaller versions ofthe originalproblem. • Improved readability: In some cases, recursive code canbe more readable and easier tounderstandcompared to iterative solutions, especiallyfor problems with inherentself-similarity.
- 4. Real-world Applications of Recursion • Factorials: Calculating factorials of numbers is a classic example of recursion. • Tree traversals: Recursion is commonly used to traverse tree data structures (like binary trees) in a depth-firstmanner. • File system navigation: Recursioncan be used to navigate throughdirectory structureswithin a file system. • Maze solving algorithms: Several maze-solving algorithms, like backtracking,utilize recursionto explore differentpaths within the maze. • Divide and conquer algorithms: Many divide- and-conqueralgorithms, like merge sort and quick sort, rely on recursionto break down problems into smaller subproblems.
- 5. Analyzing time complexity • Definition of AsymptoticNotation • Importancein AnalyzingAlgorithm Efficiency • Definition of Big O Notation • Analyzetime complexity of below code.
- 6. Dynamic Programming • Core Idea: Breaks down a complex problem into smaller, overlapping subproblems. • Solves subproblems: Computes the optimal solutions for each subproblem and stores them for reuse. • Bottom-up approach: Builds solutions for larger subproblems using previously solved smaller ones. • Suitable for: Optimization problems with overlapping subproblems (e.g., shortest path, edit distance).
- 7. Solving some Interview coding problems • Generate all subsets , given an array of numbers • K sum problem • Check number of uni-value sub nodes in a tree.
- 8. Greedy Algorithms • Core Idea: Makes the locally optimal choice at each step with the hope of achieving a globally optimal solution. • Heuristic approach: Employs a set of rules to make seemingly optimal choices at each step. • Simpler implementation: Often easier to understand and implement compared to Dynamic Programming. • No guarantee of global optimality: The locally optimal choice at each step might not lead to the overall best solution.
- 9. Q & A