Introduction to Algorithms And DataStructure

Introduction to Algorithms And DataStructure

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  Data Structures and Algorithms ByPrasanna Jatla Sr Software Engineer American Express
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  Basic Data Structures •Overview of Arrays • Overview of Linked Lists • Overview of Stacks and Queues
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  What is Recursion • Recursionisa 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.
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  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.
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  Analyzing time complexity •Definition of AsymptoticNotation • Importancein AnalyzingAlgorithm Efficiency • Definition of Big O Notation • Analyzetime complexity of below code.
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  Dynamic Programming • CoreIdea: 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).
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  Solving some Interviewcoding problems • Generate all subsets , given an array of numbers • K sum problem • Check number of uni-value sub nodes in a tree.
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  Greedy Algorithms • CoreIdea: 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.
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  Q & A