Data Structure With C
- INTRODUCTION TO DS
- ALGORITHMS
- ASYMPTOTIC ANALYSIS
- DYNAMIC PROGRAMMING
- DIVIDE AND CONQUER
- Arrays
- Linked List
- STACK
- EXPRESSION PARSING
- Queue
- GRAPHS
- TREE
- SEARCHING TECHNIQUES
- Binary Search
- Indexed Sequential Search
- Breadth First Search
- Depth First Search
- Interpolation Search
- Complexity
- Binary Search
- Indexed Sequential Search
- Breadth First Search
- Depth First Search
- Interpolation Search
- Complexity
- Binary Search
- Indexed Sequential Search
- Breadth First Search
- Depth First Search
- Interpolation Search
- Complexity
- Binary Search
- Indexed Sequential Search
- Breadth First Search
- Depth First Search
- Interpolation Search
- Complexity
- Binary Search
- Indexed Sequential Search
- Breadth First Search
- Depth First Search
- Interpolation Search
- Complexity
- Binary Search
- Indexed Sequential Search
- Breadth First Search
- Depth First Search
- Interpolation Search
- Complexity
- SORTING
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Bubble Sort
- Selection Sort
- Quick Sort
- Insertion Sort
- Radix Sort
- Merge Sort
- Shell Sort
- Heap Sort
- Complexity
- Recursion
- Backtracking
- Hashing
DIVIDE AND CONQUER
Divide the problem into a number of sub-problems that are smaller instances of the same problem. This step follows a recursive approach to break the problem into modules.
Conquer the sub-problems by solving them recursively. If the sub-problem sizes are small enough, however, just solve the sub-problems in a straightforward manner.
Combine the solutions to the sub-problems into the solution for the original problem. Here the merging process performed recursively till the solution for the actual problem formulated.
Examples: Following are some problems that can be solved by divide and Conquer approach.
- Binary Search
- Heap Constructions
- Tower of Hanoi
- Exponentiations
- Quick Sort, Merge Sort
- Matrix Multiplications
- Closest Pairs etc.
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