# Design and Analysis of Algorithm

BFS(V, E, s) 1.         for each u in V − {s}             ▷ for each vertex u in V[G] except s. 2.             do color[u] ← WHITE 3.                 d[u] ← infinity...

## Graphs Traversal/Searching

Graph Traversal To traverse a graph is to process every node in the graph exactly once. Because there are many paths leading from one node to another, the hardest...

## Introduction to Graphs

Introduction to graphs Graphs are widely-used structure in computer science and different computer applications. We don’t say data structure here and see the difference. Graphs mean to store and...

## Shortest Path by Dynamic programming

Given a cost matrix cost and a position (m, n) in cost, write a function that returns cost of minimum cost path to reach (m, n) from (0, 0)....

## Calculation of binomial Co-efficient using Dynamic Programming

Following are common definition of Binomial Coefficients. 1) A binomial coefficient C(n, k) can be defined as the coefficient of X^k in the expansion of (1 + X)^n. 2)...

## Problems solved using Dynamic programming

Calculation of binomial Co-efficient Making Change Problem Assembly Line Scheduling Knapsack Problem Shortest Path Matrix Chain multiplication Longest Common Subsequence

## Dynamic Programming

Dynamic programming, like the divide-and-conquer method, solves problems by combining the solutions to subproblems. (“Programming” in this context refers to a tabular method, not to writing computer code.) divide-and-conquer...

## Problem solved using Greedy strategy

Problem solved using Greedy strategy Knapsack Prim`s Kruskal Finding Shortest Path Job Sequencing with deadline Activity Selection Problem

## Exponentation

Can we use divide-and conquer to solve the exponentiation problem (or to improve our solution)? What can we divide in half? (What gives the “size” of the problem?) Here’s...

## Problems Solved by divide and conquer strategy

Binary Search Quick Sort Merge Sort Matrix Multiplications Exponential