Algorithm

Getting Started with AlgorithmWhat is an Algorithm?

Characteristics of Algorithm1 Topic

Analysis Framework

Performance Analysis3 Topics

Mathematical Analysis2 Topics

Sorting AlgorithmSorting Algorithm10 Topics

Searching Algorithm6 Topics

Fundamental of Data StructuresStacks

Queues

Graphs

Trees

Sets

Dictionaries

Divide and ConquerGeneral Method

Binary Search

Recurrence Equation for Divide and Conquer

Finding the Maximum and Minimum

Merge Sort

Quick Sort

Stassenâ€™s Matrix Multiplication

Advantages and Disadvantages of Divide and Conquer

Decrease and ConquerInsertion Sort

Topological Sort

Greedy MethodGeneral Method

Coin Change Problem

Knapsack Problem

Job Sequencing with Deadlines

Minimum Cost Spanning Trees2 Topics

Single Source Shortest Paths1 Topic

Optimal Tree Problem1 Topic

Transform and Conquer Approach1 Topic

Dynamic ProgrammingGeneral Method with Examples

Multistage Graphs

Transitive Closure1 Topic

All Pairs Shortest Paths6 Topics

BacktrackingGeneral Method

NQueens Problem

Sum of Subsets problem

Graph Coloring

Hamiltonian Cycles

Branch and Bound2 Topics

0/1 Knapsack problem2 Topics

NPComplete and NPHard Problems1 Topic
Participants2253
The greedy method suggests that one can devise an algorithm that works in stages, considering one input at a time.At each stage,a decision is made regarding whether a particular input is in an optimal solution. This is done by considering the inputs in an order determined by some selection procedure. If the inclusion of the next input into the partially constructed optimal solution will result in an infeasible solution,then this input is not added to the partial solution.Otherwise,it is added. The selection procedure itself is based on some optimization measure. This measure may be the objective function.In fact, several different optimization measures may be plausible for a given problem.Most of these,however, will result in algorithms that generates suboptimal solutions. This version of the greedy technique is called the subset paradigm.
We can describe the subset paradigm abstractly, but more precisely than above, by considering the control abstraction in Algorithm 1. The function Select selects an input from a[ ] and removes it. The selected input’s value is assigned to x. Feasible is a Booleanvalued function that determines whether x can be included into the solution vector. The function Union combines x with the solution and updates the objective function. The function Greedy describes the essential way that a greedy algorithm will look, once a particular problem is chosen and the functions Select,Feasible,and Union are properly implemented.