In this article, you will learn about greedy algorithms and how these can be used in a lot of programming problems.

In this article, you will learn about greedy algorithms and how these can be used in a lot of programming problems.

Greedy algorithms are simple instinctive algorithms used for optimization (either maximized or minimized) problems. This algorithm makes the best choice at every step and attempts to find the optimal way to solve the whole problem. This means that it makes a locally optimal choice in the hope that this choice will lead to a globally optimal solution.** Ge_nerally, greedy algorithms do not provide globally optimized solutions._**

*This algorithm is very successful in many tasks but, in some cases, it can fail to produce the optimal solution.*

A greedy algorithm is an algorithmic paradigm that follows the problem-solving heuristic of making the locally optimal choice at each stage with the hope of finding a global optimum.

- Always easy to choose the best option. Usually, requires sorting choices.
- Analyzing the run time for greedy algorithms will generally be much easier than for other techniques (like Divide and conquer).

- It is not suitable for problems where a solution is required for every subproblem like sorting. In such problems, the
**greedy**strategy can be wrong, in the worst case even lead to a non-optimal solution. - Even with the correct algorithm, it is hard to prove why it is correct.

, from which a solution is created*Candidate set*

*2. _**_Selection function***, which chooses the best candidate to be added to the solution

3.** *Feasibility function***, that is used to determine if a candidate can be used to contribute to a solution

4. ** Objective function**, which assigns a value to a solution, or a partial solution

5. ** Solution function**, which will indicate when we have discovered a complete solution

Now let’s discuss how to solve a programming problem with the Greedy approach. Below mentioned are some problems that use the optimal solution using the Greedy approach.

- Travelling Salesman Problem
- Kruskal’s Minimal Spanning Tree Algorithm
- Dijkstra’s Minimal Spanning Tree Algorithm
- Knapsack Problem
- Job Scheduling Problem

Let’s discuss how to solve the Job Scheduling problem in detail.😊

In this problem, they give an array of jobs where every job has a deadline and associated profit if the job is finished before the deadline. And they say that every job takes a single unit of time, so the minimum possible deadline for any job is 1. The question asks how to maximize the total profit if only one job can be scheduled at a time.

When reading this question, we can say this is a maximization problem. So we can follow greedy algorithm to solve this problem. Let’s analyze this problem as below.

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