# When to use Greedy Algorithms in Problem Solving 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.

## Formal Definition

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.

## Advantages of Greedy algorithms

• 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).

## Disadvantages

• 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.

## In general, greedy algorithms have five components,

1. Candidate set, from which a solution is created

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.😊

## Job Scheduling problem

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.

## How to do Problem Solving as a Developer

In this video, I will be talking about problem-solving as a developer 🔴 Subscribe for more https://www.youtube.com/channel/UCMA8gVyu_IkVIixXd2p18NQ?sub_confi...

## How to Install OpenJDK 11 on CentOS 8

What is OpenJDK? OpenJDk or Open Java Development Kit is a free, open-source framework of the Java Platform, Standard Edition (or Java SE).

## Top Tips for Learning Java Programming

In this tutorial, we'll learn Top Tips for Learning Java Programming. Not everyone knows. Do not miss!!!

## Factorial Program in Java | Java Program to Find Factorial

Java is one of the popular languages. It has gained a lot of craze among the programmers because of its platform independence. Java proves itself flexible in web developers, view and user-friendly in programmers. In this post, we'll learn Factorial Program in Java | Java Program to Find Factorial.

## Going Beyond Java 8: Local Variable Type Inference (var) - DZone Java

In this article, we will talk about the most important new feature introduced with Java 10, officially called local variable type inference. An extremely important function in java. You will regret skipping this article.