Greedy Algorithm vs. Dynamic Programming - IT Assignment Help

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Abstract

Dynamic programming and greedy algorithm are two processes involved towards building solutions for computational programming problems. In this literature review, the key issues and the complex IT related situations have been discussed with examples. The review also discusses the knapsack problem with the analysis of real-world situation along with the feasibility study. The two main research papers selected for review are ‘Dynamic algorithm configuration: Foundation of a new meta-algorithmic framework’ and ‘A smoothed analysis of the greedy algorithm for the linear contextual bandit problem’.

The literature review highlights key findings related to the chosen subject of the study. It was observed that the concepts of greedy algorithm and dynamic programming had a deep and dynamic association. It was further evaluated that both the systems have their own separate reasons and methods of how they function. Besides that, dynamic programming might prove to be a more suitable method to use in comparison with the greedy algorithm.

Introduction

The literature review is set to focus on two key research papers that are ‘Dynamic algorithm configuration: Foundation of a new meta-algorithmic framework’ and ‘A smoothed analysis of the greedy algorithm for the linear contextual bandit problem’.

As mentioned by Kannan, et al. (2018), the greedy algorithm is recognized as an intuitive and simpler algorithm, locally used in optimization problems, which directs optimal choice at each stage aiding in finding the overall optimal way for solving the entire problem. On the other hand, solving a problem by breaking it down into simplifier problems in such a way that the optimal solution of the problem depends upon the solution of the sub-problems is called dynamic programming. An example of dynamic programming can be taken as Fibonacci numbers. Greedy strategy points out the initial stage of the problem. While within the framework of dynamic programming, lists of techniques for optimization are employed for solving the specific aspects.

Usage of Dynamic Programming as an Optimal Solution

According to Wang, et al. (2017), dynamic programming is computational programming along with a mathematical optimization method. Where there is a problem that can be divided into similar sub-problems, dynamic programming is used so that the results can be re-used. For optimization, these algorithms are mostly used. The dynamic algorithm tries to examine the subproblems which were previously solved before solving the in-hand problems. To solve the most challenging problem in the most efficient way dynamic programming is used. It guarantees the perfect solution based on the needs as it requires extremely researched data. Some of the problems which can be solved perfectly using dynamic programming are the longest common subsequence problem, longest repeated subsequence problem, shortest common subsequence, etc. dynamic programming is most promising in solving these problems. Nowadays, dynamic programming is also used for bitcoin mining along with blockchain technology and these, as a result, are yielding positive results.

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