Greedy Algorithm vs Dynamic Programming Report Writing - IT Assignment Help

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Abstract

The use of algorithm design techniques is based over the requirement to solve various complex computational problems. The selection of a proper technique helps attain an efficient solution to the problem. This report aims at discussing the literature associated to two of the most commonly used algorithm design techniques including greedy algorithm and dynamic programming. The selected research papers for this paper comprise of ‘Track- before-detect algorithm based on dynamic programming for multi-extended targets detection’ and ‘Greedy Algorithm based Track-before-Detect in Radar Systems’. This literature review also aims at comparing these two algorithm design techniques on the basis of their implementation in track before detect algorithms.

Introduction

According to Skiena (2020), the efficiency of an algorithm is based over its design approach and the effectiveness of the design approach is based over the various techniques available to design an algorithm. One of the most important aspects related to algorithm design includes the development of such an algorithm which is able to solve any task irrespective of its complexity with minimum space and time. Hence, there are many such techniques which can be used to develop algorithms such as randomized algorithm, backtracking, greedy algorithm and dynamic programming. This report is based over the review of literature associated to the two algorithm design techniques comprising of dynamic programming and greedy algorithm. The literature is base over two research papers titled ‘Track-before-detect algorithm based on dynamic programming for multi-extended targets detection’ and ‘Greedy Algorithm based Track-before-Detect in Radar Systems’.

Literature Review

Greedy Algorithm
As per Cerrone, Cerulli and Golden (2017), the development of Greedy algorithms was based over the need for software which could solve a large number of problems related to computational optimization. The working of this algorithm can be described as an algorithmic paradigm which is able to generate a solution a piece at a time in which the choice of the next piece is made with such an approach that it is able to provide a benefit which is obvious and immediate. An example of greedy algorithm technique-based algorithm is the Kruskal’s algorithm which is used in graph theory as a method to find the minimum spanning tree for a connected weighted graph by adding increasing cost arcs at each step in the process (Li, Xia and Wang 2017).

Dynamic Programming

According to Bouman, Agatz and Schmidt (2018), dynamic programming approaches are used in cases which need to find optimal solutions for a problem in which these optimal solutions have an optimal substructure such that they can be split in to form optimal solutions to several smaller sub-problems. An example of dynamic programming is the Golomb sequence which is used to describe the sequence of non-decreasing integers where the ‘nth’ the term is same as the number of times ‘n’ appears in the sequence of integers.

Comparison between greedy algorithm and dynamic programming
There have been various algorithm design techniques been developed and tested but the use of greedy algorithm and dynamic programming have been two of the widely used design techniques. Hence, there have also been instances where one technique has been found to be better suited than the other due to certain requirements of the algorithm being developed. Considering the case of working methodologies for both techniques, the greedy algorithm is based over such an approach that it calculates its solution by making choices in the serially forward path and never looks back or considers the previous or last choice whereas, in the case of dynamic programming, the algorithm develops its solution from top to down or down to up by dividing them into smaller sub-problems and sub-solutions. Also, considering recursion as a means of comparison, greedy algorithm is based over the method of problem- solving heuristic which makes locally optimal choices at each stage of the computation process whereas considering dynamic programming, the approach is based over a recurrent formula which might make use of previously calculated sub-solutions.

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