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An algorithm is a set of instructions used to solve a class of issues or perform a computation. Algorithms are not ambiguous, especially in maintaining data processing, automated reasoning, calculation, etc. Computer systems use the instructions in algorithms to perform specific operations. For example, you can automate the paycheck for employees.
Features of a good algorithm
 It should not be ambiguous – The algorithm must be unambiguous in every stage, and its source of input and output must be well known and should only have one meaning.
 Uniqueness – The results and efficiency of every step are uniquely identified, and this means that it relies on input and specific results of the primary steps.
 Input – Algorithms receive the output
 Feasibility – The process should be feasible with the sources given.
 Independence – Every algorithm must have a set of instructions that must not be similar to those of any other programming language.
 Precision – Every step in the algorithm must be well defined.
The efficiency of an algorithm is overviewed through different methods. This must happen before and after implementation, which is stated below;
 A posterior analysis – This is an empirical analysis where the algorithm is implemented based on other programming languages. In this analysis, the algorithm is done on the targeted computer system. The experts must ensure that they overview the space needed and the total running time of the algorithm.
 Priori analysis – This can be described as a theoretical base of an algorithm. To know whether an algorithm is efficient and performing well, factors such as processor and constant speed must be measured. This should not have any effect on the implementation of the algorithm.
Algorithm design techniques in data structures
Whichever programming language you use, you must learn algorithm design in data structures to have more knowledge of building scalable systems. Selecting the right design for algorithms is not easy work. Below are some of the main algorithm design techniques;
 Greedy algorithms
 Bruteforce
 Divide and conquer
 Branch and bound algorithm
 Dynamic programming
 Randomized algorithm
 Backtracking
All problems are solved in different ways, and some techniques deliver better results than others. Algorithm analysis is used to measure the performance and effectiveness of algorithms. Algorithm analysis helps determine the quality of an algorithm based on parameters like maintainability, userfriendliness, security, usage, space usage, and others.
Points to remember when structuring an algorithm
 Each point in the process is important and must be straightforward.
 Ensure that you use a syntax where necessary
 The algorithm used must be successful among the methods of giving guidance
 All the information sources must be well defined
 An algorithm must be well planned to be connected to different programming languages.
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Types of greedy algorithms
 Pure greedy algorithms
 Relaxed greedy algorithms
 Orthogonal greedy algorithms
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How divideandconquer algorithms works
The process of divideandconquer follows the following steps;
 Divide – The first step is to divide the given problem into small problems or subproblems through recursion.
 Conquer – The second step is solving the smaller problems recursively. If the problems are small enough, find solutions completely.
 Combine – After you get the solutions in the subproblems, combine the solutions to get the solution to the actual problem.
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