CSE Seminar Topic on Algorithm

Introduction to Seminar Topic on Algorithm:

The primitive types of algorithms dates back to centuries, ever since mathematics have taken predominance in the society algorithms have also been influencing mathematicians worldwide. So what is an algorithm, it can be loosely defined as a collection of set rules that explains the finite operations sequence of a specific problem.  We use algorithms everywhere, for small mathematical problems to complex ones. An algorithm is commonly used for calculating equations, processing of data, defining logic and understanding various aspect of reasoning.

There should be a definite common characteristics an algorithm should exhibit, namely input, output, finiteness, definiteness and effectiveness. An external data should be given as input which comes out as output after processing.  Certain steps must be stopped after a finite number and should not move on infinitely and it should have clarity and must be unambiguous. Absolutely no intelligence must be used to execute the algorithm steps and once all the steps are completed with success, an algorithm should be terminated fully.

To process a data, computers use algorithms that instruct to perform in a specific way and manner. An algorithm will contain a precise number of finite steps and its flow is controlled in a definite way. Data’s can be stored in the entity in which the algorithm is processed. The different types of asymptotic notations in use are Big Oh, Big Omega, Big theta, Little Oh and Little Omega. 

 The expressing of algorithms can be through

  • Natural Languages
  • Flowcharts
  • Pseudo codes
  • Control tables
  • Programming Languages

A method by which a complex programming structure is dissolved into simpler finite steps is called dynamic programming whereas the greedy approach makes the optimum choice at a given stage and solves the major problem. Greedy algorithms can be classified as pure, relaxed and orthogonal greedy algorithms. The time taken by an algorithm to execute fully is called time complexity. 

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