Understanding Algorithms: Design Techniques of Algorithms

By Harshvardhan Mishra Feb 26, 2024
Understanding Algorithms: Design Techniques of AlgorithmsUnderstanding Algorithms: Design Techniques of Algorithms

In the world of computer science, algorithms play a vital role in solving problems efficiently. But what exactly is an algorithm? In simple terms, an algorithm is a step-by-step procedure designed to solve a specific problem with a defined input size. It is like a recipe that guides a computer on how to perform a task.

Classification of Algorithms

Algorithms can be classified in various ways, each serving a specific purpose. Let’s explore some of the common classification methods:

  • Implementation Method
  • Design Method
  • Design Approaches
  • Other Classifications

The Importance of Algorithm Classification

Now that we have explored the different classification methods, let’s understand why it is essential to classify algorithms:


Complex algorithms can be challenging to comprehend and compare. By classifying them, it becomes easier to organize and understand their functionalities. It allows researchers and developers to group similar algorithms together, making it simpler to study and analyze their characteristics.

Problem Solving

Not all problems can be solved using the same algorithm. By classifying algorithms, we can identify the most suitable one for a particular problem. This classification helps in selecting the algorithm that provides the most efficient and effective solution.

Performance Comparison

Classifying algorithms enables us to compare their performance in terms of time and space complexity. This comparison helps in choosing the best algorithm for a particular use case, ensuring optimal performance and resource utilization.


By classifying algorithms, it becomes easier to reuse existing algorithms for similar problems. This reusability saves time and effort in developing new algorithms from scratch. It also enhances efficiency by leveraging the knowledge and experience gained from previous algorithm implementations.

Research and Development

Algorithm classification is crucial for research and development in computer science. It helps researchers identify new algorithms and improve existing ones. By understanding the different classifications, researchers can explore new avenues and enhance the efficiency of problem-solving techniques.

In conclusion, the classification of algorithms plays a vital role in computer science. It helps in organizing, understanding, and comparing different algorithms. It enables us to choose the most appropriate algorithm for a specific problem, compare their performance, and improve efficiency. Algorithm classification is essential for research, development, and problem-solving in the field of computer science.

Classification by Implementation Method

Implementation Method has primarily three main categories. These are as follows:

1. Recursion or Iteration: A recursive algorithm is an algorithm which calls itself again and again until a base condition is achieved whereas iterative algorithms use loops and/or data structures like stacks, queues to solve any problem. Every recursive solution can be implemented as an iterative solution and vice versa.
Example: The Tower of Hanoi is implemented in a recursive fashion while Stock Span problem is implemented iteratively.

2. Exact or Approximate: Algorithms that are capable of finding an optimal solution for any problem are known as the exact algorithm. For all those problems, where it is not possible to find the most optimized solution, an approximation algorithm is used. Approximate algorithms are the type of algorithms that find the result as an average outcome of sub outcomes to a problem.
Example: For NP-Hard Problems, approximation algorithms are used. Sorting algorithms are the exact algorithms.

3. Serial or Parallel or Distributed Algorithms: In serial algorithms, one instruction is executed at a time while parallel algorithms are those in which we divide the problem into subproblems and execute them on different processors. If parallel algorithms are distributed on different machines, then they are known as distributed algorithms.

Classification by Design Method

When we look at how algorithms are designed, we can sort them into different groups:

1. Greedy Method:  At each step, this method chooses the best immediate option without thinking about the future.
– For example, problems like Fractional Knapsack and Activity Selection are solved using this approach.

2. Divide and Conquer: This strategy breaks down a big problem into smaller ones, solves them, and then combines the solutions.
– Examples include popular sorting algorithms like Merge sort and Quicksort.

3. Dynamic Programming: Similar to divide and conquer, but here, instead of redoing calculations, we store results to avoid repetition.
– Problems like the 0-1 Knapsack and subset-sum problem are tackled this way.

4. Linear Programming: Involves solving problems with input constraints and maximizing or minimizing linear functions.
– For instance, problems like finding the maximum flow in a Directed Graph.

5. Reduction (Transform and Conquer):  Solves hard problems by turning them into easier ones with known solutions.
– An example is the Selection algorithm for finding the median, which involves sorting the list first.

6. Backtracking: Useful for combinatorial problems with a single unique solution.
– It explores all possible options step by step and backtracks when it hits a dead end.
– Problems like the N-queen problem and maze problem are solved using this method.

7. Branch and Bound: Works well for combinatorial optimization problems with multiple solutions, aiming to find the best one.
– It explores the entire solution space systematically, replacing suboptimal solutions with better ones.
– Examples include Job sequencing and the Travelling Salesman problem.

Classification by Design Approaches

Top-Down Approach:

  • Here, we start by breaking down a big problem into smaller sub-problems.
  • We keep doing this until each small problem is simple enough to solve easily.
  • Then, we solve these smaller problems one by one until we’ve solved the whole big problem.

Bottom-Up Approach:

  • This is like the opposite of the top-down approach.
  • Instead of starting with the big problem, we begin by solving small parts of the problem using programming.
  • Then, we gradually combine these smaller solutions into a complete solution for the entire problem

Read this for better explanation: Top-Down Approach vs Bottom-Up Approach: Explained with Code Example

Other Classification

Besides the broad categories mentioned earlier, algorithms can be classified into other groups as well:

  1. Randomized Algorithms:
    • These algorithms use randomness to achieve faster or more efficient solutions.
    • For example, the Randomized Quicksort Algorithm randomly selects pivot elements for partitioning.
  2. Classification by Complexity:
    • Algorithms can be categorized based on the time they take to solve a problem for a given input size, known as time complexity.
    • Some algorithms have linear time complexity (O(n)), while others may have exponential time complexity.
  3. Classification by Research Area:
    • Different areas of computer science have specific problems and requirements, leading to the development of specialized algorithms.
    • For instance, there are algorithms designed for sorting, searching, machine learning, and many other fields within CS.
  4. Branch and Bound, Enumeration, and Backtracking:
    • These techniques are commonly used in Artificial Intelligence and combinatorial optimization problems.
    • Branch and Bound involves systematically searching through a solution space and replacing suboptimal solutions with better ones.
    • Enumeration refers to the process of listing all possible solutions to a problem.
    • Backtracking is a systematic way of exploring all possible paths to find a solution, often used in combinatorial problems.


Algorithms are like recipes for computers. We can classify them in different ways, like by how they’re designed or how they work. They can be fast because they make smart choices, or slow because they need to check a lot of possibilities. Algorithms help solve all sorts of problems, from sorting numbers to finding the best route on a map. And there are special techniques, like using randomness or organizing data smartly, to make them even better at their jobs

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