Algorithmic thinking has recently become a buzzword among programmers. It is a method for solving problems based on a clear definition of steps: logical and repetitive. This can help us understand various problem-solving strategies.
Let's understand the importance of algorithmic thinking from a simple perspective. If we observe, two key skills are essential for solving coding problems in programming:
Some programmers may struggle with the first skill, which requires algorithmic thinking. On the other hand, during interviews, some employers may be more interested in how a candidate approaches a problem than the actual implementation of the solution. So, demonstrating expertise in algorithmic thinking can be a way to impress a potential employer.
The critical question is: How do we develop algorithmic thinking independently of learning programming? There are several ways to do it! Let's explore.
Solving algorithmic puzzles is an enjoyable activity for developing algorithmic thinking and improving our ability to break down complex problems and develop logical solutions. It will help us to approach coding problems more abstractly and analytically.
For example, we can learn several problem-solving strategies using puzzles:
Here are some more examples of such types of puzzles:
Here are some critical ideas related to solving algorithmic puzzles:
To solve any problem, it's important to follow a series of well-defined steps. Practising these steps can help us save time, identify patterns that can be applied to multiple coding problems, and effectively find solutions to even the most complex challenges.
To effectively solve a problem, it's important to clearly understand what is being asked. This involves drawing a visualization of the problem and asking various critical questions.
In addition to this, it's important to understand the computational and non-computational details of the problem like data structures used, input distribution, specific constraints on the input, mathematical properties related to the problem, etc.
After analyzing the problem description, we need to make predictions about the concepts and approaches necessary to solve it. We can try to think around these questions:
Identifying concepts and approaches used to solve similar problems can save a lot of time and effort.
To design a good solution, it can be helpful to first explore a hand-written approach by going through several examples and developing a general step-by-step strategy. This will involve carefully thinking about each step and identifying common actions for all examples.
Two types of thinking are important at this stage:
It can be helpful to describe the operations needed to transform the given input into the desired output on paper. From there, you can write out the steps in simple English and translate them into pseudocode or a flowchart. This can help clarify the logic and make it easier to write the final correct code.
Once you have developed a pseudocode solution, you can move on to implementing it in a programming language such as C++, Java, or Python. During this process, it's important to carefully select programming elements that will help create effective code. This may include elements such as a solution function, helper functions, loops, base cases for recursive code, and variables such as local variables, global variables, and pointers.
It's also important to pay attention to memory management, pre-processing, and other details that can impact the efficiency and correctness of the code. Remember to follow a good coding style to make your code easy to read and understand.
Once you have implemented your solution, it's important to test it for bugs, analyze its time and space complexity, and think about ways to optimize it further. Here are some ideas that can be helpful:
Here are some popular coding questions to practice the steps of problem-solving. These questions can be solved using four or more approaches.
Many applications use efficient strategies to deliver a great user experience and performance. Understanding these strategies is an important step in developing algorithmic thinking. Here are some good examples of these strategies:
Enjoy learning, Enjoy thinking!
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