Ashish Kumar
Methods For Solving Problems in Data Structures and Algorithms

An algorithm is a plan for resolving the coding problem. Every solution begins with a strategy. Therefore, to complete the task, programmers must learn to create an effective algorithm and translate this "algorithm" into the appropriate code.

However, there are several coding issues with data structures and algorithms, and most of the time, we are unfamiliar with these issues. As a result, programmers need to become self-assured problem solvers who are not deterred by a problem's complexity. Our long-term objective should be straightforward: to acquire the ability to quickly and correctly create code.

As we continue to practice, we will get more problem-solving expertise, and our work will become simpler.

For every coding problem, we should practice the following fundamental skills:

Creating a strategy for understanding the problem
Considering a sound fundamental solution
Creating an elaborate pseudocode solution
Examining a solution's effectiveness
Furthering the solution's optimization
Converting erroneous code from pseudocode

The crucial question at this point is — Is there a simple, step-by-step technique for approaching and resolving coding problems? If so, what are the necessary actions?
Let's consider and research!

Step 1: Understanding the problem

Every little thing counts when designing an adequate answer to a problem. The approach to the solution can occasionally differ if the question is slightly altered. Spending a little extra time on the problem will boost our confidence afterwards. The truth is that we never want to discover in the middle of a project that we misunderstood the issue.

Whether or not we have read through a question, we should still read it numerous times. So get a piece of paper, and while you work through the issue, get everything down. By looking at a few samples, we can better understand how many situations our algorithm can handle in any potential input-output patterns. We should also investigate the case of huge input, edge cases, and invalid input. A problem description may occasionally have one or more of the following deficiencies:

The problem description can be predicated on some unstated premises.
The problem description can be vague or lacking.
There are several contradictions in the problem description.

For detailed and technical information, refer to the data structure training, and become a DSA expert.

The abstract explanation of the problem description in our natural languages may be to blame for these deficiencies. Therefore, we must recognize this deficit and cooperate with the interviewer or problematic provider to clarify it. These are the questions that we should start by looking for answers to:

What are the input and output?
What kind of data is offered?
What is the input's dimension or scale?
How are files stored? Which data structure is it?
Are there any unique requirements or arrangements in the data?
What guidelines apply to using the data?

Step 2: Formulating correct basic solution

The best method would be to think of a correct solution that comes immediately into our thought. It does not matter, even if it is in an inefficient method, i.e. having a valid and inefficient answer is much better than an incorrect solution or significant delay in the solution. This might help us in many ways:
Help us to start off with good motivation or confidence.
Give the interviewer a great talking point to get the conversation going.
Here are some illustrations of patterns using brute force: Three nested loops, two nested loops, extra memory, sorting, double traversal of the binary tree, taking into account every sub-array or substring, exhaustive search, etc., are all examples of solutions.
Step 3: Designing efficient pseudocode solutions
The best problem-solving knowledge and techniques, along with various problem-solving techniques, should be used at this stage. One applicable reality is that it can be challenging to transition from a simple algorithm to the most effective algorithm in a single step. Every time, the preceding algorithm must be optimized, and we must end when there is no more room for improvement. Further optimization can be significantly aided by reviewing the problem description again and looking for new information. For instance:

We can consider applying a single loop, a two-pointers technique, the concept of binary search, etc., if the input array is sorted or has a similar sorted order.
If the input array is not sorted, we can use the divide-and-conquer strategy, hash tables, sorting with two pointers, etc.
If a subarray of size k requires some output, we can consider using a hash table, sliding window concept, etc.
The concept would be straightforward: we should learn how to apply effective problem-solving strategies to a variety of data types. The core idea is continuously thinking, analyzing, and looking for a better solution.
Step 4: Transforming pseudocode into a correct, clean, and optimized code

Finally, we need to write actual code in our preferred programming languages, such as C++, Java, Python, C#, JavaScript, etc., for each line of pseudocode. Remember to verify that the actual output matches the expected output while testing the existing code with sample test data. Before writing the code, discuss the sample data or test cases with the interviewer.

The code may need to be simplified and optimized through a few observational rounds. Once we have finished coding the code, we need to consider the following:
Does this code successfully handle all inputs, even the most unlikely ones?
Can we make the code even more efficient? Can you eliminate a loop, some variables, or some extra space?

Hope you got the answers to how to solve problems with data structures methods. If you’re looking for resources to learn DSA for your tech career, join the best data structure course and become certified by IBM.Folder: 2022-12-05 17h03

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