You want to excel in algorithms. What are the most important strategic thinking skills you need?

This process enables individuals to analyze and solve each component independently, resulting in a more systematic and efficient approach to issue solving. Decomposing a problem allows you to better grasp its structure, identify critical parts and design tailored solutions for each subproblem. This strategy streamlines the entire problem-solving process while simultaneously promoting a better knowledge of the problem's complexities. Problem decomposition is a basic tool in algorithmic thinking, allowing individuals to approach complex difficulties with a systematic and organized attitude.

Every real life problem can be solved either by 1 algorithm, or multiple algorithms working together. There are also times when these algorithms need modifications too. Hence, always break into sub-problems if possible, and then find the right algorithms to work together. For eg: let's take an example of booking cabs. The idea is "find an available cab near me". It can be broken down into sub-problems, such as find my location, and then find nearest cabs. Then, find the next possible available one. As multiple people are searching for cabs in the same area, and a cab can be booked by only customer, race conditions need a solve too, and so on. Inculcating the skill of analytical ability for decomposing the problem is very important.

1. Problem decomposition: Breaking complex problems into smaller, manageable tasks. 2. Pattern recognition: Identifying similarities between problems to apply known solutions. 3. Algorithm design: Crafting efficient, scalable, and adaptable algorithms. 4. Critical analysis: Evaluating trade-offs between time complexity, space complexity, and performance. 5. Creativity: Generating novel approaches to tackle unique challenges. 6. Optimization: Iteratively refining algorithms for improved efficiency. 7. Abstraction: Extracting essential concepts to generalize solutions. 8. Debugging: Systematically identifying and rectifying errors in algorithm implementation.

A buggy solution always happen when a problem is not decomposed. To make problem solving enjoyable, break it down into smaller bit by bit until you solve the complex problem

To excel in algorithms, crucial strategic thinking skills include problem decomposition, abstraction, and pattern recognition. Problem decomposition involves breaking down a complex problem into simpler, manageable components, making it easier to devise solutions. Abstraction requires looking beyond the specifics of a problem to understand its essence, enabling the application of generic solutions to multiple scenarios. Pattern recognition is key to identifying similarities between problems, allowing for the reuse of algorithms and techniques.

Algorithm selection is a crucial skill for coding interviews as well, as it involves choosing the most suitable algorithm for a given problem based on the requirements, constraints, and trade-offs. - Interviewers often evaluate candidates' ability to select appropriate algorithms for specific problems, as it demonstrates their understanding of different algorithms and their applicability to various scenarios.

Effective algorithm selection is one of the most critical aspects of any modeling process. A clear understanding of the system characteristics is required. Even if the system is considered as a black box, understanding its characteristics is imperative. Whether the systems is time variant or not, linear or not etc. plays an important role in algorithm selection. In short, clear understanding of the system enables us to choose the 'right' algorithm.

Algorithms selection is done on the basis of time complexity and space complexity. Choosing the best algorithm to solve a problem is crucial in acing coding interviews.

Algorithm selection is also about - how easily you can explain to the interviewer about the algorithm. If there are no hard constraints then I would rate algorithm selection like below: Correctness > Simplicity > Time complexity > Space Complexity

Algorithm design is crucial for excelling in algorithms. It involves creating or modifying algorithms to solve problems efficiently. This skill is beneficial, especially with the constant emergence of new algorithms, as it enables adaptation to specific needs, innovation, and optimization in addressing evolving challenges.

Finding the right balance between time and space complexity while structuring algorithms is essential to a successful implementation, for example, an algorithm which operates with high efficiency but takes up lot of memory might not be the most optimal. Analysis of the problem at hand coupled with analysis of proposed approach is essential to get the optimal computing solution.

One of the crucial skills in algorithm analysis is the ability to define time and space complexity as a function of input parameters. When you can define this dependency as a function of one or multiple arguments, you can analyze how the efficiency of the algorithm changes when you scale one of those parameters.

An important step in the process of algorithm analysis is to compare the algorithm with other algorithms in terms of performance and complexity. This is important to understand the pros and cons of the chosen algorithm.

If the algorithm has to be implemented on hardware, algorithm optimization such as pruning needs to be done. Performance vs complexity trade-off is an important factor in algorithm optimization.

In my experience, almost all problems first require the right selection of data structures. Algorithms get much simpler if you can use a combination of simpler data structures, start with the most obvious brute force approach, and reason about the time and space complexity. One other thing to note, understanding the scale is important. For example, if you have an array of 10 numbers which you will only ever sort once, a simpler algorithm which is brute force may be ok. But if you have a larger array which needs updated sorting, you can optimize more. Understanding the scope and scale is critical.

Tip - Always try to solve the problem first instead of going for the optimization in one go. This will give some confidence. Once you have solved it, try to look for what part we can optimize/ tweak in the algorithm for it to run faster.

Strategic thinking enables individuals to make better decisions by considering the long-term impact of their choices, breaking down complex problems into manageable components, prioritizing tasks for increased efficiency, and being adaptable to changing circumstances. Additionally, strategic thinkers are innovative, intelligent, open to feedback, and able to critically assess situations to make informed decisions. Strategic mindset is cultivated by identifying opportunities and risks, developing strategic plans, and continuously evaluating and adjusting strategies. By combining creativity, analysis, and vision, individuals can turn challenges into opportunities, make informed decisions, and drive business success through strategic thinking.