According to a popular website- Scaler, the position of a dynamic programmer is one of the most sought-after jobs in the technology economy.
You might be wondering what Dynamic Programming is. Well, Dynamic programming boils down to breaking the optimization problem into simpler subproblems and storing the solution of each subproblem so that each subproblem is solved only once.
This makes the concept of Dynamic Programming extremely important to learn for programmers.
The Programmers and coders have to learn the concept of Dynamic Programming early on to land a job in any field related to programming.
Now, you might be wondering why is it important to practice Dynamic Programming interview questions for coding interviews.
Well, optimizing everyday tasks such as solving recursive problems is one of the many utilisations of learning Dynamic Programming. Learn more about this in the blog.
What Is Dynamic Programming?
Dynamic programming is a computer programming technique that helps efficiently solve a class of problems with overlapping subproblems and optimal substructure properties.
Such problems require repeated computation of the same subproblem values to find the optimal solution.
This is why it is vital for tech companies like Google, Amazon, Wipro, and TCS amongst others to hire candidates who are well-versed in the concept of dynamic programming.
With that said, let’s have a look at the basic aspects of Dynamic Programming and why it is important to learn this concept.
Why Is It Important to Learn Dynamic Programming for Coding Interviews?
From Simplifying real-time complications to providing solutions to complex maths problems, dynamic programming as a concept is capable of providing solutions to any and all problems.
Dynamic programming is used when you have a problem that can be split into similar subproblems so that the results can be reused.
Most often these algorithms are used for optimization. Before solving the current subproblem, the dynamic algorithm attempts to examine the results of previously solved subproblems.
Basically, Dynamic Programming is useful in many aspects that mainly pro-level programmers are aware of.
Have a look at why it is important to learn Dynamic Programming for cracking coding interviews:
Computing Large Amounts of Data
This technique is used for solving problems that contain a large number of unknowns.
- The first step of the process is to create a model which will help the user understand the problem.
- The second step is to create a table with a series of values that will be known at each step of the process.
- The third step is to create an algorithm that will help the user determine the next value in the table.
- The final step is to apply the algorithm to each value in the table, and then use the results to determine the next value in the table.
Solves Loop-Based Problems
Dynamic programming is a method of solving problems that use a series of steps to solve a problem in a loop.
This is different from the more common linear programming because in linear programming, the input to the problem is given and then the solution is found, whereas, in dynamic programming, the problem is solved continuously.
Hence, the main difference between the two concepts is that in one of them it is not possible to find a solution (linear programming) to a problem within one loop, but with dynamic programming it is.
Helps Simplify Larger Problems
Dynamic programming is a technique in which you solve a problem by breaking it down into smaller, more manageable problems.
The idea behind dynamic programming is that you can’t solve a large problem in one fell swoop, so instead you break it down into smaller problems and solve them one by one.
You can start with the easiest problem, and then work your way up to the hardest.
Optimizes Mathematical Equations
Dynamic programming is a computational method that uses a single equation to solve multiple related problems.
It is used in a variety of fields including computer science, economics, and mathematics. Dynamic programming is a mathematical technique that is often used to solve optimization problems with an iterative process and can be solved by using a recursive algorithm.
Improves Artificial Intelligence
Artificial intelligence is a broad term, but in a general sense, it is the design of computers and software to act in intelligent ways.
The term has been used to describe computers that can be intelligent in the same way that humans are. This can be achieved in a variety of ways, such as by using neural networks or machine learning.
Dynamic programming has been shown to be an effective way of optimizing artificial intelligence.
From solving complex math problems to optimizing loop-based questions, Dynamic Programming helps in simplifying the more difficult aspects of computer programming.
Does this intrigue your curiosity to solve a few dynamic programming problems yourself?
Then check out the following section of this blog!
Important Dynamic Programming Questions Asked During Coding Interviews
- Given N cubes with M faces numbered from 1 to M, find the number of ways to get the sum X. X is the sum of the values on each side of all the dice rolled.
- Find the nth Fibonacci number given a positive integer n. Return the response modulo 1000000007 because the response can be very large.
- Given an array B[ ] indicating the time required to complete G tasks, determine the minimum time required to complete the task. Note, however, that any task can be skipped by skipping two consecutive tasks.
- Given a string str , a partition of the string is a palindrome partition if all substrings of the partition are palindrome. Determines the minimum cut required for palindromic division of the given string.
The basic fundamentals of dynamic programming are extremely simple! It is mainly used for solving recursion problems using common sense.
If you are planning on applying for the position of Dynamic Programmer, you can check out study materials and guides such as the “DSA Sheet” for solving Dynamic Programming-based problems.
Other than that, there are a few other concepts such as recursive problem-solving for coding interviews and tree interview questions that are also important for cracking dynamic programming interviews.