## Identify The Problem As A Dynamic Programming Problem

The first thing youll need to do when given a dynamic programming problem in a coding interview is… to realize that youve been given a dynamic programming problem, obviously!

Side note: I realize the phrase dynamic programming problem is a bit of a misnomer. Dynamic programming is not a *type of problem*, it is a *technique* which can be used to solve a problem. In interviews, you will be given problems where dynamic programming may only be one of a number of possible techniques to solve the problem. However, I will continue using this phrase for brevity.

As mentioned earlier, dynamic programming can be used on any problem with a recursive substructure and overlapping subproblems within that substructure. However, this can be a little tricky to recognize immediately. But, dont worry! There are some clues that you can look for that may tip you off to a problem being a dynamic programming problem.

Dynamic programming problems are often optimization problems. This means that the problem is asking you for an optimal answer to the set of inputs. What is the longest increasing subsequence in this array? What is the minimal amount of coins to make a certain amount of change? What is the longest palindromic substring of a given string? Keep on the lookout for problems that are asking you to find things like the longest, shortest, maximum, or minimum of something.

## What Are Some Characteristics Of Dynamic Programming

The problem can be divided into stages with *optimal policies* for each stage.

The variable states in each stage of the process examine how future actions will be influenced by present decisions.

In dynamic programming, you develop a *recursive optimization*procedure to build a solution to the N-stage problem.

We use the dynamic programming approach when there are problems that can be broken down into sub-problems.

**Thus in dynamic programming, the results can be reused.**

And by learning common algorithms, youll be able to navigate programming problems and solutions using dynamic programming for coding interviews.

*Now lets dig into this course.*

*Want to know more about the Grokking series on Educative?*

In this pattern youll work on this and other special cases of knapsacks such as:

**Equal Subset Sum Partition**

**Subset Sum**

**Example challenge of subset sum:** Given a set of positive numbers, determine if a subset exists whose sum is equal to a given number S.

**Minimum Subset Sum Difference**

**Count of Subset Sum**

**Target Sum**

**Example challenge of a target sum: **Given a set of positive numbers and a target sum S. Each number should be assigned either a + or – sign. Then find out total ways to assign symbols to make the sum of numbers equal to target S.

## What Is One Advantage And One Disadvantage Of Using A Bottom

Employers may ask this question to ensure that you not only know when to use a bottom-up approach versus a top-down approach but also to verify that you understand the pros and cons of using a tabulation approach. This question is can be an opportunity to highlight your understanding of tabulation by discussing the features of this method and reviewing hypothetical situations when you may benefit from its use.

Example:”A bottom-up or tabulation approach can be particularly useful when addressing complex problems. One benefit of this method is the ability to do optimizations in a way in which memoization may not allow. This is essential for situations that require optimization. Alternatively, one disadvantage of this approach is that it requires the programmer to come up with an ordering system. This means that the professional selects the exact order that they plan to execute their computations ahead of time, which can complicate the dynamic programming process.”

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## Can You Define Dynamic Programming

If you’re interviewing for a role that involves dynamic programming, it’s essential for you to be able to define it and use it when contributing to a project. There are two fundamental approaches to apply to this method, top-down and bottom-up, so ensure that you include both strategies in your definition. The primary objective of dynamic programming is to organize your computations to avoid recalculating the same subproblems, so consider discussing the purpose of this process when answering this question.

Example:”Dynamic programming is a technique that allows you to avoid computing the same subproblems within a recursive algorithm, and you can implement dynamic programming algorithms with recursion, although the programs don’t require this. Dynamic programming prevents computation repetition by allowing you to store results in a general table and access this information when trying to answer a problem. This can help you verify that you don’t already have the answer to the issue within your existing data. You can also use this data to help figure out a solution to the problem if one doesn’t already exist.

Related:FAQ: What Is An Array?

## Determine The Base Case

Now that we have our recurrence relation, we need to figure out our base case. All recursive functions must have at least one base case, otherwise we will get stuck in an infinite recursion. How do we identify base cases? This is also something that comes with practice, but there are some different ideas that we can consider when confronted with this task.

One type of base case is to stop recursing for an input that exists at the edge of the possible range of inputs. For example, if we have a parameter that can be any integer 0, we might have a base case for when that integer is 0. This is what we did in our Fibonacci example, except we also had a base case for n = 1, since we call fib within the function and fib is outside the range of possible values for n.

Another type of base case is to stop recursing when we hit an invalid input. A good example of this is depth first search on a binary tree:

functiondfs

Here, we just return false if we encounter a null node, since theres no way for a subtree that doesnt exist to contain the value were looking for.

For our house robber problem, we already discussed a couple possible base cases when we were trying to figure out the recurrence relation:

- What do we do if we have one house? Obviously we rob that one!
- What do we do if we have two houses? Obviously we pick the one with more money!

f=hf=maxf=max,f)

f=0f=max,f)=max,f)=max=hf=max,f)=max,f)=max=max

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## Grokking Dynamic Programming Patterns For Coding Interviews: Full Course Review

Disclaimer: THIS COURSE IS NOT FOR CODE NEWBIES.

When youre preparing for that coding interview, you need all the help you can get.

*Especially when it comes to dynamic programming patterns.*

Weve found a dynamic programming course And it **contains some of the most common dynamic programming problems and solutions. **

But first, lets go what dynamic programming is

*This post contains affiliate links. I may receive compensation if you buy something. Read my disclosure for more details.*

## Common Dynamic Programming Interview Questions

Dynamic programming may be the bane of most software engineers’ existence. I don’t think there’s any topic that I’ve received more questions about.

And I totally get it.

Interviewers love to ask these questions because they’re hard.

Interviewees really struggle because they don’t have a problem solving framework for approaching DP problems.

That means that every time you try to solve a dynamic programming problem, you are starting from square one. You can’t apply patterns you seen with other DP problems because they look totally different. So you’re always starting over and trying to solve these difficult problems from scratch.

In this post, I want to show you a better way.

The following videos will walk you through 6 of the most common DP problems that you can expect to see in your interviews. If you learn these problems and learn how to apply the FAST Method, you will be in very good shape to tackle dynamic programming in your interviews.

*Protip: If youre still new to dynamic programming, check out our free 42 page ebook, Dynamic Programming for Interviews,* *first*

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## Module 3 Common Dynamic Programming Problems

- Use active studying techniques to cut your study time in half while making the concepts stick so you never worry about blanking when youre in the interview.
- Apply the FAST Method and see different dynamic programming patterns with 5 common practice problems, so youre never caught off guard in your interview.
- Experiment with the problem solutions yourself using the Github repo and runnable Java code for every problem in the course and never forget how to solve the problems interview.

## S To Solve A Coding Problem Using Recursion

Once you have identified that a coding problem can be solved using Recursion, You are just two steps away from writing a recursive function.

1. Find the base case2. Finding how to call the method and what to do with the return value.

As discussed above, finding a base case for any recursive solution is the first step toward writing a recursive function in Java or any other programming language. This is usually the simplest way to solve the problem without using recursion.

For example, when you calculate factorial, the base case is factorial which is 1, you mean you know the answer so you can directly return it and from there onwards recursion will unroll and calculate factorial for the given number.

Once you have done that, you need to find a way to call the recursive method and what to do with the result returned by the method, sometime you may need to add, multiply, and divide those depending upon your problem. This will be more clear when you will solve Recursive Practice Questions in Java.

And, if you want to learn Recursion from scratch then **Recursion for Coding Interviews in Java** course on Educative is a great resource to start with, I really loved it as it also forms the basis for Dynamic Programming which they have explained in their **Grokking Dynamic Programming Patterns** for Coding Interview course.

**Also Check: Why Product Manager Interview Question **

## Faqs On How To Solve Dynamic Programming Interview Questions

**Q1 Can Dynamic Programming solve all problems?**

No, DP canât solve all the problems. The DP approach is applicable if the problem has the following two attributes: optimal substructure and overlapping sub-problems.

**Q2 What are the two key attributes that a problem must have for dynamic programming to be applicable?**

The two key attributes a problem must have for DP to be applicable are optimal substructure and overlapping sub-problems. When a solution to the problem can be found by combining optimal solutions to non-overlapping sub-problems, we call it the divide and conquer strategy instead.

**Q3 What are the drawbacks of dynamic programming over recursion?**

Some of the drawbacks of dynamic programming over recursion are: a significant amount of memory is needed to store the calculated result of every subproblem. Thereâs no guarantee whether all the stored values will be used or not. Often the result that gets stored is never utilized in the subsequent subproblems.

**Q4. Why is dynamic programming important?**

DP as a technique helps us solve difficult problems efficiently. Thatâs the reason why itâs so popular in academia, industry, and software engineering interviews in top roles.

**Q5. How is dynamic programming different from recursion?**

In recursion, a method calls itself again, while problems with an optimal substructure that can be broken down into similar subproblems are solved in dynamic programming.

## How To Nail Your Next Tech Interview

Learning to answer dynamic programming interview questions is essential if you want to be a serious contender for the best software engineering jobs available. DP is a technique that helps solve complex problems by breaking them down into simpler subproblems, solving them once, and storing their solutions. Dynamic Programming can thus be seen as a more efficient recursive algorithm in which the same subproblem is not solved twice. This article explains how to approach Dynamic Programming questions and provides sample Dynamic Programming interview questions.

If you are preparing for a tech interview, check out our technical interview checklist, interview questions page, and salary negotiation ebook to **get interview-ready!** Also, read , , and for specific insights and guidance on **Coding interview preparation**.

Having trained **over 9,000 software engineers**, we know what it takes to crack the most challenging tech interviews. Since 2014, **Interview Kickstart** alums have landed lucrative offers from FAANG and Tier-1 tech companies, with an **average salary hike of 49%.** The highest ever offer received by an IK alum is a whopping **$933,000****!**

At IK, you get the unique opportunity to learn from expert instructors who are **hiring managers and tech leads** at Google, Facebook, Apple, and other top Silicon Valley tech companies.

*Want to nail your next tech interview?** Sign up for our **FREE Webinar***.**

In this article, youâll learn:

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## These Are The Best Courses To Learn Dynamic Programming For Interviews From Udemy Educative And Coursera For Coding Interviews In 2022

Hello guys, if you want to learn **Dynamic Programming**, a useful technique to solve complex coding problems, and looking for the best Dynamic Programming courses then you have come to the right place. Earlier, I have shared the **best data structure and algorithm courses** and some coding problems for interviews, and today I am going to share the best online courses to learn Dynamic Programming.

If you are looking for a job and giving interviews then you might have noticed that getting a Software development Job is becoming more and more difficult every day.

For example, there was a time when you can get a Java developer position by just knowing Core Java concepts like String, Collections, Multithreading, etc, but, not anymore.

## Module 2 How To Solve Any Dynamic Programming Problem Using The Fast Method

In Module 2, well explore

- Identify any dynamic programming problem so you know exactly when to use dynamic programming in the first place.
- Learn the FAST Method and solve any dynamic programming problem with ease, even if youve never seen the problem before.
- Quickly understand how to apply the FAST method. No learning theoretical concepts that you cant directly apply to your interview.

**Also Check: How To Conduct Virtual Interviews **

## Dynamic Programming Predictable And Preparable

One of the reasons why I personally believe that DP questions might not be the best way to test engineering ability is that theyre predictable and easy to pattern match. They allow us to filter much more for preparedness as opposed to engineering ability.

These questions typically seem pretty complex on the outside, and might give you an impression that a person who solves them is very good at algorithms. Similarly, people who may not be able to get over some mind-twisting concepts of DP might seem pretty weak in their knowledge of algorithms.

The reality is different, and the biggest factor in their performance is preparedness. So lets make sure everyone is prepared for it. Once and for all.

## How Do Dynamic Programming Algorithms Differ From The Divide

Since dynamic programming is an extension of the divide-and-conquer paradigm, interviewers may ask this question to ensure that you understand the relationship between these two concepts. While subproblems are independent of each other in a divide-and-conquer system, subproblems aren’t independent in a dynamic programming algorithm, so consider highlighting this fundamental difference when answering this question. You may also highlight how a dynamic programming algorithm extends the divide-and-conquer paradigm by using either the top-down or bottom-up approach.

Example:”While both of these systems function recursively to break down a problem into multiple, easier-to-answer subproblems, dynamic programming algorithms are an extension of the divide-and-conquer paradigm. When using either technique, the answers and data collected from the subproblems contribute to addressing the overall issue.The primary difference between them relates to how subproblems relate to each other. In a dynamic programming algorithm, the subproblems are dependent, whereas in a divide-and-conquer system, the subproblems are independent. Dynamic programming extends the divide and conquers paradigm by using one of its two primary approaches, top-down or bottom-up.”

Related:The Guide To Passing a Programmer Interview

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## Master The Art Of Dynamic Programming

If you like Udemy courses like me, this is another good course to learn the Dynamic Programming technique. It covers problems like Edit Distance, Regular Expression Matching, Minimum deletion to make a String palindrome, and Longest increasing subsequence.The course will also teach you Recursion and Backtracking, two important techniques for solving coding problems. The best thing is that he explains the solution in depth. Examples from the **CLRS book** are also covered in this course, which one can refer to know more about concepts.

Most importantly, the way Ajay explains how to approach a Dynamic Programming problem from identification to formulation is great.

He also divides the problems into two categories one-dimensional Dynamic Programming problems and Two-dimensional dynamic programming problems.

## S To Solve A Dynamic Programming Problem

In the rest of this post, I will go over a recipe that you can follow to figure out if a problem is a DP problem, as well as to figure out a solution to such a problem. Specifically, I will go through the following steps:

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## Ready To Nail Your Next Coding Interview

Whether youâre a coding engineer gunning for a software developer or software engineer role, a tech lead, or youâre targeting management positions at top companies, IK offers courses specifically designed for your needs to help you with your technical interview preparation!

If youâre looking for guidance and help with getting started, As pioneers in the field of technical interview preparation, we have trained thousands of software engineers to crack the toughest coding interviews and land jobs at their dream companies, such as Google, Facebook, Apple, Netflix, Amazon, and more!