Memoization in Dynamic Programming Through Examples

Dynamic programming is a technique for solving problems, whose solution can be expressed recursively in terms of solutions of overlapping sub-problems. A gentle introduction to this can be found in How Does DP Work? Dynamic Programming Tutorial.

This lesson was originally published at https://algodaily.com, where I maintain a technical interview course and write think-pieces for ambitious developers.

Memoization is an optimization process. In simple terms, we store the intermediate results of the solutions of sub-problems, allowing us to speed up the computation of the overall solution. The improvement can be reduced to an exponential time solution to a polynomial time solution, with an overhead of using additional memory for storing intermediate results.

Let’s understand how dynamic programming works with memoization with a simple example.

Image for post

Fibonacci Numbers

You have probably heard of [**Fibonacci numbers**](https://algodaily.com/challenges/fibonacci-sequence/) several times in the past, especially regarding recurrence relations or writing recursive functions. Today we’ll see how this simple example gives us a true appreciation of the power of dynamic programming and memoization.

Definition of Fibonacci Numbers

The nth Fibonacci number f(n) is defined as:

f(0) = 0                                       // base case
f(1) = 1                                       // base case 
f(n) = f(n-1) + f(n-2)    for n>1              // recursive case

The sequence of Fibonacci numbers generated from the above expressions is:

0 1 1 2 3 5 8 13 21 34 ...

Pseudo-code for Fibonacci Numbers

When implementing the mathematical expression given above, we can use the following recursive pseudo-code attached.

Routine: f(n)
Output: Fibonacci number at the nth place

Base case:
1\. if n==0 return 0
2\. if n==1 return 1
Recursive case:
1\. temp1 = f(n-1)
2\. temp2 = f(n-2)
3\. return temp1+temp2

The recursion tree shown below illustrates how the routine works for computing f(5) or fibonacci(5).

If we look closely at the recursive tree, we can see that the function is computed twice for f(3), thrice for f(2) and many times for the base cases f(1) and f(0). The overall complexity of this pseudo-code is therefore exponential O(2n). We can very well see how we can achieve massive speedups by storing the intermediate results and using them when needed.

Image for post

Memoization of Fibonacci Numbers: From Exponential Time Complexity to Linear Time Complexity

To speed things up, let’s look at the structure of the problem. f(n) is computed from f(n-1) and f(n-2). As such, we only need to store the intermediate result of the function computed for the previous two numbers. The pseudo-code to achieve this is provided here.

The figure below shows how the pseudo-code is working for f(5). Notice how a very simple memoization technique that uses two extra memory slots has reduced our time complexity from exponential to linear (**O(n)**).

Image for post

Routine: fibonacciFast
Input: n
Output: Fibonacci number at the nth place
Intermediate storage: n1, n2 to store f(n-1) and f(n-2) respectively
1\. if (n==0) return 0
2\. if (n==1) return 1
3\. n1 = 1
4\. n2 = 0
5\. for 2 .. n
    a. result = n1+n2           // gives f(n)
    b.   n2 = n1                // set up f(n-2) for next number
    c.   n1 = result            // set up f(n-1) for next number
6\. return result

Maximizing Rewards While Path Finding Through a Grid

Now that we understand memoization a little better, let’s move on to our next problem. Suppose we have an m * n grid, where each cell has a “reward” associated with it. Let’s also assume that there’s a robot placed at the starting location, and that it has to find its way to a “goal cell”. While it’s doing this, it will be judged by the path it chooses. We want to get to the “goal” via a path that collects the maximum reward. The only moves allowed are “up” or “right”.

#data-structures #computer-science #software-development #dynamic-programming #algorithms #algorithms

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Memoization in Dynamic Programming Through Examples

All You Need to Know About Dynamic Programming

In this article, I will introduce the concept of dynamic programming, developed by Richard Bellman in the 1950s, a powerful algorithm design technique to solve problems by breaking them down into smaller problems, storing their solutions, and combining these to get to the solution of the original problem.

The hardest problems asked in FAANG coding interviews usually fall under this category. It is likely that you will get tasked with solving one during your interviews, hence the importance of knowing this technique. I will explain what dynamic programming is, give you a recipe to tackle dynamic programming problems, and will take you through a few examples so that you can understand better when and how to apply it.

#dynamic-programming #programming-interviews #programming

Bhakti Rane

1624531051

Alerts4Dynamics - Alerts / Notifications Management in Dynamics 365 CRM

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• Alerts can be sent as Pop-ups, Form Notifications or Email to target Dynamics 365 CRM users.
• Categorize alerts as Information, Warning or Critical.
• Track log of read/dismissed alerts by users.
• Define process start date from when the notifications will start getting created and process end date when creation of new notifications will stop. Also, add the display end date for notification.

https://www.inogic.com/product/productivity-apps/add-manage-schedule-notifications-alerts-4-dynamics-365-crm

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Bhakti Rane

1625057623

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• Undo Last Changes: Ability to restore the last changes done to a Dynamics 365 CRM record by clicking the Click2Undo button
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Memoization in Dynamic Programming Through Examples

Dynamic programming is a technique for solving problems, whose solution can be expressed recursively in terms of solutions of overlapping sub-problems. A gentle introduction to this can be found in How Does DP Work? Dynamic Programming Tutorial.

This lesson was originally published at https://algodaily.com, where I maintain a technical interview course and write think-pieces for ambitious developers.

Memoization is an optimization process. In simple terms, we store the intermediate results of the solutions of sub-problems, allowing us to speed up the computation of the overall solution. The improvement can be reduced to an exponential time solution to a polynomial time solution, with an overhead of using additional memory for storing intermediate results.

Let’s understand how dynamic programming works with memoization with a simple example.

Image for post

Fibonacci Numbers

You have probably heard of [**Fibonacci numbers**](https://algodaily.com/challenges/fibonacci-sequence/) several times in the past, especially regarding recurrence relations or writing recursive functions. Today we’ll see how this simple example gives us a true appreciation of the power of dynamic programming and memoization.

Definition of Fibonacci Numbers

The nth Fibonacci number f(n) is defined as:

f(0) = 0                                       // base case
f(1) = 1                                       // base case 
f(n) = f(n-1) + f(n-2)    for n>1              // recursive case

The sequence of Fibonacci numbers generated from the above expressions is:

0 1 1 2 3 5 8 13 21 34 ...

Pseudo-code for Fibonacci Numbers

When implementing the mathematical expression given above, we can use the following recursive pseudo-code attached.

Routine: f(n)
Output: Fibonacci number at the nth place

Base case:
1\. if n==0 return 0
2\. if n==1 return 1
Recursive case:
1\. temp1 = f(n-1)
2\. temp2 = f(n-2)
3\. return temp1+temp2

The recursion tree shown below illustrates how the routine works for computing f(5) or fibonacci(5).

If we look closely at the recursive tree, we can see that the function is computed twice for f(3), thrice for f(2) and many times for the base cases f(1) and f(0). The overall complexity of this pseudo-code is therefore exponential O(2n). We can very well see how we can achieve massive speedups by storing the intermediate results and using them when needed.

Image for post

Memoization of Fibonacci Numbers: From Exponential Time Complexity to Linear Time Complexity

To speed things up, let’s look at the structure of the problem. f(n) is computed from f(n-1) and f(n-2). As such, we only need to store the intermediate result of the function computed for the previous two numbers. The pseudo-code to achieve this is provided here.

The figure below shows how the pseudo-code is working for f(5). Notice how a very simple memoization technique that uses two extra memory slots has reduced our time complexity from exponential to linear (**O(n)**).

Image for post

Routine: fibonacciFast
Input: n
Output: Fibonacci number at the nth place
Intermediate storage: n1, n2 to store f(n-1) and f(n-2) respectively
1\. if (n==0) return 0
2\. if (n==1) return 1
3\. n1 = 1
4\. n2 = 0
5\. for 2 .. n
    a. result = n1+n2           // gives f(n)
    b.   n2 = n1                // set up f(n-2) for next number
    c.   n1 = result            // set up f(n-1) for next number
6\. return result

Maximizing Rewards While Path Finding Through a Grid

Now that we understand memoization a little better, let’s move on to our next problem. Suppose we have an m * n grid, where each cell has a “reward” associated with it. Let’s also assume that there’s a robot placed at the starting location, and that it has to find its way to a “goal cell”. While it’s doing this, it will be judged by the path it chooses. We want to get to the “goal” via a path that collects the maximum reward. The only moves allowed are “up” or “right”.

#data-structures #computer-science #software-development #dynamic-programming #algorithms #algorithms

Vern  Greenholt

Vern Greenholt

1598532420

An Introduction to Dynamic Programming

Although people make a big deal about how scary dynamic programming problems are, there’s really no need to be afraid of them. In fact, once you get the hang of them, these can actually be very easy problems.

What is dynamic programming?

_Dynamic programming is a technique to solve problems by breaking it down into a collection of sub-problems, solving each of those sub-problems just once and storing these solutions inside the cache memory in case the same problem occurs the next time. Dynamic Programming is mainly an optimization over plain _recursion . Wherever we see a recursive solution that has repeated calls for same inputs, we can optimize it using Dynamic Programming. This simple optimization reduces the time complexities from exponential to polynomial.

Briefly speaking, dynamic programming is mostly just a matter of taking a recursive algorithm and finding the overlapping subproblems (that is, the repeated calls). You then cache those results for future recursive calls. Alternatively, you can study the pattern of the recursive calls and implement something iterative. You still “cache” previous work.

There are two different ways to store our values so that they can be reused. Let’s discuss both of them:

1. Tabulation or the Bottom Up approach.

2. Memoization or the Top Down Approach.

Image for post

Tabulation vs Memoization

A note on terminology: Some people call top-down dynamic programming “memoization” and only use “dynamic programming” to refer to bottom-up work.

#dynamic-programming #computer-science #software-development #programing #big-o-notation #big data