A vital component of every programming language involves the incorporation of JavaScript functions. Developers often utilize functions extensively in their projects, emphasizing the importance of reusability. Should you require assistance during your coding process, feel free to reach out to us. Functions can either take a function as a parameter or return a function. In cases where your program is complex, you are likely to utilize a function repeatedly.
The need for process optimization arises as systems evolve and begin to carry out more complex calculations. This raises the demand for faster performance of the web application within the system. Failure to address this issue can lead to the program consuming a large amount of resources and experiencing delays in execution.
This tutorial delves into the concept of memoization, a technique that, if implemented effectively, can reduce computational time.
How Memorization Works?
Memoization is an optimization technique that involves storing previously computed results to avoid redundant calculations. By reusing the output of prior computations, the software can skip the repetitive task of recalculating the same values. This approach helps eliminate the need for time-consuming computations that are frequently repeated, ultimately saving significant processing time for tasks that are resource-intensive.
The concept relates to the utilization of functional programming. It is common to encounter repetitive functions in a program. Caching is employed to store the outcome of a function temporarily upon its execution. Subsequent computations requiring the function's output can directly utilize the stored result instead of recomputing it.
In this instance, memoization refers to a strategy that involves storing the results of expensive function calls to enhance the performance of software applications by providing the cached outcome when the same input is encountered again. This approach eliminates the need to recompute these values as memoization preserves and retrieves them when necessary.
Benefits of Memorization
- It is a method of improving performance by saving function call outcomes. It keeps track of previous results and retrieves them as needed for the duration of your program. This shortens execution times while increasing CPU performance.
- A memoized function is what a pure function ought to be. This means that the way the function is executed stays the same. No matter how many times the function is run, it should always return the same value when given a specific argument.
- Why not commit the result of a function that runs more than once, twice, or even thrice to memory? This function only must be used once in this manner. Your software performs better as a result.
- When to Use Memoization in JavaScript A pure function is one that always provides the same value when it is called. A function that is impure returns distinct outcomes each time it is invoked. If such data are cached, return values can come as a surprise.
- Caching the results of a costly computation makes software run faster when it executes it. Memorization allows the function to deliver the same results every time without requiring the user to recalculate its values.
- Memorization in Javascript is used to prevent multiple requests to the server. It prevents requests while making recurring API calls when they are made from a distance.
- You do not have to make another call because you already know how the previous one ended to get similar results.
- Recursive functions use recurrent input values to repeat themselves.
Memoization Process
Consider a real-life scenario where you are looking through a book with an appealing, stylish, and engaging cover. Suddenly, a person approaches you and inquires about the author and title of the book. Simply flip the page to reveal the author's name and title, and then respond to the individual.
The book has captured the interest of many readers due to its thrilling content. When approached by someone with questions about the book, you may find yourself unable to recall the author's name or title. In such instances, you may need to refer back to the book to refresh your memory and provide accurate details. This is a common occurrence when trying to recollect specific information.
Memoization in JavaScript relies on two key elements:
- Closures
- Higher-order functions
Clouser
Understanding lexical scoping is a necessary step before delving into the concept of memoization with closures in JavaScript. Lexical scoping refers to how variables are scoped based on their location in the source code.
A closure function is formed by a nested function along with its access to the surrounding state, enabling the inner function to reach and utilize the outer function's code.
A closure is created in JavaScript when a function is defined. It retains access to the outer function's variables within the inner function's scope.
Example of the clouser
Below is a demonstration of a closure function in JavaScript, showcasing both outer and inner functions. The functions solutionouter and solutioninner are employed with memoization stored in the js variable. The inner function utilizes a local variable and references a variable present in the source code.
<!DOCTYPE html>
<html>
<head>
<title> Memoization in Javascript </title>
</head>
<body style="background-color:beige;">
<h2> Memoization in Javascript </h2>
<h4> We can learn memoization in javascript with clouser factor </h4>
<script>
function solution_outer() {
let name = 'Example';
function solution_inner() {
alert(`${name}! website to learn coding`);
}
return solution_inner;
}
let memoizationinjs = solution_outer();
memoizationinjs();
</script>
</body>
</html>
Output
The result displays the alert message from the closure function.
High Order Functions
High-order functions in JavaScript refer to functions that either take another function as an argument or return a function as a result. To grasp the concept of high-order functions in memoization, it is beneficial to explore the Fibonacci sequence along with its corresponding code implementation.
Example of high high-order function
Below is an illustration demonstrating memoization of a higher-order function in JavaScript. You have the option of employing the squared formula within the function provided a specific input argument.
<!DOCTYPE html>
<html>
<head>
<title> Memoization in Javascript </title>
</head>
<body style="background-color:beige;">
<h2> Memoization in Javascript </h2>
<h4> We can learn memoization in JavaScript with high order function </h4>
<script>
const mysquare_memo = number =>{
let result = 0;
for (let i = 1; i <= number; i++) {
for (let j = 1; j <= number; j++) {
result ++;
}
}
return result;
}
console.log("Memoization in Javascript");
console.log(mysquare_memo(3));
console.log(mysquare_memo(10));
console.log(mysquare_memo(15));
console.log(mysquare_memo(18));
console.log(mysquare_memo(21));
</script>
</body>
</html>
Output
The result displays the console log output of the memoization function.
Examples of Memoization in JavaScript
Numerous instances of memoization can be observed across various functions and variables.
Example 1:
Below is an illustration demonstrating the implementation of a memoization function with error handling.
<!DOCTYPE html>
<html>
<head>
<title> Memoization in Javascript </title>
</head>
<body style="background-color:beige;">
<h2> Memoization in Javascript </h2>
<h4> We can learn memoization in javascript with Catch </h4>
<script>
// a function that accepts a function and returns another
const memoize_catch = (func) => {
// a collection of results
const results = {};
// return a function for the results cache
return (...args) => {
//a JSON key is used to preserve the cached value as a results
const argsKey_catch = JSON.stringify(args);
//If mysquare_catch function has no cached value (), then 'func' can be used
if (!results[argsKey_catch]) {
// square value function return value should be saved ()
results[argsKey_catch] = func(...args);
}
// retrieve the results variable from the cache and argument
return results[argsKey_catch];
};
};
//mysquare_catch() function must be wrapped with memoize_catch ()function
const mysquare_catch = memoize_catch(num_catch => {
let result_catch = 0;
for (let i = 1; i <= num_catch; i++) {
for (let j = 1; j <= num_catch; j++) {
result_catch++;
}
}
return result_catch;
});
console.log(mysquare_catch(11));
console.log(mysquare_catch(89));
console.log(mysquare_catch(500));
console.log(mysquare_catch(646));
console.log(mysquare_catch(766));
</script>
</body>
</html>
Output
The result displays the console log output of the memoization function.
Example 2:
Below is an illustration demonstrating the memoization function along with performance evaluation. The standard execution path of the function is employed to assess the efficiency of the memoization function implemented in JavaScript.
<!DOCTYPE html>
<html>
<head>
<title> Memoization in Javascript </title>
</head>
<body style="background-color:beige;">
<h2> Memoization in Javascript </h2>
<h4> We can learn memoization in javascript with performance testing </h4>
<script>
const mysquare_catc = (num_catc) => {
let result = 0;
for (let i = 1; i <= num_catc; i++) {
for (let j = 1; j <= num_catc; j++) {
result++;
}
}
return result;
};
console.log("We can learn memoization in javascript with performance testing");
console.time("First Call");
console.log(mysquare_catc(467));
console.timeEnd("First call");
// two times the same value
console.time("Second Call");
console.log(mysquare_catc(467));
console.timeEnd("SecondCall");
Console.time("Third Call");
console.log(mysquare_catc(467));
console.timeEnd("ThirdCall");
</script>
</body>
</html>
Output
The result displays the console log output of the memoization function.
Example 3:
Below is a demonstration of the memoization process utilizing the memorized function, which plays a crucial role in performance evaluations.
<!DOCTYPE html>
<html>
<head>
<title> Memoization in Javascript </title>
</head>
<body style="background-color:beige;">
<h2> Memoization in Javascript </h2>
<h4> We can learn memoization in javascript with memoized function </h4>
<script>
const memoize_fun = (func) => {
const results = {};
return (...args) => {
const argsKey = JSON.stringify(args);
if (!results[argsKey]) {
results[argsKey] = func(...args);
}
return results[argsKey];
};
};
const mysquare_fun = memoize_fun((num_fun) => {
let result_fun = 0;
for (let i = 1; i <= num_fun; i++) {
for (let j = 1; j <= num_fun; j++) {
result_fun++;
}
}
return result_fun;
});
console.time("First Call");
console.log(mysquare_fun(767));
console.timeEnd("First Call");
// two times the same value
console.time("Second Call");
console.log(mysquare_fun(747));
console.timeEnd("Second Call");
Console.time("Third Call");
console.log(mysquare_fun(746));
console.timeEnd("Third Call");
console.log("We can learn memoization in javascript with memoized function");
</script>
</body>
</html>
Output
The result displays the console log output of the memoization function.
Recursive Functions for Memoization
Recursion is a concept in programming where a function calls itself multiple times. To prevent infinite looping, a recursive function includes a termination condition. Recursion utilizes the idea of iteration. An iteration occurs when a value repeats continuously until a specific condition is satisfied.
Recursion is elegantly demonstrated through the Fibonacci sequence, where each Fibonacci term is calculated by summing up the two numbers that precede it in the sequence.
Example of the basic Fibonacci series
Example 1:
Displayed below are instances illustrating the implementation of a recursive function with memoization in JavaScript. An ideal scenario for applying this technique is by using the Fibonacci series as the basis for the function's recursion.
<!DOCTYPE html>
<html>
<head>
<title> Memoization in Javascript </title>
</head>
<body style="background-color:beige;">
<h2> Memoization in Javascript </h2>
<h4> We can learn memoization in javascript with higher nth Fibonacci term </h4>
<script>
const fibonacci_memo = (n) => {
// Return the first term of the 1 value if n equals 1.
if (n == 1) {
return 1;
}
// Return the second term of the 1 value if n equals 2.
else if (n == 2) {
return 1;
}
// If n is more than two values, return the total of the two values before it.
else
return fibonacci_memo(n - 1) + fibonacci_memo(n - 2);
};
//The nth Fibonacci series values are printed
console.log(fibonacci_memo(6));
console.log("We can learn memoization in javascript with higher nth Fibonacci term");
</script>
</body>
</html>
Output
The displayed result presents the console log output of a function that generates the Fibonacci series.
Example 2:
Below is an illustration demonstrating a memoization function that incorporates recursion. We can employ the Fibonacci sequence function alongside a memorized variable within a JavaScript function.
<!DOCTYPE html>
<html>
<head>
<title> Memoization in Javascript </title>
</head>
<body style="background-color:beige;">
<h2> Memoization in Javascript </h2>
<h4> We can learn memoization in javascript with memoized function Fibonacci term </h4>
<script>
const memoize_memo = (func) => {
const results = {};
return (...args) => {
const argsKey = JSON.stringify(args);
if (!results[argsKey]) {
results[argsKey] = func(...args);
}
return results[argsKey];
};
};
const fibonacci_memo = memoize_memo((n) => {
// Return the first term 1 values if n equals 1 value.
if (n == 1) {
return 1;
}
//Return the second term 1 value if n equals 2 values.
else if (n == 2) {
return 1;
}
// If the n value is more than two, then return the total of the two words before it.
else
return fibonacci_memo(n - 1) + fibonacci_memo(n - 2);
});
//the sixtieth phrase in the series should be printed
console.log(fibonacci_memo(8));
console.log("We can learn memoization in javascript with higher nth Fibonacci term");
</script>
</body>
</html>
Output
The result displays the console log output of the memoization function.
Conclusion
Discussing the concept of memoization in JavaScript is crucial. The programming principle of memoization can be advantageous in any programming language. Enhancing the efficiency of your software is its primary objective, which becomes particularly evident when dealing with complex computations.