Memoization is a technique used in computer science and programming to optimize the performance of functions by caching the results of expensive function calls and returning the cached result when the same inputs occur again.
The basic idea behind memoization is to store the results of expensive function calls and return the cached result when the function is called with the same set of parameters or inputs again. This helps in avoiding unnecessary recomputation of the same result, thereby improving the overall performance of the program, especially for functions that are computationally expensive or involve repetitive calculations.
Here's a simplified example in JavaScript:
// Function to calculate the factorial of a number (basic example)
function calculateFactorial(n) {
if (n === 0 || n === 1) {
return 1;
} else {
return n * calculateFactorial(n - 1);
}
}
// Applying memoization to the factorial function
const memoizedFactorial = (function () {
const cache = {}; // Cache to store computed results
return function memoized(n) {
if (n in cache) {
return cache[n]; // If result exists in cache, return it
} else {
// If result doesn't exist, compute and store it in the cache
const result = calculateFactorial(n);
cache[n] = result;
return result;
}
};
})();
// Using the memoized factorial function
console.log(memoizedFactorial(5)); // Computes factorial of 5
console.log(memoizedFactorial(4)); // Returns result from cache (factorial of 4)
In this example:
-
calculateFactorial()
is a basic factorial function. -
memoizedFactorial()
is a memoized version of the factorial function. -
The
cache
object stores previously computed results based on input values. -
When
memoizedFactorial()
is called with an input, it first checks if the result exists in the cache. If it does, it returns the cached result. If not, it computes the result, stores it in the cache, and returns it.
Memoization can significantly improve the performance of recursive or computationally intensive functions by avoiding redundant calculations and storing previously computed results for reuse when the function is called with the same inputs.