Frequently asked questions (FAQs) about Memoization

1: Is memoization better than DP?

Memoization is the top-down approach to solving a problem with dynamic programming. It’s called memoization because we will create a memo for the values returned from solving each problem.

2: Is memoization the same as caching?

Memoization is actually a specific type of caching. The term caching can generally refer to any storing technique (like HTTP caching) for future use, but memoizing refers more specifically to caching function that returns the value.

3: Why memoization is top-down?

The top-Down approach breaks the large problem into multiple subproblems. if the subproblem is solved already then reuse the answer. Otherwise, Solve the subproblem and store the result in some memory.

4: Does memoization use recursion?

Memoization follows top-down approach to solving the problem. It consists of recursion and caching. In computation, recursion represents the process of calling functions repeatedly, whereas cache refers to the process of storing intermediate results.

5: Should I use tabulation or memoization?

For problems requiring all subproblems to be solved, tabulation typically outperforms memoization by a constant factor. This is because the tabulation has no overhead of recursion which reduces the time for resolving the recursion call stack from the stack memory.
Whenever a subproblem needs to be solved for the original problem to be solved, memoization is preferable since a subproblem is solved lazily, i.e. only the computations that are required are carried out.

6: Where is memoization used?

Memoization is an optimization technique used to speed up computer programs by caching the results of expensive function calls and returning them when the same inputs are encountered again.

7: Why is it called memoization?

The term “memoization” comes from the Latin word “memorandum” (“to remember”), which is commonly shortened to “memo” in American English, and which means “to transform the results of a function into something to remember.”.

8: How does memoization reduce time complexity?

Solving the same problem again and again takes time and increases the run-time complexity of the overall program. This problem can be resolved by maintaining some cache or memory where we will store the already calculated result of the problem for some particular input. So that if we don’t want to recalculate the same problem, we can simply use the result that is stored in the memory and reduce the time complexity.

9: What is the difference between memoization and caching?

Memoization is actually a specific type of caching that involves caching the return value of a function based on input. Caching is a more general term. For example, HTTP caching is caching but it is not memoization.

10: Why tabulation is faster than memoization?

Tabulation is usually faster than memoization, because it is iterative and solving subproblems requires no overhead of recursive calls.

Memoization is a technique used in computer science to speed up the execution of recursive or computationally expensive functions by caching the results of function calls and returning the cached results when the same inputs occur again.

The basic idea of memoization is to store the output of a function for a given set of inputs and return the cached result if the function is called again with the same inputs. This technique can save computation time, especially for functions that are called frequently or have a high time complexity.

Here’s a step-by-step guide to implementing memoization:

Identify the function that you want to optimize using memoization. This function should have repeated and expensive computations for the same input.

Create a dictionary object to store the cached results of the function. The keys of the dictionary should be the input parameters to the function, and the values should be the computed results.

At the beginning of the function, check if the input parameters are already present in the cache dictionary. If they are, return the cached result.

If the input parameters are not in the cache dictionary, compute the result and store it in the cache dictionary with the input parameters as the key.

Return the computed result.

Here’s a Python code example of implementing memoization using a dictionary:

In the above code, we define a function called fibonacci that calculates the nth number in the Fibonacci sequence. We use a dictionary object called cache to store the results of the function calls. If the input parameter n is already present in the cache dictionary, we return the cached result. Otherwise, we compute the result using recursive calls to the fibonacci function and store it in the cache dictionary before returning it.

Memoization can be used to optimize the performance of many functions that have repeated and expensive computations. By caching the results of function calls, we can avoid unnecessary computations and speed up the execution of the function.

The term “Memoization” comes from the Latin word “memorandum” (to remember), which is commonly shortened to “memo” in American English, and which means “to transform the results of a function into something to remember.”.

In computing, memoization is used to speed up computer programs by eliminating the repetitive computation of results, and by avoiding repeated calls to functions that process the same input.

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