Precomputation
Designing a solution that leverages precomputation depends on several factors, and recognizing when to apply this technique can greatly enhance efficiency. Here’s how you can make the design decision that precomputation will be useful in solving a given problem:
1. Identify Repetitive Computations
Analyze the problem to find computations that are performed multiple times with the same inputs. If a function is repeatedly called with the same parameters, precomputation can be beneficial.
2. Evaluate Time and Space Complexity
Consider the time and space complexity of the repeated calculations. If the computations are timeconsuming, precomputing and caching the results may speed up the solution.
3. Assess Memory Constraints
Evaluate the memory available for caching precomputed values. If memory is not a major constraint and the range of possible inputs is manageable, precomputation can be a good choice.
4. Balance Time and Space Tradeoffs
Analyze the tradeoffs between the time saved by precomputation and the additional space required to store the precomputed values. If the time saved outweighs the space used, precomputation is likely a good approach.
5. Consider the Range of Inputs
If the problem involves computations within a specific range or set of values, precomputation (like range caching) can be strategically applied to that subset.
6. Determine the Frequency of Changes
If the underlying data changes infrequently, precomputation is more beneficial, as the cached values remain valid for longer. If the data changes often, constant recomputation may diminish the benefits of precomputation.
7. Analyze Algorithmic Patterns
Look for algorithmic patterns like dynamic programming, where precomputation naturally fits in. If the problem can be broken down into overlapping subproblems, precomputing those subproblems might lead to an efficient solution.
8. Examine RealWorld Scenarios
Consider realworld scenarios and how the system will be used. If there are clear patterns in user behavior or data access that suggest certain computations will be requested frequently, precomputation could be a sensible strategy.
Key Takeaways
 Precomputation is useful when repetitive, timeconsuming computations occur, and when the memory required to store the results is acceptable.
 Analyzing the problem in terms of complexity, memory constraints, input ranges, frequency of changes, and realworld usage can guide the design decision to utilize precomputation.
 Careful consideration of timespace tradeoffs and the specific context of the problem helps in deciding whether precomputation will be beneficial.
A very simple and illustrative example of precomputation is calculating Fibonacci numbers. Calculating Fibonacci numbers using recursion can be quite inefficient for larger numbers, as many calculations are repeated. By using precomputation, we can greatly speed up this process.
Here’s a simple example of how you might use precomputation to calculate Fibonacci numbers:
Python


In this code, the precomputed
dictionary stores the results of previous calculations. When the function is called with a value of n
that’s already in the dictionary, it returns the stored result instead of recalculating. This avoids the redundant calculations that would be performed by a naive recursive approach, leading to a significant increase in efficiency.
Key Takeaway
 Precomputation allows us to store results of computations so that we don’t have to recalculate them in the future.
 In this simple example, precomputation turns what would be an exponentialtime algorithm into a lineartime algorithm, showcasing the potential efficiency gains.
A 2D data structure like a matrix can also benefit from precomputation. A common example is the construction of a prefix sum matrix to allow efficient querying of the sum of elements in a submatrix.
Problem
Given a 2D matrix, we want to answer queries that ask for the sum of elements in a rectangular submatrix defined by its topleft and bottomright coordinates.
Precomputation Solution
We can precompute a prefix sum matrix, where each element at position (i, j)
stores the sum of all elements in the rectangle defined by (0, 0)
to (i, j)
in the original matrix. With this precomputed matrix, we can answer sum queries in constant time.
Python Code


Explanation
 The
precompute_prefix_sum
function builds the prefix sum matrix, allowing constanttime queries of rectangular sums.  The
query_sum
function retrieves the sum of a specific submatrix using the precomputed prefix sum matrix.
Key Takeaway
Precomputation of a prefix sum matrix enables efficient querying of rectangular sums within a 2D matrix. It turns what would be an O(rows * cols) operation for each query into an O(1) operation, providing a significant efficiency gain for scenarios where multiple queries are required.
Dynamic Programming and Precomputation
Dynamic Programming (DP) and precomputation are two computational strategies that often go hand in hand but serve different purposes. Here’s the relationship between the two:
1. Overlapping Subproblems
 Dynamic Programming: DP is used to solve problems with overlapping subproblems. By breaking down a complex problem into smaller subproblems, and solving each only once, DP avoids redundant computations.
 Precomputation: Precomputation is a specific strategy to avoid repeated calculations by storing the results of computations that will be needed again later.
 Relationship: Precomputation can be considered a mechanism within DP to store the solutions of subproblems. By caching these solutions, DP ensures that each subproblem is solved only once.
2. Memoization
 Dynamic Programming: Memoization is a common technique in DP where the solutions to subproblems are stored in a table (or other data structure) to avoid redundant calculations.
 Precomputation: Precomputation involves calculating and storing values ahead of time that will be needed later. This is essentially the same concept as memoization.
 Relationship: Memoization is a form of precomputation used in DP. When a subproblem is solved, its solution is cached (precomputed) for later use.
3. BottomUp vs TopDown Approaches
 Dynamic Programming: DP can be implemented using a bottomup (tabulation) approach, where solutions to all subproblems are systematically solved and stored in a table, or a topdown (memoization) approach, where solutions are computed and stored as needed.
 Precomputation: Precomputation aligns more closely with the topdown approach, where specific values are calculated and stored based on what’s required.
 Relationship: The topdown approach to DP, using memoization, directly employs precomputation. The bottomup approach can also be seen as a form of precomputation, where all possible values are calculated in advance.
4. Space and Time Tradeoffs
 Dynamic Programming: DP aims to reduce time complexity by using extra space to store subproblem solutions.
 Precomputation: Precomputation also involves a tradeoff between time and space, as it saves computation time at the expense of using extra memory to store precomputed values.
 Relationship: Both DP and precomputation leverage the tradeoff between time and space to increase efficiency. They save time by using extra memory to avoid redundant calculations.
Key Takeaways
 Dynamic Programming is a method for solving complex problems by breaking them down into simpler overlapping subproblems.
 Precomputation is a technique used to store results that will be needed again later to avoid redundant calculations.
 Precomputation is an inherent part of DP, specifically in the form of memoization, and contributes to DP’s efficiency in solving problems with overlapping subproblems.
Can Precomputation be used without using Dynamic Programming?
Yes, precomputation can be used without using Dynamic Programming (DP). Precomputation is a general strategy that involves calculating and storing results ahead of time, and it can be applied in many contexts beyond DP.
Examples and Use Cases
Lookup Tables: You can precompute values for a specific function and store them in a lookup table. This way, when the function is called with a particular input, you can simply retrieve the precomputed result from the table rather than recalculating it. This is often used in mathematical functions, such as trigonometric functions.
Graph Algorithms: In some graph algorithms, precomputation can be used to calculate and store specific values that are frequently accessed, such as shortest paths between specific nodes.
Image Processing: In computer graphics or image processing, precomputed values like prerendered textures or lighting calculations can be stored to accelerate rendering.
Prefix Sum Arrays: Precomputation can be used to calculate prefix sums or other aggregate values over a data structure, allowing for efficient queries later. This doesn’t necessarily involve overlapping subproblems, so it doesn’t require DP.
Caching Database Queries: In database systems, frequently accessed query results can be precomputed and cached, improving the performance of subsequent identical queries.
Key Takeaway
Precomputation is a broader concept that simply involves calculating and storing results that will be needed later. While it is often used in conjunction with Dynamic Programming to avoid redundant calculations in problems with overlapping subproblems, precomputation can be applied in many other contexts to improve efficiency. It’s a versatile tool that serves to reduce computational time at the expense of using additional memory.