2552. Count Increasing Quadruplets

Problem Description

The problem asks us to find the number of increasing quadruplets in an array nums of size n. A quadruplet (i, j, k, l) is considered increasing if:

1. The indexes follow the relationship 0 <= i < j < k < l < n
2. The values at those indices follow the relationship nums[i] < nums[k] < nums[j] < nums[l]

Basically, we need to count how many combinations of four different indices (i, j, k, l) in the array satisfy the conditions where the elements are strictly increasing with respect to those indices.

Intuition

To solve this problem, the intuitive approach would be to try every possible quadruplet and check if it satisfies the condition. However, this would result in an inefficient solution with a time complexity of O(n^4) which is not practical for large arrays.

Observing the problem, we can cleverly reduce the problem's complexity by dividing it into subproblems:

1. For each pair (j, k) where j < k, count how many l (where l > k) exist such that nums[j] < nums[l]. Store this count since it will be common for all i that come before j. We'll call this array f.
2. Similarly, for each pair (j, k) where j < k, count how many i (where i < j) exist such that nums[i] < nums[k]. We need this to avoid recounting for different i with the same j and k. We'll call this array g.

Now, for each pair (j, k), the number of valid quadruplets with indices (i, j, k, l) is f[j][k] * g[j][k]. By summing these products for all pairs (j, k), we get the total count of increasing quadruplets.

This approach significantly reduces the time complexity to O(n^3) as we perform three nested loops to count elements and use additional arrays f and g to store intermediate counts and avoid recomputation.

Learn more about Dynamic Programming and Prefix Sum patterns.

Solution Approach

The solution implements a dynamic programming approach to solve the problem efficiently. Two auxiliary matrices f and g are used to store the counts of valid l indices for the second half of the quadruplet and valid i indices for the first half of the quadruplet. The solution involves three main steps:

1. Counting the number of l elements for each (j, k) pair where j < k, such that nums[j] < nums[l]. This count is stored in the f[j][k] matrix. To avoid recomputation, we iterate backward from the penultimate index down to 1 and update the count dynamically.

2. Counting the number of i elements for each (j, k) pair where j < k, such that nums[i] < nums[k]. This count is stored in the g[j][k] matrix. Similarly, we iterate through the array and update the count as we move.

3. Finally, for each (j, k) pair found in the previous steps, we multiply the values at f[j][k] and g[j][k] to get the number of valid quadruplets with indexes (i, j, k, l). We sum these products to derive the total count.

The code uses three nested loops: The outer two are for iterating over all possible (j, k) pairs, and the inner ones are for calculating the counts that get stored in f and g. It's notable that relying on previously computed values and only updating them when necessary is a hallmark of dynamic programming which reduces time complexity.

Here's a more detailed step-by-step breakdown of what each part of the code is doing:

• We initialize the matrices f and g as nxn matrices filled with 0s.
• We iterate over variable j from 1 to n-3 for the first loop.
  • We calculate the count of l values greater than nums[j] using a list comprehension and store it in cnt.
  • Then we iterate over k from j+1 to n-2 and populate the f[j][k] matrix using cnt. If nums[j] > nums[k], the count remains unchanged; else, we decrease cnt by 1 as that k is not valid.
• We iterate over variable k from 2 to n-2 for the second loop.
  • We calculate the count of i values smaller than nums[k] using a list comprehension and store in cnt.
  • Then we iterate backward over j from k-1 to 1, populating the g[j][k] matrix using cnt. If nums[j] > nums[k], the count remains unchanged; else, we decrease cnt by 1 for the same reason as above.
• We calculate the sum of products of corresponding elements in f and g to get the final count of increasing quadruplets.

By storing these intermediate results, the algorithm effectively avoids repetitive calculations and achieves a lower time complexity than the naive approach would permit.

Example Walkthrough

Let's consider a small example array nums with elements [3, 1, 4, 2, 5].

Following the solution approach to count the number of increasing quadruplets:

1. Count number of l elements for each (j, k) pair:

   • Starting from j = 1 (the second element):

     • We have (j, k) as (1, 2) i.e., elements 1 and 4. There's one element greater than 4 in the remaining list [2, 5], which is 5. So, f[1][2] = 1.
     • For (j, k) as (1, 3), there are no elements greater than 2 in our remaining list [5], so f[1][3] = 0.

   • Next with j = 2 (the third element):

     • We only have one (j, k) pair (2, 3) since k should be greater than j. There is one element greater than 2 which is 5, so f[2][3] = 1.

2. Count number of i elements for each (j, k) pair:

   • Starting from k = 2 (the third element):

     • Consider i values less than 4 when k = 2. We have one i at index 1 (element 3), so g[1][2] = 1.

   • Next with k = 3 (the fourth element):

     • The values less than 2 when k = 3 are 3 and 1 from indices 0 and 1, respectively. Thus, g[0][3] = 1 and g[1][3] = 1.

3. Calculate the total count of increasing quadruplets:

   • We only consider pairs where j < k, obtaining the products of corresponding f[j][k] and g[j][k].
   • For (j, k) being (1, 2), we multiply f[1][2] and g[1][2] which gives us 1 * 1 = 1.
   • For (j, k) being (1, 3), the multiplication is f[1][3] * g[1][3] = 0 * 1 = 0.
   • For (j, k) being (2, 3), the multiplication is f[2][3] * g[0][3] = 1 * 1 = 1.

By summing these up, we have 1 + 0 + 1 = 2 increasing quadruplets in the array.

As illustrated in this example, we utilized dynamic programming to systematically break down and solve the problem of finding all increasing quadruplets in an array, rather than naively comparing all possible combinations. Thanks to the intermediate storage of the count of l and i indices, we significantly reduce unnecessary recalculations and optimize the entire process.

Solution Implementation

Time and Space Complexity

Time Complexity

For the given countQuadruplets function, let's examine the time complexity step by step:

• The generation of the matrices f and g each require O(n^2) time since they involve creating two two-dimensional lists of size n x n.

• In the first loop (for j), the internal loop for counting (sum(nums[l] > nums[j] ...) has a complexity of O(n) for each j iteration since it potentially scans all elements after index j. The nested loop for k runs within the range of j+1 to n-1, making it also on average O(n/2) per j iteration. Therefore, the total time complexity of this block is O(n^2 * n/2) which simplifies to O(n^3).

• The second loop (for k) is similar in structure but runs the counting in reverse order. It has an inner loop for counting (sum(nums[i] < nums[k] ...)) which also has O(n) complexity for each k, and the reversed j loop again has an average complexity of O(n/2) per k. This again leads to O(n^3) for this block.

• The final sum operation iterates over the elements of the f and g matrices, which has O(n^2) complexity since it involves a nested loop that traverses most of the matrix.

Adding all of these together, the dominant term is O(n^3) since O(n^3) is much larger than O(n^2) as n grows.

Thus, the total time complexity of the countQuadruplets method is O(n^3).

Space Complexity

The space complexity is determined by the additional space needed aside from the input:

• Two matrices f and g each of size n x n are created, resulting in O(n^2) space for each. Since they exist simultaneously, this amounts to O(2 * n^2) space in total, which simplifies to O(n^2) space.

• There are no other significant space-consuming objects or recursive calls that use additional space.

Hence, the space complexity of the method countQuadruplets is O(n^2).

Learn more about how to find time and space complexity quickly using problem constraints.