Problem Description

The problem is about finding the total count of arithmetic subsequences in a given array nums. A subsequence is defined as a sequence that can be derived from the original array by deleting some or no elements without changing the order of the remaining elements. The question specifies arithmetic subsequences which are described by two key characteristics:

1. They must contain at least three elements.
2. The difference between consecutive elements must be the same throughout the subsequence (this is known as the common difference in arithmetic sequences).

For example, [1, 3, 5] is an arithmetic subsequence with a common difference of 2, and [1, 1, 1] is also an arithmetic subsequence with a common difference of 0. However, a sequence like [1, 2, 4] is not arithmetic since the differences between the consecutive elements are not equal (1 and 2, respectively).

The goal is to find the number of such subsequences in the given array nums.

Intuition

When solving this type of problem, it's important to recognize that a straightforward approach to generating all possible subsequences and checking whether each is arithmetic would be inefficient, likely leading to an exponential time complexity. Instead, we should strive for a dynamic programming approach that builds on the solution of smaller subproblems.

The intuition is to use dynamic programming to keep track of the count of arithmetic subsequence slices ending at each index with a given common difference. We create an array f of dictionaries, with each dictionary at index i holding counts of subsequences that end at nums[i] categorized by their common differences.

As we iterate through nums, we consider each pair of indices (i, j) such that i > j. The common difference d is calculated as nums[i] - nums[j]. Then, we update our dynamic programming array f and count as follows:

1. The number of arithmetic subsequences ending at i with a common difference d is incremented by the count of subsequences ending at j with the same difference (found in f[j][d]) plus one (for the new subsequence formed by just including nums[i] and nums[j]).
2. The overall number of arithmetic subsequences (stored in ans) is increased by f[j][d], since we can extend any subsequence ending at j with nums[i] to form a new valid subsequence.

The use of a dictionary enables us to keep track of different common differences efficiently, and by iterating over all pairs (i, j) where i > j, we ensure that we consider all valid subsequences that could be formed.

Learn more about Dynamic Programming patterns.

Solution Approach

The provided Python solution uses dynamic programming with an implementation that optimizes the process of counting arithmetic subsequences.

These are the key components of the implementation:

1. Dynamic Programming Array (f): An array of dictionaries is used to store the state of the dynamic programming algorithm. This array f has the same length as the input array nums, where each index i of f has a corresponding dictionary. In this dictionary, keys represent the common differences of arithmetic subsequences that end at nums[i], and values represent the number of such subsequences.

2. Counting Arithmetic Subsequences: The algorithm iterates through the input array nums using two nested loops to consider each pair (i, j) where i > j as potential end points for an arithmetic subsequence. The difference d = nums[i] - nums[j] is computed for each pair.

3. Updating State and Counting Slices (ans): For each pair (i, j), the algorithm does two things:

   • It updates the state in f[i][d] by adding f[j][d] + 1 to it. The extra +1 accounts for a new subsequence formed by including just nums[i] and nums[j].
   • It increases the overall count (ans) by f[j][d]. Each entry in f[j] represents a valid subsequence that ends at nums[j] and can be extended by nums[i] to form a new subsequence.

In summary, the solution involves iterating over pairs of elements to calculate possible common differences and uses dynamic programming to store the count of arithmetic subsequences up to the current index, avoiding the inefficiency of checking each possible subsequence individually. The final variable ans holds the count of all valid arithmetic subsequences of length three or more across the entire array nums.

Since this approach avoids redundant calculations by reusing previously computed results, it allows for an efficient calculation of the total number of arithmetic subsequences in the input array.

Example Walkthrough

Let's illustrate the solution approach using a small example array nums = [2, 4, 6, 8, 10].

1. Initializing Dynamic Programming Array (f): We start by creating an array of dictionaries, f, where each index will store dictionaries. Initially, all dictionaries are empty since no subsequences have been computed yet.

2. Counting Arithmetic Subsequences:

   • For i = 1 (nums[1] = 4), we look backward for j < i:

     • At j = 0 (nums[0] = 2), the common difference d = 4 - 2 = 2.
     • The count at f[1][d] is initially 0. We increment it by f[0][2] + 1 (0 + 1 since f[0][2] does not exist yet) to account for the subsequence [2, 4].
     • However, this subsequence is not yet long enough to increment our answer, as we need at least three elements for an arithmetic subsequence.

   • For i = 2 (nums[2] = 6), we again look backward for j < i:

     • For j = 1 (nums[1] = 4), d = 6 - 4 = 2. We increment f[2][2] by f[1][2] + 1 which is 1 + 1 = 2 (the [2, 4, 6] subsequence and just [4, 6]).
     • For j = 0 (nums[0] = 2), d = 6 - 2 = 4. This common difference does not align with the subsequences formed up to i = 2, so no update is made to ans.
     • Our overall count ans is increased by f[1][2] which is 1, as [2, 4, 6] is a valid arithmetic subsequence.

   • For i = 3 (nums[3] = 8), we look backward again for j < i:

     • For j = 2 (nums[2] = 6), d = 8 - 6 = 2. Increment f[3][2] by f[2][2] + 1 which becomes 2 + 1 = 3 (we can form [4, 6, 8], [2, 6, 8], and just [6, 8]).
     • For j = 1, d = 2, we again increment f[3][2] and ans by f[1][2].
     • Our count ans is updated with the sum of f values for d = 2 for j = 1 and j = 2 which gives us 3 more valid subsequences ([2, 4, 8], [4, 6, 8], [2, 6, 8]).

   • Continuing this process for i = 4 (nums[4] = 10), we find the subsequences ending with 10 and update f and ans accordingly.

3. Final Count (ans): By the end of our iteration, ans would include the count of all valid arithmetic subsequences of at least three elements. In our case, the subsequences are [2, 4, 6], [2, 4, 6, 8], [2, 4, 8], [4, 6, 8], [2, 6, 8], [2, 4, 6, 8, 10], [2, 4, 6, 10], [2, 4, 8, 10], [2, 6, 8, 10], [4, 6, 8, 10], so ans would be 7.

By reusing previously computed results for overlapping subproblems, the efficient dynamic programming solution allows us not to recalculate every potential subsequence, leading to a more optimized approach for counting the total number of arithmetic subsequences.

Solution Implementation

Time and Space Complexity

The given Python code defines a method numberOfArithmeticSlices, which calculates the number of arithmetic slices in a list of numbers. It does this by using dynamic programming with a list of default dictionaries to track the arithmetic progressions.

Time Complexity:

The time complexity of the method can be determined by analyzing the nested loops and the operations performed within those loops. The outer loop runs for n iterations, where n is the length of the list nums. The inner loop runs i times for each iteration of the ith element in the outer loop:

• The outer loop: O(n), iterating through each element of nums.
• The inner loop: Up to O(n), iterating through elements up to i, which on average would be O(n/2) for each iteration of the outer loop.
• Within the inner loop:
  • The operation d = x - y is O(1).
  • The lookup and update operations on the default dictionaries f[j][d] and f[i][d] are average-case O(1), assuming hash table operations.

Therefore, the correct time complexity is derived from the total iterations of the inner loop across all iterations of the outer loop, which is:

• O(1 + 2 + 3 + ... + (n - 1))
• This equals O(n(n - 1) / 2)
• Hence, the time complexity simplifies to O(n^2).

Space Complexity:

The space complexity can be analyzed by looking at the data structures used:

• The list f contains n defaultdicts, one for each element in nums.
• Each defaultdict can have up to i different keys, where i is the index of the outer loop, leading to O(n^2) in the worst case because each arithmetic progression can have a different common difference.

Thus, the space complexity is O(n^2) based on the storage used by the f list.

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