Problem Description

In the LeetCode problem presented, we are given a set of equations that express relationships between variables. Each variable is represented by a single lowercase letter, and each equation is a string of 4 characters. The equations come in two forms:

1. "xi==yi" which indicates that the two variables represented by xi and yi must be equal.
2. "xi!=yi" which specifies that the two variables must not be equal, where xi and yi are any lowercase letters.

We need to determine if there is a way to assign integers to each variable such that all the equality and inequality equations are satisfied simultaneously. If there is a way to do so, we should return true, else return false.

Intuition

To solve this problem, we can use the union-find algorithm, which is a data structure that is very efficient at grouping elements into disjoint sets and determining whether two elements are in the same set.

Here is the intuition broken into steps:

1. Initialization: We first initialize 26 elements, one for each lowercase letter, to represent each variable. Each element is its own parent in the union-find data structure in the beginning.

2. Processing Equalities: We iterate through all the equalities - equations that say xi==yi. For each equality, we find the parent representations of the variables xi and yi. If they are different, then we merge the sets by assigning one parent to both, effectively stating that they are equal.

3. Processing Inequalities: After dealing with all the equalities, we iterate through the inequalities - equations that say xi!=yi. For each inequality, again, we find the parent representations of xi and yi. If they have the same parent, this means we have previously determined they are equal, which contradicts the current inequality equation. Therefore, we can return false.

4. Conclusion: After checking all inequalities, if none have caused a contradiction, it means that all equations can be satisfied, so we return true.

Using union-find allows us to efficiently merge groups and keep track of the connected components or disjoint sets. The two-pass approach first ensures all equalities are accounted for which forms the base state for dealing with inequalities.

Learn more about Union Find and Graph patterns.

Solution Approach

The solution leverages the union-find algorithm, also known as the disjoint set union (DSU) algorithm. This is well suited for problems that deal with connectivity and component relationships, like the one we have at hand.

Here's a step-by-step breakdown of how the union-find algorithm is applied in this solution:

1. Find Function: A find function is defined to determine the parent or the representative of a set to which a particular element belongs. The purpose is to find the topmost parent (the representative) of a variable. If a variable's parent is not itself, the function recursively updates and assigns the variable's parent to be the result of the find function. This also implements path compression, where during the find operation, we make each looked-up node point to the root (representative) directly to speed up future lookups.

   def find(x):
       if p[x] != x:
           p[x] = find(p[x])
       return p[x]

2. Initializing Parent Array: A parent array p is initialized to keep track of the representative of each element (in this case, variables represented by lowercase letters). It starts with each element being its own parent, which means they are each in their own separate set.

   p = list(range(26))  # 26 for the number of lowercase English letters

3. Union Operation: For each equality equation (denoted by ==), we union the sets containing the variables of the equation. This involves changing the parent of one element to be the parent of the other element, therefore establishing that they are in the same set (they are connected or equal).

   for e in equations:
       if e[1] == '=':
           a, b = ord(e[0]) - ord('a'), ord(e[-1]) - ord('a')
           p[find(a)] = find(b)

   Here, ord function converts a character into its corresponding ASCII value, and the subtraction of ord('a') is done to get an index from 0 to 25 for each letter.

4. Checking Inequalities: After equalities are processed, we iterate over the inequality equations (denoted by !=). For each inequality, we check if the variables are in the same set by comparing their parents. If they have the same parent, it implies they are equal (by the union operations done previously), which contradicts the inequality condition.

   for e in equations:
       if e[1] == '!' and find(a) == find(b):
           return False

5. Returning the Result: If no contradictions are found in the inequality equations, we return true since all equations can be satisfied with the unions performed.

By applying the union-find data structure for the equality relationships first and then checking for any violations in the inequality relationships later, we can efficiently resolve if all equations can hold true concurrently or not.

Example Walkthrough

Let's walk through a small example to illustrate the solution approach.

Suppose we have the following equations:

["a==b", "b!=c", "c==a"]

Our goal is to determine if we can assign integers to each variable (a, b, and c) such that these equations are simultaneously satisfied.

1. Initial Parent Array: We initialize an array p representing the parents of each variable.

   p = [0, 1, 2]  // since we have 26 letters, for simplicity let's consider only indices for a, b, and c.

2. Process Equalities: Following the equalities, "a==b" and "c==a":

   • For "a==b":

     • We find the parents of a (which is 0) and b (which is 1).

     • Since a and b are not in the same set, we perform a union by setting the parent of a to be the parent of b. Now, p[0] is 1.

       p = [1, 1, 2]  // a and b are now in the same set.

   • For "c==a":

     • We find the parents of c (which is 2) and a (now 1 after compression).

     • We perform a union by setting the parent of c to be the parent of a. Now, p[2] is 1.

       p = [1, 1, 1] // a, b, and c are now all connected.

3. Checking Inequalities: We go over the inequality "b!=c":

   • We find the parents for b and c, which is index 1 for both after compression.
   • Since b and c have the same parent, they are considered to be in the same set, implying they are equal according to our previous unions, which contradicts the inequality "b!=c".
   • Thus, we return False at this step because there is no way we can satisfy this inequality given the equality connections that we have already made.

With this example, it is clear that the union-find algorithm helps efficiently determine the connectivity between variables, and due to the contradicting inequality, the entire set of equations cannot be satisfied simultaneously.

Solution Implementation

Time and Space Complexity

Time Complexity

The time complexity of the code consists of two main parts: the union operations that occur when equations with "==" are processed, and the find operations to check for contradictions in equations with "!=".

• The find function performs a path compression, which is an optimization of the Disjoint Set Union (DSU) data structure. With path compression, the complexity of each find operation is amortized to O(alpha(n)), where alpha(n) is the inverse Ackermann function, which grows very slowly and is considered almost constant for all practical purposes.
• During the first loop, there are potentially n union operations (one for each "==" equation).
• During the second loop, there may be up to n find operations (one for each "!=" equation).

Since n is the number of equations given, the time complexity approximates to O(n * alpha(n)), but it's commonly denoted as O(n) due to the negligible growth of alpha(n).

Space Complexity

The space complexity is determined by the space required for the parent array p.

• There is a fixed size parent array p of size 26 to account for each lowercase letter from 'a' to 'z'.

Therefore, the space complexity of the code is O(1), as space does not scale with the input size and remains constant because we are only dealing with lowercase English letters, which are just 26.

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