Editorial
GCD Subset Challenge
1. Problem Understanding
We are given an array of positive integers and asked to count the number of non-empty subsets such that the greatest common divisor (GCD) of all elements in the subset is exactly . In other words, we need to identify those subsets in which the numbers, when taken together, are "co-prime" in the sense that no divisor greater than can evenly divide all of them. Since checking every subset by brute force is infeasible for as large as , an efficient combinatorial and number-theoretic approach, often employing inclusion-exclusion or its related concept, Möbius inversion, must be used.
2. Solution Analysis
The idea is to leverage the properties of divisibility and the principle of inclusion-exclusion. Here is a step-by-step breakdown:
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Step 1: Counting Subsets with Divisibility Constraints
For every integer , consider counting the number of non-empty subsets where every number in the subset is divisible by . Let be the number of such subsets.
If we denote by the number of elements in the array that are multiples of , then the number of possible non-empty subsets is:
This formula holds because each element that is a multiple of can either be chosen or not, and we subtract to exclude the empty subset. -
Step 2: Using Möbius Inversion
Notice that a subset with will be counted in but might also be over-counted in for other divisors .
The Möbius function helps correct for this over-counting. By Möbius inversion, the number of valid subsets (with ) is given by:
where the summation is taken over all possible divisors up to some maximum value (which can be assumed as the maximum value in the input array), and is defined as:- if is a square-free positive integer with an even number of prime factors.
- if is a square-free positive integer with an odd number of prime factors.
- if has a squared prime factor.
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Step 3: Preprocessing Step
To efficiently compute for all relevant , count the numbers in the array which are divisible by . This can be done by:- Computing the frequency of each element in the given array.
- Iterating over each divisor up to the maximum number and summing up frequencies for multiples of .
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Step 4: Modular Arithmetic
Since the answers can be very large, and we need to report the result modulo , all arithmetic operations (especially when computing powers of ) must be performed modulo .
3. Time and Space Complexity Analysis
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Time Complexity:
- Counting frequencies requires time.
- The divisor aggregation process iterates over all divisors for each number up to the maximum value (which is up to ) and hence takes approximately where is the maximum value among array elements.
- Precomputing the Möbius function via a sieve-like method takes .
- Calculating modular exponents for each divisor takes constant time on average (using fast exponentiation), leading to a total time close to operations.
Overall, the approach works in time, which is acceptable for .
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Space Complexity:
- The additional space used is for the frequency array and auxiliary arrays for the Möbius function and , each of size .
Therefore, the space complexity is .
- The additional space used is for the frequency array and auxiliary arrays for the Möbius function and , each of size .