In computer science, the subset sum problem is an important problem in complexity theory and cryptography. The problem is this: given a set of integers, is there a non-empty subset whose sum is zero? For example, given the set { −7, −3, −2, 5, 8}, the answer is yes because the subset { −3, −2, 5} sums to zero. The problem is NP-complete.
An equivalent problem is this: given a set of integers and an integer s, does any non-empty subset sum to s? Subset sum can also be thought of as a special case of the knapsack problem.[1] One interesting special case of subset sum is the partition problem, in which s is half of the sum of all elements in the set.
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The complexity (difficulty of solution) of subset sum can be viewed as depending on two parameters, N, the number of decision variables, and P, the precision of the problem (stated as the number of binary place values that it takes to state the problem). (Note: here the letters N and P mean something different than what they mean in the NP class of problems.)
The complexity of the best known algorithms is exponential in the smaller of the two parameters N and P. Thus, the problem is most difficult if N and P are of the same order. It only becomes easy if either N or P becomes very small.
If N (the number of variables) is small, then an exhaustive search for the solution is practical. If P (the number of place values) is a small fixed number, then there are dynamic programming algorithms that can solve it exactly.
What is happening is that the problem becomes seemingly non-exponential when it is practical to count the entire solution space. There are two ways to count the solution space in the subset sum problem. One is to count the number of ways the variables can be combined. There are 2N possible ways to combine the variables. However, with N = 10, there are only 1024 possible combinations to check. These can be counted easily with a branching search. The other way is to count all possible numerical values that the combinations can take. There are 2P possible numerical sums. However, with P = 5 there are only 32 possible numerical values that the combinations can take. These can be counted easily with a dynamic programming algorithm. When N = P and both are large, then there is no aspect of the solution space that can be counted easily.
Efficient algorithms for both small N and small P cases are given below.
There are several ways to solve subset sum in time exponential in N. The most naïve algorithm would be to cycle through all subsets of N numbers and, for every one of them, check if the subset sums to the right number. The running time is of order O(2NN), since there are 2N subsets and, to check each subset, we need to sum at most N elements.
A better exponential time algorithm is known which runs in time O(2N/2). The algorithm splits arbitrarily the N elements into two sets of N/2 each. For each of these two sets, it stores a list of the sums of all 2N/2 possible subsets of its elements. Each of these two lists is then sorted. Using a standard comparison sorting algorithm for this step would take time O(2N/2N). However, given a sorted list of sums for k elements, the list can be expanded to two sorted lists with the introduction of a (k + 1)st element, and these two sorted lists can be merged in time O(2k). Thus, each list can be generated in sorted form in time O(2N/2). Given the two sorted lists, the algorithm can check if an element of the first array and an element of the second array sum up to s in time O(2N/2). To do that, the algorithm passes through the first array in decreasing order (starting at the largest element) and the second array in increasing order (starting at the smallest element). Whenever the sum of the current element in the first array and the current element in the second array is more than s, the algorithm moves to the next element in the first array. If it is less than s, the algorithm moves to the next element in the second array. If two elements with sum s are found, it stops. Horowitz and Sahni first published this algorithm in a technical report in 1972.[2]
The problem can be solved as follows using dynamic programming. Suppose the sequence is
and we wish to determine if there is a nonempty subset which sums to s. Let N be the sum of the negative values and P the sum of the positive values. Define the boolean-valued function Q(i,s) to be the value (true or false) of
Thus, the solution to the problem is the value of Q(n,0).
Clearly, Q(i,s) = false if s < N or s > P so these values do not need to be stored or computed. Create an array to hold the values Q(i,s) for 1 ≤ i ≤ n and N ≤ s ≤ P.
The array can now be filled in using a simple recursion. Initially, for N ≤ s ≤ P, set
Then, for i = 2, …, n, set
For each assignment, the values of Q on the right side are already known, either because they were stored in the table for the previous value of i or because Q(i − 1,s − xi) = false if s − xi < N or s − xi > P. Therefore, the total number of arithmetic operations is O(n(P − N)). For example, if all the values are O(nk) for some k, then the time required is O(nk+2).
This algorithm is easily modified to return the subset with sum 0 if there is one.
This solution does not count as polynomial time in complexity theory because P − N is not polynomial in the size of the problem, which is the number of bits used to represent it. This algorithm is polynomial in the values of N and P, which are exponential in their numbers of bits.
For the case that each xi is positive and bounded by the same constant, Pisinger found a linear time algorithm.[3]
An approximate version of the subset sum would be: given a set of N numbers x1, x2, ..., xN and a number s, output
If all numbers are non-negative, the approximate subset sum is solvable in time polynomial in N and 1/c.
The solution for subset sum also provides the solution for the original subset sum problem in the case where the numbers are small (again, for nonnegative numbers). If any sum of the numbers can be specified with at most P bits, then solving the problem approximately with c = 2−P is equivalent to solving it exactly. Then, the polynomial time algorithm for approximate subset sum becomes an exact algorithm with running time polynomial in N and 2P (i.e., exponential in P).
The algorithm for the approximate subset sum problem is as follows:
initialize a list S to contain one element 0.
for each i from 1 to N do
let T be a list consisting of xi + y, for all y in S
let U be the union of T and S
sort U
make S empty
let y be the smallest element of U
add y to S
for each element z of U in increasing order do
//trim the list by eliminating numbers close to one another
//and throw out elements greater than s
if y + cs/N < z ≤ s, set y = z and add z to S
if S contains a number between (1 − c)s and s, output yes, otherwise no
The algorithm is polynomial time because the lists S, T and U always remain of size polynomial in N and 1/c and, as long as they are of polynomial size, all operations on them can be done in polynomial time. The size of lists is kept polynomial by the trimming step, in which we only include a number z into S if it is greater than the previous one by cs/N and not greater than s.
This step ensures that each element in S is smaller than the next one by at least cs/N and do not contain elements greater than s. Any list with that property consists of no more than N/c elements.
The algorithm is correct because each step introduces an additive error of at most cs/N and N steps together introduce the error of at most cs.