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The multiple subset sum problem is an optimization problem in computer science and operations research.It is a generalization of the subset sum problem.The input to the problem is a multiset of n integers and a positive integer m representing the number of subsets.
Conversely, given a solution to the SubsetSumZero instance, it must contain the −T (since all integers in S are positive), so to get a sum of zero, it must also contain a subset of S with a sum of +T, which is a solution of the SubsetSumPositive instance. The input integers are positive, and T = sum(S)/2.
Several additional heuristics can be used to improve the runtime: [2] In a node in which the current sum-difference is at least the sum of all remaining numbers, the remaining numbers can just be put in the smallest-sum subset. If we reach a leaf in which the sum-difference is 0 or 1, then the algorithm can terminate since this is the optimum.
The two subsets should contain floor(n/2) and ceiling(n/2) items. It is a variant of the partition problem. It is NP-hard to decide whether there exists a partition in which the sums in the two subsets are equal; see [4] problem [SP12]. There are many algorithms that aim to find a balanced partition in which the sum is as nearly-equal as possible.
In number theory and computer science, the partition problem, or number partitioning, [1] is the task of deciding whether a given multiset S of positive integers can be partitioned into two subsets S 1 and S 2 such that the sum of the numbers in S 1 equals the sum of the numbers in S 2. Although the partition problem is NP-complete, there is a ...
[1]: sec.5 The problem is parametrized by a positive integer k, and called k-way number partitioning. [2] The input to the problem is a multiset S of numbers (usually integers), whose sum is k*T. The associated decision problem is to decide whether S can be partitioned into k subsets such that the sum of each subset is exactly T.
In other words, is a partition of consisting of subsets contained in . The exact cover problem to find an exact cover is a kind of constraint satisfaction problem . The elements of S {\displaystyle {\mathcal {S}}} represent choices and the elements of X {\displaystyle X} represent constraints.
The running time is of order O(2 N N), since there are 2 N 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(2 N/2 N). The algorithm splits arbitrarily the N elements into two sets of N/2 each.