On Finding Maximum Cardinality Subset of Vectors with a Constraint on Normalized Squared Length of Vectors Sum
This work addresses a combinatorial optimization problem in vector selection, which is incremental as it modifies a constraint from prior research.
The paper tackles the problem of selecting the largest subset of vectors under a constraint on the normalized squared length of their sum, proving it is NP-hard even for feasibility and proposing an exact algorithm with pseudo-polynomial time complexity for constant dimensions and integer data. In computational experiments, the algorithm outperforms COINBONMIN for low dimensions, while COINBONMIN is better for larger dimensions.
In this paper, we consider the problem of finding a maximum cardinality subset of vectors, given a constraint on the normalized squared length of vectors sum. This problem is closely related to Problem 1 from (Eremeev, Kel'manov, Pyatkin, 2016). The main difference consists in swapping the constraint with the optimization criterion. We prove that the problem is NP-hard even in terms of finding a feasible solution. An exact algorithm for solving this problem is proposed. The algorithm has a pseudo-polynomial time complexity in the special case of the problem, where the dimension of the space is bounded from above by a constant and the input data are integer. A computational experiment is carried out, where the proposed algorithm is compared to COINBONMIN solver, applied to a mixed integer quadratic programming formulation of the problem. The results of the experiment indicate superiority of the proposed algorithm when the dimension of Euclidean space is low, while the COINBONMIN has an advantage for larger dimensions.