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Konar, A. and Sidiropoulos, N.D. (2017) Fast Approximation Algorithms for a Class of Nonconvex QCQP Problems Using First-Order Methods. IEEE Transactions on Signal Processing, 65, 3494-3509.

has been cited by the following article:

• TITLE: An Efficient Random Algorithm for Box Constrained Weighted Maximin Dispersion Problem

  AUTHORS: Jinjin Huang

  KEYWORDS: Maximin Dispersion Problem, Successive Convex Approximation Algorithm, Quadratically Constrained Quadratic Programming (QCQP)

  JOURNAL NAME: Advances in Pure Mathematics, Vol.9 No.4, April 11, 2019

  ABSTRACT: The box-constrained weighted maximin dispersion problem is to find a point in an n-dimensional box such that the minimum of the weighted Euclidean distance from given m points is maximized. In this paper, we first reformulate the maximin dispersion problem as a non-convex quadratically constrained quadratic programming (QCQP) problem. We adopt the successive convex approximation (SCA) algorithm to solve the problem. Numerical results show that the proposed algorithm is efficient.