  Quadratic constrained quadratic programming problems often occur in various
fields such as engineering practice, management science, and network
communication. This article mainly studies a non convex quadratic programming
problem with convex quadratic constraints. Firstly, based on our existing
results, the problem is reconstructed as an equivalent problem with a simple
concave quadratic objective function in the result space, with a convex
feasible domain. A global optimization algorithm for solving equivalent
problems is proposed based on a branch and bound framework that can ensure the
global optimality of the solution. This algorithm combines effective relaxation
processes with branching processes related to new external approximation
techniques. Finally, the theoretical feasibility of the algorithm was analyzed.
