Abstract
This paper addresses a sort of non-convex relaxation problem for the minimization problem of a quadratic function. We define relaxation problems by generalizing the feasible region of the original problem to the space consisting of hypercomplex numbers. Computational experiments for 0-1 quadratic minimization problems reveal the effectiveness of two proposed algorithms based on the derived properties. Fundamental properties of the relaxation problem for a more general class of quadratic minimization problems are also discussed.
