Convergence Properties of Dikin's Affine Scaling Algorithm for Nonconvex Quadratic Minimization

We study convergence properties of Dikin's affine scaling algorithm applied to nonconvex quadratic minimization. First, we show that the objective function value either diverges or converges Q‐linearly to a limit. Using this result, we show that, in the case of box constraints, the iterates converge to a unique point satisfying first‐order and weak second‐order optimality conditions, assuming the objective function Hessian Q is rank dominant with respect to the principal submatrices that are maximally positive semidefinite. Such Q include matrices that are positive semidefinite or negative semidefinite or nondegenerate or have negative diagonals. Preliminary numerical experience is reported.