Yu Li

Cyclic steepest descent is R-superlinearly convergent for almost every initialization on two-spectrum quadratics

We completely classify cyclic steepest descent (CSD) on strictly convex quadratics whose Hessian has exactly two distinct eigenvalues. At the beginning of each cycle CSD recomputes an exact steepest-descent stepsize and reuses it for m updates. For every m >= 2, write A = aP + bQ...

Cyclic steepest descent is R-superlinearly convergent for almost every initialization on two-spectrum quadratics

We completely classify cyclic steepest descent (CSD) on strictly convex quadratics whose Hessian has exactly two distinct eigenvalues. At the beginning of each cycle CSD recomputes an exact steepest-descent stepsize and reuses it for m updates. For every m >= 2, write A = aP + bQ...