Least-change quasi-Newton updates for equality-constrained optimization

This paper investigates quasi-Newton updates for equality-constrained optim ization. Using a least-change argument we derive a class of rank-3 updates to approximations of the one-sided projection of the Hessian of tho Lagrang ian which keeps the appropriate part symmetric (and possibly positive defin ite). By imposing the usual assumptions we are able to prove 1-step superli near convergence for one of these updates. Encouraging numerical results an d comparisons with other previously analyzed updates are presented.