Abstract.
This paper investigates quasi-Newton updates for equality-constrained optimization. Using a least-change argument we derive a class of rank-3 updates to approximations of the one-sided projection of the Hessian of the Lagrangian which keeps the appropriate part symmetric (and possibly positive definite). By imposing the usual assumptions we are able to prove 1-step superlinear convergence for one of these updates. Encouraging numerical results and comparisons with other previously analyzed updates are presented.
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Received: May 3, 1999 / Accepted: January 28, 2000¶Published online March 15, 2000
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Wagner, M., Todd, M. Least-change quasi-Newton updates for equality-constrained optimization. Math. Program. 87, 317–350 (2000). https://doi.org/10.1007/s101070050117
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DOI: https://doi.org/10.1007/s101070050117