Poster

Generalization of Quasi-Newton Methods: Application to Robust Symmetric Multisecant Updates

Damien Scieur · Lewis Liu · Thomas Pumir · Nicolas Boumal

Keywords: [ Algorithms, Optimization and Computation Methods ] [ Convex optimization ]

[ Abstract ]
Tue 13 Apr 2 p.m. PDT — 4 p.m. PDT

Abstract:

Quasi-Newton (qN) techniques approximate the Newton step by estimating the Hessian using the so-called secant equations. Some of these methods compute the Hessian using several secant equations but produce non-symmetric updates. Other quasi-Newton schemes, such as BFGS, enforce symmetry but cannot satisfy more than one secant equation. We propose a new type of quasi-Newton symmetric update using several secant equations in a least-squares sense. Our approach generalizes and unifies the design of quasi-Newton updates and satisfies provable robustness guarantees.

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