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Trust-region algorithms for training responses: machine learning methods using indefinite Hessian approximations. (English) Zbl 1440.90092
Summary: Machine learning (ML) problems are often posed as highly nonlinear and nonconvex unconstrained optimization problems. Methods for solving ML problems based on stochastic gradient descent are easily scaled for very large problems but may involve fine-tuning many hyper-parameters. Quasi-Newton approaches based on the limited-memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update typically do not require manually tuning hyper-parameters but suffer from approximating a potentially indefinite Hessian with a positive-definite matrix. Hessian-free methods leverage the ability to perform Hessian-vector multiplication without needing the entire Hessian matrix, but each iteration’s complexity is significantly greater than quasi-Newton methods. In this paper we propose an alternative approach for solving ML problems based on a quasi-Newton trust-region framework for solving large-scale optimization problems that allow for indefinite Hessian approximations. Numerical experiments on a standard testing data set show that with a fixed computational time budget, the proposed methods achieve better results than the traditional limited-memory BFGS and the Hessian-free methods.
MSC:
90C53 Methods of quasi-Newton type
15A06 Linear equations (linear algebraic aspects)
90C06 Large-scale problems in mathematical programming
65K05 Numerical mathematical programming methods
65K10 Numerical optimization and variational techniques
49M15 Newton-type methods
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