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SP2: A Second Order Stochastic Polyak Method
  • Shuang Li, UCLA, Los Angeles, United States
  • William J. Swartworth, UCLA, Los Angeles, United States
  • Martin Takac, Mohamed bin Zayed University of Artificial Intelligence
  • Deanna Needell, UCLA, Los Angeles, United State
  • Robert M. Gower, Center for Computational Mathematics, Flatiron Institute, Simons Foundation, New York, 10010, NY, United States
Document Type

Recently the SP (Stochastic Polyak step size) method has emerged as a competitive adaptive method for setting the step sizes of SGD. SP can be interpreted as a method specialized to interpolated models, since it solves the interpolation equations. SP solves these equation by using local linearizations of the model. We take a step further and develop a method for solving the interpolation equations that uses the local second-order approximation of the model. Our resulting method SP2 uses Hessian-vector products to speed-up the convergence of SP. Furthermore, and rather uniquely among second-order methods, the design of SP2 in no way relies on positive definite Hessian matrices or convexity of the objective function. We show SP2 is very competitive on matrix completion, non-convex test problems and logistic regression. We also provide a convergence theory on sums-of-quadratics. Copyright © 2022, The Authors. All rights reserved.

Publication Date
  • Machine learning,
  • Stochastic systems

IR Deposit conditions: non-described

Preprint available on arXiv

Citation Information
S. Li, W.J. Swartworth, M. Takac, D. Needell, and R.M. Gower, "SP2: A Second Order Stochastic Polyak Method", 2022, arXiv:2207.08171