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Adaptive Cubic Regularization Methods for Nonconvex Unconstrained Optimization

Ziyan Zhu


Adaptive cubic regularization methods have several favorable properties for nonconvex optimization. In particular, under mild assumptions, they are globally convergent to a second-order stationary point. In this talk, I will introduce an adaptive cubic regularization method for unconstrained optimization. Methods analogous to those used to solve the trust-region subproblem will be discussed for solving the local cubic model. Some numerical results will be presented that compare a cubic regularized Newton's method, a standard trust-region method and a trust-search method.

Tuesday, November 12, 2019
11:00AM AP&M 2402