Computing a Trust Region Step

By Jorge J. Moré, Danny C. Sorensen. 1983

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mathematical Optimization

Citations in the corpus

New Insights and Perspectives on the Natural Gradient Method (2014, methodology): “Examples include Tikhonov regularization/damping and the closely related trustregion method (e.g. Tikhonov, 1943; Moré and Sorensen, 1983; Conn et al., 2000; Nocedal and Wright, 2006), and other ones such as the “structural damping” approach of Martens and Sutskever (2011), or the approach present i”
Space mapping: the state of the art (2004, methodology): “A good survey of methods for updating the trust-region size is given in [55].”
Mining adversarial patterns via regularized loss minimization (2010, background): “This neighborhood is the so-called trust region [13].”
Iterative Scaled Trust-Region Learning in Krylov Subspaces via Pearlmutter's Implicit Sparse Hessian-Vector Multiply (2003, background): “which is known as the dogleg step [4, 9].”
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization (2018, background): “Hence, a well-known result by Moré and Sorensen [55] yields convergence of the whole sequence (xk)k to x ∗.”
Global rates of convergence for nonconvex optimization on manifolds (2016, unknown): “See also (Sorensen, 1982; Mor´e and Sorensen, 1983) for more about the trust-region subproblem.”
A D.C. Optimization Algorithm for Solving the Trust-Region Subproblem (1998, background): “These problems play an important role in optimization and numerical analysis.”
On Computational Issues of Market-Based Optimal Power Flow (2007, methodology): “3, we use the algorithm documented in [18], which is essentially a Newton’s procedure applied to solve for in (10) where and is positive deﬁnite.”