In this work, we present global analysis of the iteration complexity of inexact successive quadratic approximation methods, showing that an inexact solution of the subproblem that is within a fixed multiplicative precision of optimality suffices to guarantee the same order of convergence rate as the exact version, with complexity related in an intuitive way to the measure of inexACTness.Expand

Variants of the coordinate descent approach for minimizing a nonlinear function are distinguished in part by the order in which coordinates are considered for relaxation. Three common orderings are… Expand

We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving ERM problems with a nonsmooth regularization term.Expand

We propose an approach based on neural networks and the AC power flow equations to identify single- and double- line outages in a power grid using the information from phasor measurement unit sensors (PMUs).Expand

It is well known that both gradient descent and stochastic coordinate descent achieve global convergence rate of $O(1/k)$ in the objective value, when applied to a scheme for minimizing a Lipschitz-continuously differentiable, unconstrained convex function.Expand

We propose a communication- and computation-efficient distributed optimization algorithm using second-order information for solving empirical risk minimization (ERM) problems with a nonsmooth regularization term.Expand

This work proposes an inexact randomized block-coordinate descent method based on a regularized quadratic subproblem, in which the quadrastic term can vary from iteration to iteration: a variable metric.Expand

Successive quadratic approximations, or second-order proximal methods, are useful for minimizing functions that are a sum of a smooth part and a convex, possibly nonsmooth part that promotes… Expand