activity
20172020
most citedA one-phase interior point method for nonconvex optimization

10 citations · 15 across the 3 of their papers we have counts for

collaborators

6 papers

math.OC20204 cited

An efficient nonconvex reformulation of stagewise convex optimization problems

Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli +2

Convex optimization problems with staged structure appear in several contexts, including optimal control, verification of deep neural networks, and isotonic regression. Off-the-she…

math.OC20201 cited

A generic adaptive restart scheme with applications to saddle point algorithms

Oliver Hinder, Miles Lubin

We provide a simple and generic adaptive restart scheme for convex optimization that is able to achieve worst-case bounds matching (up to constant multiplicative factors) optimal r…

math.OC2018

Cutting plane methods can be extended into nonconvex optimization

Oliver Hinder

We show that it is possible to obtain an expected runtime --- including computational cost --- for finding -stationary points of smooth nonconvex functions using c…

math.OC201810 cited

A one-phase interior point method for nonconvex optimization

Oliver Hinder, Yinyu Ye

The work of Wachter and Biegler suggests that infeasible-start interior point methods (IPMs) developed for linear programming cannot be adapted to nonlinear optimization without si…

math.OC2017

Lower Bounds for Finding Stationary Points II: First-Order Methods

Yair Carmon, John C. Duchi, Oliver Hinder +1

We establish lower bounds on the complexity of finding -stationary points of smooth, non-convex high-dimensional functions using first-order methods. We prove that deterministic…

math.OC2017

"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions

Yair Carmon, Oliver Hinder, John C. Duchi +1

We develop and analyze a variant of Nesterov's accelerated gradient descent (AGD) for minimization of smooth non-convex functions. We prove that one of two cases occurs: either our…