Oliver Hinder

 

I am PhD Candidate in the Department of Management Science and Engineering at Stanford in the Operations Research group. My advisor is Yinyu Ye.

Currently, I focus on developing efficient algorithms for finding local optimum to continuous nonconvex optimization problems. Roughly speaking, my research is split into constrained optimization where I work on interior point methods and unconstrained optimization where I focus on first order methods. However, my broad areas of interests include market design, integer programming, machine scheduling, and machine learning.

Feel free to contact me with any questions. My email is ohinder at stanford dot edu.

Scholar page

CV

Papers

A one-phase interior point method for nonconvex optimization Joint work with Yinyu Ye. Code for solver

Lower Bounds for Finding Stationary Points II: First-Order Methods. Joint work with Yair Carmon, John Duchi and Aaron Sidford.

Lower Bounds for Finding Stationary Points I. Joint work with Yair Carmon, John Duchi and Aaron Sidford.

‘Convex Until Proven Guilty’: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. Accepted into ICML 2017. Joint work with Yair Carmon, John Duchi and Aaron Sidford.

Accelerated Methods for Non-Convex Optimization. Joint work with Yair Carmon, John Duchi and Aaron Sidford.

A novel integer programming formulation for scheduling with family setup times on a single machine to minimize maximum lateness. 2017. Accepted to European Journal of Operations Research. Joint work with Andrew Mason.

The Stable Matching Linear Program and an Approximate Rural Hospital Theorem with Couples. 2015. Accepted into WINE.