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



Grouped by topic and author lists are alphabetical.

Interior point methods for problems with nonconvex constraints

This work studies interior point methods for finding KKT points of the problem min_{x in mathbf{R}} f(x) subject to a(x) le 0. We assume f : mathbf{R}^{n} rightarrow mathbf{R} and a : mathbf{R}^{n} rightarrow mathbf{R}^m are twice differentiable functions. We develop theory and implementations. The work consists of the following papers.

The complexity of finding stationary points of nonconvex functions

This work studies the worst-case runtime of first-order methods for finding points with | nabla f(x) | le epsilon under the assumptions that the function f : mathbf{R}^{n} rightarrow mathbf{R} derivatives are not changing too quickly (Lipschitz). The work consists of the following papers.

For a brief overview of the lower bounds see our NIPS 2017 workshop paper.

Scheduling and integer programming

Market design