Eric Sager Luxenberg
Eric Luxenberg is a PhD candidate in the Electrical Engineering department at Stanford University, advised by Stephen Boyd. His research interests include robust optimization and mathematical finance. He is a contributor to CVXPY, and has developed an open-source package for saddle optimization called DSP. He has also served as the primary instructor of Stanford’s convex optimization course.
Sessions
In this tutorial, attendees will learn hands-on how to optimize the trajectory of a self-landing rocket in a real-time simulated setting using CVXPY, a Python-embedded modeling language for convex optimization. We integrate the optimization with the Kerbal Space Program, to showcase a complete landing mission without human intervention, ideally in one piece. CVXPY allows solving complex problems declaratively, letting convex optimization find an optimal way of meeting target conditions with respect to an objective function. After solving the initial problem, attendees will use a selection of advanced CVXPY features while making the example gradually more realistic.
Our recent work implements a domain-specific language called Disciplined Saddle Programming (DSP) in Python. It is available at https://github.com/cvxgrp/dsp. DSP allows specifying convex-concave saddle, or minimax problems, a class of convex optimization problems commonly used in game theory, machine learning, and finance. One application for DSP is to naturally describe and solve robust optimization problems. We show numerous examples of these problems, including robust regressions and economic applications. However, this only represents a fraction of problems solvable with DSP, and we want to engage with the SciPy community to hear about further potential applications.