221 citations · 588 across the 17 of their papers we have counts for
6 papers · 1 filter
Learning Convex Optimization Models
Akshay Agrawal, Shane Barratt, Stephen Boyd
A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cas…
Multi-Period Liability Clearing via Convex Optimal Control
Shane Barratt, Stephen Boyd
We consider the problem of determining a sequence of payments among a set of entities that clear (if possible) the liabilities among them. We formulate this as an optimal control p…
Optimal Representative Sample Weighting
Shane Barratt, Guillermo Angeris, Stephen Boyd
We consider the problem of assigning weights to a set of samples or data records, with the goal of achieving a representative weighting, which happens when certain sample averages…
Convex Optimization Over Risk-Neutral Probabilities
Shane Barratt, Jonathan Tuck, Stephen Boyd
We consider a collection of derivatives that depend on the price of an underlying asset at expiration or maturity. The absence of arbitrage is equivalent to the existence of a risk…
Automatic Repair of Convex Optimization Problems
Shane Barratt, Guillermo Angeris, Stephen Boyd
Given an infeasible, unbounded, or pathological convex optimization problem, a natural question to ask is: what is the smallest change we can make to the problem's parameters such…
Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint
Malayandi Palan, Shane Barratt, Alex McCauley +3
We consider the problem of learning a linear control policy for a linear dynamical system, from demonstrations of an expert regulating the system. The standard approach to this pro…