133 citations · 192 across the 9 of their papers we have counts for
9 papers · 1 filter
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…
Learning Convex Optimization Control Policies
Akshay Agrawal, Shane Barratt, Stephen Boyd +1
Many control policies used in various applications determine the input or action by solving a convex optimization problem that depends on the current state and some parameters. Com…
Minimizing a Sum of Clipped Convex Functions
Shane Barratt, Guillermo Angeris, Stephen Boyd
We consider the problem of minimizing a sum of clipped convex functions; applications include clipped empirical risk minimization and clipped control. While the problem of minimizi…
Fitting a Kalman Smoother to Data
Shane Barratt, Stephen Boyd
This paper considers the problem of fitting the parameters of a Kalman smoother to data. We formulate the Kalman smoothing problem with missing measurements as a constrained least…
Least Squares Auto-Tuning
Shane Barratt, Stephen Boyd
Least squares is by far the simplest and most commonly applied computational method in many fields. In almost all applications, the least squares objective is rarely the true objec…