1.2k citations · 3.4k across the 72 of their papers we have counts for
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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…
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize
Youngsuk Park, Sauptik Dhar, Stephen Boyd +1
Variable metric proximal gradient (VM-PG) is a widely used class of convex optimization method. Lately, there has been a lot of research on the theoretical guarantees of VM-PG with…
Anderson Accelerated Douglas-Rachford Splitting
Anqi Fu, Junzi Zhang, Stephen Boyd
We consider the problem of non-smooth convex optimization with linear equality constraints, where the objective function is only accessible through its proximal operator. This prob…
Disciplined Quasiconvex Programming
Akshay Agrawal, Stephen Boyd
We present a composition rule involving quasiconvex functions that generalizes the classical composition rule for convex functions. This rule complements well-known rules for the c…