20 citations · 20 across the 3 of their papers we have counts for
7 papers
Embedded Code Generation with CVXPY
Maximilian Schaller, Goran Banjac, Steven Diamond +3
We introduce CVXPYgen, a tool for generating custom C code, suitable for embedded applications, that solves a parametrized class of convex optimization problems. CVXPYgen is based…
Accelerating Quadratic Optimization with Reinforcement Learning
Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato +6
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapid…
Improving Tractability of Real-Time Control Schemes via Simplified -Lemma
Goran Banjac, Jianzhe Zhen, Dick den Hertog +1
Various control schemes rely on a solution of a convex optimization problem involving a particular robust quadratic constraint, which can be reformulated as a linear matrix inequal…
On the Minimal Displacement Vector of the Douglas-Rachford Operator
Goran Banjac
The Douglas-Rachford algorithm can be represented as the fixed point iteration of a firmly nonexpansive operator. When the operator has no fixed points, the algorithm's iterates di…
On the Asymptotic Behavior of the Douglas-Rachford and Proximal-Point Algorithms for Convex Optimization
Goran Banjac, John Lygeros
The authors in (Banjac et al., 2019) recently showed that the Douglas-Rachford algorithm provides certificates of infeasibility for a class of convex optimization problems. In part…
GPU Acceleration of ADMM for Large-Scale Quadratic Programming
Michel Schubiger, Goran Banjac, John Lygeros
The alternating direction method of multipliers (ADMM) is a powerful operator splitting technique for solving structured convex optimization problems. Due to its relatively low per…