activity
20182022
most citedAccelerating Quadratic Optimization with Reinforcement Learning

20 citations · 20 across the 3 of their papers we have counts for

collaborators
Showing math.OCShow all

6 papers · 1 filter

math.OC2022

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…

math.OC2020

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…

math.OC2020

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…

math.OC2020

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…

math.OC2019

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…

math.OC2018

Decentralized Resource Allocation via Dual Consensus ADMM

Goran Banjac, Felix Rey, Paul Goulart +1

We consider a resource allocation problem over an undirected network of agents, where edges of the network define communication links. The goal is to minimize the sum of agent-spec…