1.2k citations · 1.4k across the 12 of their papers we have counts for
14 papers
Homogeneous Self-Dual Embedding via Perspective Functions
Goran Banjac
We present a generalization of the well-known homogeneous self-dual embedding model, which is widely used in conic optimization. The new embedding applies to a problem of minimizin…
Data-driven distributionally robust optimization over a network via distributed semi-infinite programming
Ashish Cherukuri, Alireza Zolanvari, Goran Banjac +1
This paper focuses on solving a data-driven distributionally robust optimization problem over a network of agents. The agents aim to minimize the worst-case expected cost computed…
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking
Ahmed Aboudonia, Goran Banjac, Annika Eichler +1
A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller…
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…
Stochastic convex optimization for provably efficient apprenticeship learning
Angeliki Kamoutsi, Goran Banjac, John Lygeros
We consider large-scale Markov decision processes (MDPs) with an unknown cost function and employ stochastic convex optimization tools to address the problem of imitation learning,…
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Angeliki Kamoutsi, Goran Banjac, John Lygeros
We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations. We assume t…
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…
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking
Ahmed Aboudonia, Annika Eichler, Francesco Cordiano +2
Various efforts have been devoted to developing stabilizing distributed Model Predictive Control (MPC) schemes for tracking piecewise constant references. In these schemes, termina…
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…
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…
OSQP: An Operator Splitting Solver for Quadratic Programs
Bartolomeo Stellato, Goran Banjac, Paul Goulart +2
We present a general-purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requ…