Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Banjac Goran"'
Publikováno v:
Scientific Technical Review, Vol 72, Iss 2, Pp 50-55 (2022)
Significant improvement of the unmanned vehicles possibility has achieved by increasing its autonomy, i.e. by excluding the human operator from the guidance loop. In this paper is considered the autonomous control of the unmanned tracked vehicle (UTV
Externí odkaz:
https://doaj.org/article/819de8405c024926b94c0960f0c31f94
Publikováno v:
Војно дело, Vol 70, Iss 4, Pp 222-241 (2018)
nema
Externí odkaz:
https://doaj.org/article/d2a141142a8640e2a80b9cdda7aa572f
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 over a Wasserstein ambiguity set that is centered at the empirical dis
Externí odkaz:
http://arxiv.org/abs/2208.10321
A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline. Unlike many standard existing schemes, this sche
Externí odkaz:
http://arxiv.org/abs/2207.09216
Autor:
Schaller, Maximilian, Banjac, Goran, Diamond, Steven, Agrawal, Akshay, Stellato, Bartolomeo, Boyd, Stephen
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 on CVXPY, a Python-embedded domain-specific language that supports a n
Externí odkaz:
http://arxiv.org/abs/2203.11419
Publikováno v:
Optimization Foundations for Reinforcement Learning Workshop at NeurIPS 2019, Vancouver, Canada
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, which consists of learning a policy from a finite set of expert demon
Externí odkaz:
http://arxiv.org/abs/2201.00039
Publikováno v:
International Conference of Machine Learning (ICML) 2021
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 that the learner is not allowed to interact with the expert and has no
Externí odkaz:
http://arxiv.org/abs/2112.14004
Autor:
Ichnowski, Jeffrey, Jain, Paras, Stellato, Bartolomeo, Banjac, Goran, Luo, Michael, Borrelli, Francesco, Gonzalez, Joseph E., Stoica, Ion, Goldberg, Ken
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 rapidly solved. These methods face two persistent challenges: manual hyperp
Externí odkaz:
http://arxiv.org/abs/2107.10847
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 inequality using the well-known $\mathcal{S}$-lemma. However, the computation
Externí odkaz:
http://arxiv.org/abs/2012.04688
Autor:
Banjac, Goran
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 diverge, but the difference between consecutive iterates converges to th
Externí odkaz:
http://arxiv.org/abs/2009.01201