Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Kalaimani, Rachel K"'
This letter investigates the convergence and concentration properties of the Stochastic Mirror Descent (SMD) algorithm utilizing biased stochastic subgradients. We establish the almost sure convergence of the algorithm's iterates under the assumption
Externí odkaz:
http://arxiv.org/abs/2407.05863
Saddle point problems, ubiquitous in optimization, extend beyond game theory to diverse domains like power networks and reinforcement learning. This paper presents novel approaches to tackle saddle point problem, with a focus on continuous-time conte
Externí odkaz:
http://arxiv.org/abs/2404.04907
This letter presents an almost sure convergence of the zeroth-order mirror descent algorithm. The algorithm admits non-smooth convex functions and a biased oracle which only provides noisy function value at any desired point. We approximate the subgr
Externí odkaz:
http://arxiv.org/abs/2303.09793
Publikováno v:
Energy Informatics Journal 2018
Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is
Externí odkaz:
http://arxiv.org/abs/1810.10619
Publikováno v:
In IFAC PapersOnLine 2022 55(30):448-453
The problem of distributed controller synthesis for formation control of multi-agent systems is considered. The agents (single integrators) communicate over a communication graph and a decentralized linear feedback structure is assumed. One of the ag
Externí odkaz:
http://arxiv.org/abs/1305.3797
Publikováno v:
In Systems & Control Letters March 2017 101:28-36
Publikováno v:
In Linear Algebra and Its Applications 15 December 2013 439(12):4003-4022
Publikováno v:
53rd IEEE Conference on Decision & Control; 2014, p6407-6412, 6p
Publikováno v:
2013 European Control Conference (ECC); 2013, p1740-1745, 6p