Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Cherukuri, Ashish"'
This paper presents a first-order distributed algorithm for solving a convex semi-infinite program (SIP) over a time-varying network. In this setting, the objective function associated with the optimization problem is a summation of a set of function
Externí odkaz:
http://arxiv.org/abs/2408.11937
Autor:
Verbree, Jasper, Cherukuri, Ashish
This work explores the relationship between the set of Wardrop equilibria~(WE) of a routing game, the total demand of that game, and the occurrence of Braess's paradox~(BP). The BP formalizes the counter-intuitive fact that for some networks, removin
Externí odkaz:
http://arxiv.org/abs/2310.04256
Autor:
Zolanvari, Alireza, Cherukuri, Ashish
This paper considers a risk-constrained motion planning problem and aims to find the solution combining the concepts of iterative model predictive control (MPC) and data-driven distributionally robust (DR) risk-constrained optimization. In the iterat
Externí odkaz:
http://arxiv.org/abs/2310.04141
Autor:
Zolanvari, Alireza, Cherukuri, Ashish
This paper proposes an iterative distributionally robust model predictive control (MPC) scheme to solve a risk-constrained infinite-horizon optimal control problem. In each iteration, the algorithm generates a trajectory from the starting point to th
Externí odkaz:
http://arxiv.org/abs/2308.11510
Autor:
Verbree, Jasper, Cherukuri, Ashish
This paper considers variational inequalities (VI) defined by the conditional value-at-risk (CVaR) of uncertain functions and provides three stochastic approximation schemes to solve them. All methods use an empirical estimate of the CVaR at each ite
Externí odkaz:
http://arxiv.org/abs/2211.07227
Apprenticeship learning is a framework in which an agent learns a policy to perform a given task in an environment using example trajectories provided by an expert. In the real world, one might have access to expert trajectories in different environm
Externí odkaz:
http://arxiv.org/abs/2209.02424
Autor:
Cherukuri, Ashish
This paper focuses on a class of variational inequalities (VIs), where the map defining the VI is given by the component-wise conditional value-at-risk (CVaR) of a random function. We focus on solving the VI using sample average approximation, where
Externí odkaz:
http://arxiv.org/abs/2208.11403
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 two-step approach to Wasserstein distributionally robust chance- and security-constrained dispatch
This paper considers a security constrained dispatch problem involving generation and line contingencies in the presence of the renewable generation. The uncertainty due to renewables is modeled using joint chance-constraint and the mismatch caused b
Externí odkaz:
http://arxiv.org/abs/2208.07642
This paper studies the problem of intervention design for steering the actions of noncooperative players in quadratic network games to the social optimum. The players choose their actions with the aim of maximizing their individual payoff functions,
Externí odkaz:
http://arxiv.org/abs/2205.15673