Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Pasupathy, Raghu"'
Motivated by applications in emergency response and experimental design, we consider smooth stochastic optimization problems over probability measures supported on compact subsets of the Euclidean space. With the influence function as the variational
Externí odkaz:
http://arxiv.org/abs/2407.00307
Model update (MU) and candidate evaluation (CE) are classical steps incorporated inside many stochastic trust-region (TR) algorithms. The sampling effort exerted within these steps, often decided with the aim of controlling model error, largely deter
Externí odkaz:
http://arxiv.org/abs/2405.20116
We present batching as an omnibus device for uncertainty quantification using simulation output. We consider the classical context of a simulationist performing uncertainty quantification on an estimator $\theta_n$ (of an unknown fixed quantity $\the
Externí odkaz:
http://arxiv.org/abs/2311.04159
We propose a general purpose confidence interval procedure (CIP) for statistical functionals constructed using data from a stationary time series. The procedures we propose are based on derived distribution-free analogues of the $\chi^2$ and Student'
Externí odkaz:
http://arxiv.org/abs/2307.08609
Stochastic Gradient (SG) is the defacto iterative technique to solve stochastic optimization (SO) problems with a smooth (non-convex) objective $f$ and a stochastic first-order oracle. SG's attractiveness is due in part to its simplicity of executing
Externí odkaz:
http://arxiv.org/abs/2103.04392
Autor:
Pasupathy, Raghu, Song, Yongjia
We present adaptive sequential SAA (sample average approximation) algorithms to solve large-scale two-stage stochastic linear programs. The iterative algorithm framework we propose is organized into \emph{outer} and \emph{inner} iterations as follows
Externí odkaz:
http://arxiv.org/abs/2012.03761
Akademický článek
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We consider a single stage stochastic program without recourse with a strictly convex loss function. We assume a compact decision space and grid it with a finite set of points. In addition, we assume that the decision maker can generate samples of th
Externí odkaz:
http://arxiv.org/abs/1811.07186
We quantify the large deviations of Gaussian extreme value statistics on closed convex sets in d-dimensional Euclidean space. The asymptotics imply that the extreme value distribution exhibits a rate function that is a simple quadratic function of a
Externí odkaz:
http://arxiv.org/abs/1810.12132
We study the optimal placement problem of a stock trader who wishes to clear his/her inventory by a predetermined time horizon t, by using a limit order or a market order. For a diffusive market, we characterize the optimal limit order placement poli
Externí odkaz:
http://arxiv.org/abs/1708.04337