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Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods
2012
Neural Information Processing Systems
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We show that if pairs of function values are available, algorithms that use gradient estimates based on random perturbations suffer a factor of at most √ d in convergence rate over traditional stochastic ...
We consider derivative-free algorithms for stochastic optimization problems that use only noisy function values rather than gradients, analyzing their finite-sample convergence rates. ...
JCD was also supported by an NDSEG fellowship and a Facebook PhD fellowship. ...
dblp:conf/nips/DuchiJWW12
fatcat:cwbm4en7czenbimttld4qarkz4
Feature Article: Optimization for simulation: Theory vs. Practice
2002
INFORMS journal on computing
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The main thesis of this article, however, is that there is a disconnect between research in simulation optimization-which has addressed the stochastic nature of discrete-event simulation by concentrating ...
P robably one of the most successful interfaces between operations research and computer science has been the development of discrete-event simulation software. ...
Acknowledgments This work was supported in part by the National Science Foundation under Grants DMI-9713720 and DMI-9988867 and by the Air Force Office of Scientific Research under Grant F496200110161. ...
doi:10.1287/ijoc.14.3.192.113
fatcat:ntus2mrdhjbptc77jf2zjmgmcu
A fuzzy simulated evolution algorithm for integrated manufacturing system design
2013
International Journal of Industrial Engineering Computations
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This study seeks to develop a fuzzy simulated evolution algorithm (FSEA) that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. ...
Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. ...
Computational results based on a set of benchmark problems concerned with the cell formation problem from the literature revealed that FSEA is a competitive algorithm which can solve hard combinatorial ...
doi:10.5267/j.ijiec.2013.01.003
fatcat:g4ebtzajtfb4zcty2a6y7qnhhe
The self-learning AI controller for adaptive power beaming with fiber-array laser transmitter system
[article]
2022
arXiv
pre-print
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For optimization of power transition through the atmosphere fiber-array is traditionally controlled by stochastic parallel gradient descent (SPGD) algorithm where control feedback is provided via radio ...
A DNN training is occurred online in sync with control system operation and is performed by applying of small perturbations to DNN's outputs. ...
Vorontsov for useful discussions of fiber-array power beaming problem and A.B. Lavrentyev for the interest in AI-based control systems. ...
arXiv:2204.05227v1
fatcat:ubmfmgztqjahzj5grwnk7fjdrm
The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality
[article]
2022
arXiv
pre-print
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However, more work should take a systems thinking approach that considers the impacts of other pipeline components on RL. ...
We focus on four challenges involving (1) uncertainty in detection, (2) reliability of communications, (3) compliance and interpretability, and (4) heterogeneous road users. ...
of this review. ...
arXiv:2206.11996v2
fatcat:jwwnydngozegjcuvsbfzodqpfi
Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers
2022
Diagnostics
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This study aims to utilize artificial intelligence algorithms to build predictive models for the successful timing of the weaning of patients from MV in RCCs and to implement a dashboard with the best ...
The development of an AI prediction dashboard is a promising method to assist in the prediction of the optimal timing of weaning from MV in RCC settings. ...
The AI Center and Department of Information Systems of Chi Mei embedded the XGBoost model in a web-based system (digital dashboard) for predicting the optimal MV weaning timing for patients in the RCC ...
doi:10.3390/diagnostics12040975
pmid:35454023
pmcid:PMC9030191
fatcat:f6vsr6pdq5gezdmdiaxz6fpcl4
Retrospective-approximation algorithms for the multidimensional stochastic root-finding problem
2009
ACM Transactions on Modeling and Computer Simulation
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This paper presents a family of algorithms to solve the multidimensional (q ≥ 1) SRFP, generalizing Chen and Schmeiser's onedimensional retrospective approximation (RA) family of algorithms. ...
We focus on a specific member of the family, called the Bounding RA algorithm, which finds a sequence of polytopes that progressively decrease in size while approaching the solution. ...
See Chen (1994) for more on (1), (2), (3) and (4).
COMPLICATING ISSUES The SRFP is one stochastic generalization of the problem of solving a non-linear deterministic system of equations. ...
doi:10.1145/1502787.1502788
fatcat:iogtlriohnhl7p7mvwlsxgf42i
Autonomous discovery in the chemical sciences part I: Progress
2019
Angewandte Chemie International Edition
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2020 pre-print arXiv:2003.13754v1 fatcat:4cfpoqs76jbj3h4gksgr65wdau
Part two reflects on these case studies and identifies a set of open challenges for the field. ...
We then introduce a set of questions and considerations relevant to assessing the extent of autonomy. ...
Acknowledgements We thank Thomas Struble for providing comments on the manuscript and our other colleagues and collaborators for useful conversations around this topic. ...
doi:10.1002/anie.201909987
pmid:31553511
fatcat:yfg4jnixhvfgdmnr4p6mbyolpe
A simulation-based approach to capturing autocorrelated demand parameter uncertainty in inventory management
2012
Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)
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Using a simulation-based sampling algorithm, we quantify the expected cost due to parameter uncertainty as a function of the length of the historical demand data, the critical fractile, the parameters ...
We consider a repeated newsvendor setting where the parameters of the demand distribution are unknown, and we study the problem of setting inventory targets using only a limited amount of historical demand ...
We approximate the expected cost due to demand parameter uncertainty using a simulation-based sampling algorithm. ...
doi:10.1109/wsc.2012.6465035
dblp:conf/wsc/AkcayBT12
fatcat:qeskm75ag5dadiuqse2pcfiru4
Simulation optimization: A tutorial overview and recent developments in gradient-based methods
2014
Proceedings of the Winter Simulation Conference 2014
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2.2 is based heavily on material in Chau and Fu (2014). ...
We then describe some recent research in two areas of simulation optimization: stochastic approximation and the use of direct stochastic gradients for simulation metamodels. ...
Specifically Chapter 2 presents some locally and globally convergent algorithms for discrete optimization on a large countable (possibly infinite) set, and Chapter 12 presents iterative model-based algorithms ...
doi:10.1109/wsc.2014.7019875
dblp:conf/wsc/ChauFQR14
fatcat:jzkyzx6kbnfephf6xtmea7dxoq
Design of large polyphase filters in the Quadratic Residue Number System
2010
2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers
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We conjecture, based on our simulation results, that the LRT rule is always optimal both in terms of robustness and error probability. ...
This paper studies the optimization of a multicell MIMO downlink system in which each base station serves multiple users, and each user is served by only one base station. ...
doi:10.1109/acssc.2010.5757589
fatcat:ccxnu5owr5fyrcjcqukumerueq
Optimal disease outbreak decisions using stochastic simulation
2011
Proceedings of the 2011 Winter Simulation Conference (WSC)
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We present a methodology for dynamic determination of optimal policies in a stochastic compartmental model with parameter uncertainty. ...
The latter step is simulation-based and relies on regression Monte Carlo techniques. To improve performance we investigate lasso regression and global policy iteration. ...
ACKNOWLEDGEMENTS Ludkovski's research was partially supported by the Hellman Family Foundation Grant. ...
doi:10.1109/wsc.2011.6148076
dblp:conf/wsc/LudkovskiN11
fatcat:du53tr64krhfbcffkvny2kd4ie
Unconstraining methods for revenue management systems under small demand
2017
Journal of Revenue and Pricing Management
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Empirical results on accuracy and revenue performance based on data from a major car rental company indicate revenue improvements over a best practice benchmark by statistically significant 0.5%-1.4% in ...
Furthermore, they are numerically robust due to our proposed group-based parameter optimization. ...
Acknowledgement All authors contributed equally and are named in alphabetical order. ...
doi:10.1057/s41272-017-0117-x
fatcat:qb7hp4td4rf6pgqujgwbwohz74
A Simulation Optimization Approach to Epidemic Forecasting
2013
PLoS ONE
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This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying ...
Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. ...
Acknowledgments We thank our external collaborators and members of the Network Dynamics and Simulation Science Laboratory (NDSSL) for their suggestions and comments. ...
doi:10.1371/journal.pone.0067164
pmid:23826222
pmcid:PMC3694918
fatcat:d2ouvfwtlfelljfkijl6afr5pa
Integer-Ordered Simulation Optimization using R-SPLINE
2013
ACM Transactions on Modeling and Computer Simulation
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We consider simulation-optimization (SO) models where the decision variables are integer ordered and the objective function is defined implicitly via a simulation oracle, which for any feasible solution ...
We develop R-SPLINE-a Retrospective-search algorithm that alternates between a continuous Search using Piecewise-Linear Interpolation and a discrete Neighborhood Enumeration, to asymptotically identify ...
We thank the area editor, associate editor and the two referees for the high quality of reviews. ...
doi:10.1145/2499913.2499916
fatcat:liczv6ulsbhhnm6ubbmgaarptu
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