This paper investigates the variance reduction techniques Antithetic Variates (AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in stochastic programming presents a comparative computational study. It shows conditions under which with AV LHS satisfy finite stopping guarantees are asymptotically valid, discussing detail. computationally compares their use both non-sequent...

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 4 September 2019Accepted: 29 January 2021Published online: 13 April 2021Keywordscomposite stochastic optimization, proximal gradient method, variance reductionAMS Subject Headings68Q25, 68W20, 90C15, 90C26, 90C30Publication DataISSN (print): 1052-6234ISSN (online): 1095-7...

Modern architecture research relies heavily on detailed pipeline simulation. Simulating the full execution of an industry standard benchmark can take weeks to months. Statistical sampling and sample techniques like SimPoint that pick small sets of execution samples have been shown to provide accurate results while significantly reducing simulation time. The inefficiencies in sampling are (a) ne...