A Self-Repairing Prefetcher in an Event-Driven Dynamic Optimization Framework

2006 ◽  
Author(s):  
Weifeng Zhang ◽  
B. Calder ◽  
D.M. Tullsen
Keyword(s):  
Dynamic Optimization ◽  
Optimization Framework ◽  
Event Driven

scholarly journals Near-Optimal A-B Testing

2020 ◽  
Vol 66 (10) ◽  
pp. 4477-4495
Author(s):  
Nikhil Bhat ◽  
Vivek F. Farias ◽  
Ciamac C. Moallemi ◽  
Deeksha Sinha
Keyword(s):  
Selection Bias ◽  
Dynamic Optimization ◽  
State Of The Art ◽  
Stopping Times ◽  
Elliptical Distribution ◽  
Statistical Efficiency ◽  
Optimization Framework ◽  
Online Setting ◽  
Operational Constraints ◽  
The Impact

We consider the problem of A-B testing when the impact of the treatment is marred by a large number of covariates. Randomization can be highly inefficient in such settings, and thus we consider the problem of optimally allocating test subjects to either treatment with a view to maximizing the precision of our estimate of the treatment effect. Our main contribution is a tractable algorithm for this problem in the online setting, where subjects arrive, and must be assigned, sequentially, with covariates drawn from an elliptical distribution with finite second moment. We further characterize the gain in precision afforded by optimized allocations relative to randomized allocations, and show that this gain grows large as the number of covariates grows. Our dynamic optimization framework admits several generalizations that incorporate important operational constraints such as the consideration of selection bias, budgets on allocations, and endogenous stopping times. In a set of numerical experiments, we demonstrate that our method simultaneously offers better statistical efficiency and less selection bias than state-of-the-art competing biased coin designs. This paper was accepted by Noah Gans, stochastic models and simulation.

A dynamic optimization framework for a Java just-in-time compiler

2001 ◽  
Vol 36 (11) ◽  
pp. 180-195 ◽  
Author(s):  
Toshio Suganuma ◽  
Toshiaki Yasue ◽  
Motohiro Kawahito ◽  
Hideaki Komatsu ◽  
Toshio Nakatani
Keyword(s):  
Dynamic Optimization ◽  
Just In Time ◽  
Optimization Framework

Heterogeneous Differential Evolution with Memory Enhanced Brownian and Quantum Individuals for Dynamic Optimization Problems

2017 ◽  
Vol 32 (02) ◽  
pp. 1859003 ◽  
Author(s):  
Weizu Wu ◽  
Dongqing Xie ◽  
Liqun Liu
Keyword(s):  
Differential Evolution ◽  
Dynamic Optimization ◽  
Environmental Changes ◽  
Optimization Problems ◽  
Continuous Process ◽  
Evolutionary Information ◽  
Optimization Framework ◽  
Optimum Solution ◽  
Different Characteristics ◽  
With Memory

In order to solve the dynamic optimization problem (DOP), this paper proposes to use a heterogeneous differential evolution (HDE) algorithm framework with memory enhanced Brownian and quantum (MEBQ) individual scheme. The proposed HDE/MEBQ algorithm has the following two advantages when solving DOP. First and foremost, the HDE optimization framework can satisfy the problem requirement of different characteristics. DOP is actually a continuous process to solve different kinds of optimization problems to meet new requirements when the search environmental change occurs. Therefore, HDE/MEBQ is able to fastly respond to the environmental changes of DOP as the HDE framework uses multiple populations with heterogeneous parameters and operators to meet different search requirements in various search environments. Secondly, according to the phenomenon that most of the environmental changes may not be too drastic in real-world applications, historical information in the past may be useful for finding the optimum solution in the new environment. Thus, the MEBQ scheme used in HDE/MEBQ provides helpful historical evolutionary information from the elite ancestors for guiding individuals to evolve in a new environment strongly and to obtain faster convergence and a more precise solution. We evaluated HDE/MEBQ on several DOPs from CEC 2009 and compared with several state-of-the-art dynamic evolutionary algorithms. The results show that HDE/MEBQ performs superior in statistics and gets very competitive results in most of the test conditions, especially in complex DOPs.

A Dynamic Optimization Framework for OpenMP

2011 ◽  
pp. 54-68 ◽  
Author(s):  
Besar Wicaksono ◽  
Ramachandra C. Nanjegowda ◽  
Barbara Chapman
Keyword(s):  
Dynamic Optimization ◽  
Optimization Framework
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