67 research outputs found
Convergence Rate Analysis for Optimal Computing Budget Allocation Algorithms
Ordinal optimization (OO) is a widely-studied technique for optimizing
discrete-event dynamic systems (DEDS). It evaluates the performance of the
system designs in a finite set by sampling and aims to correctly make ordinal
comparison of the designs. A well-known method in OO is the optimal computing
budget allocation (OCBA). It builds the optimality conditions for the number of
samples allocated to each design, and the sample allocation that satisfies the
optimality conditions is shown to asymptotically maximize the probability of
correct selection for the best design. In this paper, we investigate two
popular OCBA algorithms. With known variances for samples of each design, we
characterize their convergence rates with respect to different performance
measures. We first demonstrate that the two OCBA algorithms achieve the optimal
convergence rate under measures of probability of correct selection and
expected opportunity cost. It fills the void of convergence analysis for OCBA
algorithms. Next, we extend our analysis to the measure of cumulative regret, a
main measure studied in the field of machine learning. We show that with minor
modification, the two OCBA algorithms can reach the optimal convergence rate
under cumulative regret. It indicates the potential of broader use of
algorithms designed based on the OCBA optimality conditions
Convergence Analysis of Stochastic Kriging-Assisted Simulation with Random Covariates
We consider performing simulation experiments in the presence of covariates.
Here, covariates refer to some input information other than system designs to
the simulation model that can also affect the system performance. To make
decisions, decision makers need to know the covariate values of the problem.
Traditionally in simulation-based decision making, simulation samples are
collected after the covariate values are known; in contrast, as a new
framework, simulation with covariates starts the simulation before the
covariate values are revealed, and collects samples on covariate values that
might appear later. Then, when the covariate values are revealed, the collected
simulation samples are directly used to predict the desired results. This
framework significantly reduces the decision time compared to the traditional
way of simulation. In this paper, we follow this framework and suppose there
are a finite number of system designs. We adopt the metamodel of stochastic
kriging (SK) and use it to predict the system performance of each design and
the best design. The goal is to study how fast the prediction errors diminish
with the number of covariate points sampled. This is a fundamental problem in
simulation with covariates and helps quantify the relationship between the
offline simulation efforts and the online prediction accuracy. Particularly, we
adopt measures of the maximal integrated mean squared error (IMSE) and
integrated probability of false selection (IPFS) for assessing errors of the
system performance and the best design predictions. Then, we establish
convergence rates for the two measures under mild conditions. Last, these
convergence behaviors are illustrated numerically using test examples
Asymptotic Optimality of Myopic Ranking and Selection Procedures
Ranking and selection (R&S) is a popular model for studying discrete-event
dynamic systems. It aims to select the best design (the design with the largest
mean performance) from a finite set, where the mean of each design is unknown
and has to be learned by samples. Great research efforts have been devoted to
this problem in the literature for developing procedures with superior
empirical performance and showing their optimality. In these efforts, myopic
procedures were popular. They select the best design using a 'naive' mechanism
of iteratively and myopically improving an approximation of the objective
measure. Although they are based on simple heuristics and lack theoretical
support, they turned out highly effective, and often achieved competitive
empirical performance compared to procedures that were proposed later and shown
to be asymptotically optimal. In this paper, we theoretically analyze these
myopic procedures and prove that they also satisfy the optimality conditions of
R&S, just like some other popular R&S methods. It explains the good performance
of myopic procedures in various numerical tests, and provides good insight into
the structure and theoretical development of efficient R&S procedures
Improving the Knowledge Gradient Algorithm
The knowledge gradient (KG) algorithm is a popular policy for the best arm
identification (BAI) problem. It is built on the simple idea of always choosing
the measurement that yields the greatest expected one-step improvement in the
estimate of the best mean of the arms. In this research, we show that this
policy has limitations, causing the algorithm not asymptotically optimal. We
next provide a remedy for it, by following the manner of one-step look ahead of
KG, but instead choosing the measurement that yields the greatest one-step
improvement in the probability of selecting the best arm. The new policy is
called improved knowledge gradient (iKG). iKG can be shown to be asymptotically
optimal. In addition, we show that compared to KG, it is easier to extend iKG
to variant problems of BAI, with the -good arm identification and
feasible arm identification as two examples. The superior performances of iKG
on these problems are further demonstrated using numerical examples.Comment: 32 pages, 42 figure
AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations
Multi-task learning (MTL) aims at enhancing the performance and efficiency of
machine learning models by training them on multiple tasks simultaneously.
However, MTL research faces two challenges: 1) modeling the relationships
between tasks to effectively share knowledge between them, and 2) jointly
learning task-specific and shared knowledge. In this paper, we present a novel
model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges.
AdaTT is a deep fusion network built with task specific and optional shared
fusion units at multiple levels. By leveraging a residual mechanism and gating
mechanism for task-to-task fusion, these units adaptively learn shared
knowledge and task specific knowledge. To evaluate the performance of AdaTT, we
conduct experiments on a public benchmark and an industrial recommendation
dataset using various task groups. Results demonstrate AdaTT can significantly
outperform existing state-of-the-art baselines
Research on the Design Method of a Bionic Suspension Workpiece Based on the Wing Structure of an Albatross
An air suspension platform uses air pressure to realize the suspension function during the suspension process, and it has the disadvantage of large air pressure and a small suspension force. In this study, an air suspension platform was built using bionic design to reduce the required air pressure and increase the suspension force. A suspension structure mapping model was established according to the physiological structure characteristics of albatross wings. A bionic model was established by using the theoretical calculation formula and structural size parameters of the structural design. A 3D printer was used to manufacture the physical prototype of the suspended workpiece. Based on this, a suspension test rig was built. Six sets of contrast experiments were designed. The experimental results of the suspension test bench were compared with the theoretical calculation results. The results show that the buoyancy of the suspended workpiece with a V-shaped surface at a 15-degree attack angle was optimal for the same air pressure as the other workpieces. The surface structure of the suspended workpiece was applied to the air static pressure guide rail. By comparing the experimental data, the air pressure of the original air suspension guide rail was reduced by 37%, and the validity of the theory and design method was verified
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