Optimal Sparse Projection Design for Systems with Treatment Cardinality Constraint
Abstract Number:
2129
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Kexin Xie (1), Ryan Lekivetz (2), Xinwei Deng (1)
Institutions:
(1) Virginia Tech, N/A, (2) JMP, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Modern experimental designs often face the so-called treatment cardinality constraint, which is the constraint on the number of included factors in each treatment. Experiments with such constraints are commonly encountered in engineering simulation, AI system tuning, and large-scale system verification. This calls for the development of adequate designs to enable statistical efficiency for modeling and analysis within feasible constraints. In this work, we propose an optimal sparse projection (OSP) design for systems with treatment cardinality constraints. We introduce a tailored optimal projection (TOP) criterion that ensures a good space-filling properties in subspaces and promotes orthogonality or near-orthogonality among factors. To construct the proposed OSP design, we develop an efficient construction algorithm based on orthogonal arrays and employ parallel-level permutation and expansion techniques to efficiently explore the design space with treatment cardinality constraints. Numerical examples demonstrate the merits of the proposed method.
Keywords:
Experimental designs|Space-filling design|Orthogonal arrays|Constraint space|Treatment constraint|
Sponsors:
Quality and Productivity Section
Tracks:
Design of Experiments
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