Solving Chance-Constrained Optimization Under Nonparametric Uncertainty Through Hilbert Space Embedding

Solving Chance-Constrained Optimization Under Nonparametric Uncertainty Through Hilbert Space Embedding

In this article, we present an efficient algorithm for solving a class of chance-constrained optimization under nonparametric uncertainty. Our algorithm is built on the possibility of representing arbitrary distributions as functions in Reproducing Kernel Hilbert Space (RKHS). We use this foundation...

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Bibliographic Details
Main Authors: Gopalakrishnan, B. (Author), Krishna, K.M (Author), Manocha, D. (Author), Singh, A.K (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
Algorithm for solving
Arbitrary distribution
Chance constraint
Chance constraints
Chance-constrained optimizations
Constrained optimization
Constraint functions
Hilbert spaces
Inverse problems
Kernel trick
Manipulators
Nonparametric uncertainties
nonparametric uncertainty
Reproducing Kernel Hilbert spaces
robust optimal control
Robust-optimal controls
Space embedding
Uncertainty analysis
Vector spaces
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