Richard J Clancy

Richard J Clancy
University of Colorado Boulder | CUB · Department of Applied Mathematics (College of Engineering)

About

8
Publications
513
Reads
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12
Citations
Citations since 2016
8 Research Items
12 Citations
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20162017201820192020202120220123456
Introduction

Publications

Publications (8)
Preprint
We present an algorithm to perform trust-region-based optimization for nonlinear unconstrained problems. The method selectively uses function and gradient evaluations at different floating-point precisions to reduce the overall energy consumption, storage, and communication costs; these capabilities are increasingly important in the era of exascale...
Chapter
We present an algorithm to perform trust-region-based optimization for nonlinear unconstrained problems. The method selectively uses function and gradient evaluations at different floating-point precisions to reduce the overall energy consumption, storage, and communication costs; these capabilities are increasingly important in the era of exascale...
Article
Full-text available
Scalar optically-pumped magnetometers (OPMs) are being developed in small packages with high sensitivities. The high common-mode rejection ratio of these sensors allows for detection of very small signals in the presence of large background fields making them ideally suited for brain imaging applications in unshielded environments. Despite a flurry...
Preprint
Full-text available
Scalar optically-pumped magnetometers (OPMs) are being developed in small packages with high sensitivities. The high common-mode rejection ratio of these sensors allows for detection of very small signals in the presence of large background fields making them ideally suited for brain imaging applications in unshielded environments. Despite a flurry...
Preprint
Full-text available
In this paper we consider regression problems subject to arbitrary noise in the operator or design matrix. This characterization appropriately models many physical phenomena with uncertainty in the regressors. Although the problem has been studied extensively for ordinary/total least squares, and via models that implicitly or explicitly assume Gaus...
Article
Full-text available
In this paper we formulate and solve a robust least squares problem for a system of linear equations subject to quantization error in the data matrix. Ordinary least squares fails to consider uncertainty in the operator, modeling all noise in the observed signal. Total least squares accounts for uncertainty in the data matrix, but necessarily incre...
Preprint
Full-text available
In this paper we formulate and solve a robust least squares problem for a system of linear equations subject to quantization error. Ordinary least squares fails to consider uncertainty in the data matrices, modeling all noise in the observed signal. Total least squares accounts for uncertainty in the data matrix, but necessarily increases the condi...

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