1,721,118 research outputs found
A Simple Multiresolution Technique for Diffraction Image Recovery
No full textWe describe a Fourier-based multiresolition technique to speed up
algorithms for recovering diffraction images from noisy and
aberrated data. We use this method to achieve a fold
speed up of an adaptive optics algorithm developed by the author
for an early prototype of the James Webb Space Telescope, due to replace
the Hubble Space Telescope in 2011. The technique,
simple and frequently rediscovered, is based on windowed Fourier transforms.
While a natural strategy for our purposes, the method is not
specific to our setting and can be employed in
any application that uses a combination of far field scattering
data and spatially dependent physical constraints
A Simple Multiresolution Technique for Diffraction Image Recovery
No full textWe describe a Fourier-based multiresolition technique to speed up
algorithms for recovering diffraction images from noisy and
aberrated data. We use this method to achieve a fold
speed up of an adaptive optics algorithm developed by the author
for an early prototype of the James Webb Space Telescope, due to replace
the Hubble Space Telescope in 2011. The technique,
simple and frequently rediscovered, is based on windowed Fourier transforms.
While a natural strategy for our purposes, the method is not
specific to our setting and can be employed in
any application that uses a combination of far field scattering
data and spatially dependent physical constraints
SAMSARA: reverse communication nonlinear optimization python package
No full textSamsara is a reverse communication nonlinear optimization solver for smooth unconstrained objectives. Samsara is just an oracle that suggests a step (direction and length) using previous information provided to it by the calling routine. It does not execute function evaluations or gradient calculations, but it does build a model of the function being optimized, based on the steps, gradients and function values (if available) passed to it by the user.
This repository contains the Python version of Samsara published in the python package index. To install from a shell command line type: pip install samsar
Online Appendices for Perceptions of Attitudinal Change: The End of History Illusion and Polarization.
No full textOnline Appendices for Perceptions of Attitudinal Change: The End of History Illusion and Polarization
Prox-Regularity of Rank Constraint Sets and Implications for Algorithms
No full textWe present an analysis of sets of matrices with rank less than or equal to a specified number s. We provide a simple formula for the normal cone to such sets, and use this to show that these sets are prox-regular at all points with rank exactly equal to s. The normal cone formula appears to be new. This allows for easy application of prior results guaranteeing local linear convergence of the fundamental alternating projection algorithm between sets, one of which is a rank constraint set. We apply this to show local linear convergence of another fundamental algorithm, approximate steepest descent. Our results apply not only to linear systems with rank constraints, as has been treated extensively in the literature, but also nonconvex systems with rank constraints
Data for class ``PtychographyExperiment" in the ProxToolbox
No full textData for class ``PtychographyExperiment" in the ProxToolbox. This should get automatically downloaded if you try to run one of the ptychography demos and do not already have this data locally. (2024-04-29
Replication Data for: "Research Note: The Influence of Institutional Trust and Conspiracy Ideation on COVID-19 Behaviors"
No full textReplication data includes cleaning do file, analysis R script, original data, and READ ME text file which provides further information
A toolbox of algorithms and projection operators for implementing fixed point iterations based on the Prox operator
No full text"E Unibus Fissiparousness' a review of David Foster Wallace's Everything and More: A Compact History of Infinity
No full textImage Synthesis for Inverse Obstacle Scattering Using the Eigenfunction Expansion Theorem
No full textIn recent years several new inverse scattering techniques have been developed that determine the boundary of an unknown obstacle by reconstructing the surrounding scattered field. In the case of sound soft obstacles, the boundary is usually found as the minimum contour of the total field. In this note we derive a different approach for imaging the boundary from the reconstructed fields based on a generalization of the eigenfunction expansion theorem. The aim of this alternative approach is the construction of higher contrast images than is currently obtained with the minimum contour approach
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