Stochastic First-Order Minimization Techniques Using Jensen Surrogates for X-Ray Transmission Tomography

Joseph O'Sullivan; Soysal Degirmenci; David G. Politte

Stochastic First-Order Minimization Techniques Using Jensen Surrogates for X-Ray Transmission Tomography

Joseph O'Sullivan, Soysal Degirmenci, David G. Politte

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	arXiv
State	Published - Oct 26 2016

Disciplines

Engineering
Electrical and Computer Engineering

Cite this

@article{bc08479a8db142abbbe7de4f87cbc9b3,

title = "Stochastic First-Order Minimization Techniques Using Jensen Surrogates for X-Ray Transmission Tomography",

author = "Joseph O'Sullivan and Soysal Degirmenci and Politte, \{David G.\}",

note = "Image reconstruction in X-ray transmission tomography has been an important research field for decades. In light of data volume increasing faster than processor speeds, one needs accelerated iterative algorithms to solve the optimization problem in the X-ray CT application.",

year = "2016",

month = oct,

day = "26",

language = "American English",

journal = "arXiv",

}

TY - JOUR

T1 - Stochastic First-Order Minimization Techniques Using Jensen Surrogates for X-Ray Transmission Tomography

AU - O'Sullivan, Joseph

AU - Degirmenci, Soysal

AU - Politte, David G.

N1 - Image reconstruction in X-ray transmission tomography has been an important research field for decades. In light of data volume increasing faster than processor speeds, one needs accelerated iterative algorithms to solve the optimization problem in the X-ray CT application.

PY - 2016/10/26

Y1 - 2016/10/26

UR - https://arxiv.org/abs/1610.08568

M3 - Article

JO - arXiv

JF - arXiv

ER -

Stochastic First-Order Minimization Techniques Using Jensen Surrogates for X-Ray Transmission Tomography

Disciplines

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