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1
Global Convergence of ADMM in Nonconvex Nonsmooth Optimization
Global Convergence of ADMM in Nonconvex Nonsmooth Optimization
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Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

Journal of scientific computing, 2019-01, Vol.78 (1), p.29-63 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2018 ;Springer Science+Business Media, LLC, part of Springer Nature 2018. ;ISSN: 0885-7474 ;EISSN: 1573-7691 ;DOI: 10.1007/s10915-018-0757-z

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2
Robust optimization
Robust optimization
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Robust optimization

2009 Princeton University Press ;ISBN: 9780691143682 ;ISBN: 9781400831050 ;ISBN: 0691143684 ;ISBN: 1400831059 ;EISBN: 1400831059 ;EISBN: 9781400831050 ;DOI: 10.1515/9781400831050 ;OCLC: 439040007 ;LCCallNum: QA402.5

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3
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
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Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing

Springer Science+Business Media, LLC 2010 ;ISBN: 1441970118 ;ISBN: 9781441970114 ;ISBN: 144197010X ;ISBN: 9781441970107 ;EISBN: 1441970118 ;EISBN: 9781441970114 ;DOI: 10.1007/978-1-4419-7011-4 ;OCLC: 667655080

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4
On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers
On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers
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On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers

Journal of scientific computing, 2016-03, Vol.66 (3), p.889-916 [Peer Reviewed Journal]

Springer Science+Business Media New York 2015 ;Springer Science+Business Media New York 2015. ;ISSN: 0885-7474 ;EISSN: 1573-7691 ;DOI: 10.1007/s10915-015-0048-x

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5
Phase recovery, MaxCut and complex semidefinite programming
Phase recovery, MaxCut and complex semidefinite programming
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Phase recovery, MaxCut and complex semidefinite programming

Mathematical programming, 2015-02, Vol.149 (1-2), p.47-81 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2013 ;Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2015 ;ISSN: 0025-5610 ;EISSN: 1436-4646 ;DOI: 10.1007/s10107-013-0738-9 ;CODEN: MHPGA4

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6
Towards a Mathematical Theory of Super-resolution
Towards a Mathematical Theory of Super-resolution
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Towards a Mathematical Theory of Super-resolution

Communications on pure and applied mathematics, 2014-06, Vol.67 (6), p.906-956 [Peer Reviewed Journal]

2014 Wiley Periodicals, Inc. ;ISSN: 0010-3640 ;EISSN: 1097-0312 ;DOI: 10.1002/cpa.21455 ;CODEN: CPMAMV

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7
A Globally Convergent Algorithm for Nonconvex Optimization Based on Block Coordinate Update
A Globally Convergent Algorithm for Nonconvex Optimization Based on Block Coordinate Update
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A Globally Convergent Algorithm for Nonconvex Optimization Based on Block Coordinate Update

Journal of scientific computing, 2017-08, Vol.72 (2), p.700-734 [Peer Reviewed Journal]

Springer Science+Business Media New York 2017 ;Springer Science+Business Media New York 2017. ;ISSN: 0885-7474 ;EISSN: 1573-7691 ;DOI: 10.1007/s10915-017-0376-0

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8
The Convex Geometry of Linear Inverse Problems
The Convex Geometry of Linear Inverse Problems
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The Convex Geometry of Linear Inverse Problems

Foundations of computational mathematics, 2012-12, Vol.12 (6), p.805-849 [Peer Reviewed Journal]

SFoCM 2012 ;COPYRIGHT 2012 Springer ;ISSN: 1615-3375 ;EISSN: 1615-3383 ;DOI: 10.1007/s10208-012-9135-7 ;CODEN: FCMOA3

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9
The unconstrained binary quadratic programming problem: a survey
The unconstrained binary quadratic programming problem: a survey
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The unconstrained binary quadratic programming problem: a survey

Journal of combinatorial optimization, 2014-07, Vol.28 (1), p.58-81 [Peer Reviewed Journal]

Springer Science+Business Media New York 2014 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1382-6905 ;EISSN: 1573-2886 ;DOI: 10.1007/s10878-014-9734-0

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10
A New Primal–Dual Algorithm for Minimizing the Sum of Three Functions with a Linear Operator
A New Primal–Dual Algorithm for Minimizing the Sum of Three Functions with a Linear Operator
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A New Primal–Dual Algorithm for Minimizing the Sum of Three Functions with a Linear Operator

Journal of scientific computing, 2018-09, Vol.76 (3), p.1698-1717 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2018 ;Springer Science+Business Media, LLC, part of Springer Nature 2018. ;ISSN: 0885-7474 ;EISSN: 1573-7691 ;DOI: 10.1007/s10915-018-0680-3

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11
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Journal of scientific computing, 2022-09, Vol.92 (3), p.88, Article 88 [Peer Reviewed Journal]

The Author(s) 2022 ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-7474 ;EISSN: 1573-7691 ;DOI: 10.1007/s10915-022-01939-z

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12
Proximal alternating linearized minimization for nonconvex and nonsmooth problems
Proximal alternating linearized minimization for nonconvex and nonsmooth problems
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Proximal alternating linearized minimization for nonconvex and nonsmooth problems

Mathematical programming, 2014-08, Vol.146 (1-2), p.459-494 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2013 ;Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2014 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0025-5610 ;EISSN: 1436-4646 ;DOI: 10.1007/s10107-013-0701-9 ;CODEN: MHPGA4

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13
Optimization algorithms on matrix manifolds
Optimization algorithms on matrix manifolds
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Optimization algorithms on matrix manifolds

2008 Princeton University Press ;ISBN: 9780691132983 ;ISBN: 0691132984 ;ISBN: 1400830249 ;ISBN: 9781400830244 ;EISBN: 1400830249 ;EISBN: 9781400830244 ;DOI: 10.1515/9781400830244 ;OCLC: 438732716 ;LCCallNum: QA402.5

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14
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution
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Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution

Foundations of computational mathematics, 2020-06, Vol.20 (3), p.451-632 [Peer Reviewed Journal]

The Author(s) 2019 ;ISSN: 1615-3375 ;EISSN: 1615-3383 ;DOI: 10.1007/s10208-019-09429-9

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15
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction
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Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction

Journal of scientific computing, 2010-10, Vol.45 (1-3), p.272-293 [Peer Reviewed Journal]

The Author(s) 2009 ;The Author(s) 2009. This work is published under http://creativecommons.org/licenses/by-nc/2.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-7474 ;EISSN: 1573-7691 ;DOI: 10.1007/s10915-009-9331-z

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16
Nonsmooth optimization via quasi-Newton methods
Nonsmooth optimization via quasi-Newton methods
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Nonsmooth optimization via quasi-Newton methods

Mathematical programming, 2013-10, Vol.141 (1-2), p.135-163 [Peer Reviewed Journal]

Springer and Mathematical Optimization Society 2012 ;Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2013 ;ISSN: 0025-5610 ;EISSN: 1436-4646 ;DOI: 10.1007/s10107-012-0514-2 ;CODEN: MHPGA4

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17
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
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Accelerated gradient methods for nonconvex nonlinear and stochastic programming

Mathematical programming, 2016-03, Vol.156 (1-2), p.59-99 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2015 ;Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2016 ;ISSN: 0025-5610 ;EISSN: 1436-4646 ;DOI: 10.1007/s10107-015-0871-8 ;CODEN: MHPGA4

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18
On the linear convergence of the alternating direction method of multipliers
On the linear convergence of the alternating direction method of multipliers
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On the linear convergence of the alternating direction method of multipliers

Mathematical programming, 2017-03, Vol.162 (1-2), p.165-199 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2016 ;Mathematical Programming is a copyright of Springer, 2017. ;ISSN: 0025-5610 ;EISSN: 1436-4646 ;DOI: 10.1007/s10107-016-1034-2 ;CODEN: MHPGA4

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19
Recent advances in trust region algorithms
Recent advances in trust region algorithms
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Recent advances in trust region algorithms

Mathematical programming, 2015-06, Vol.151 (1), p.249-281 [Peer Reviewed Journal]

Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2015 ;ISSN: 0025-5610 ;EISSN: 1436-4646 ;DOI: 10.1007/s10107-015-0893-2 ;CODEN: MHPGA4

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20
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding
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Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding

Journal of optimization theory and applications, 2016-06, Vol.169 (3), p.1042-1068 [Peer Reviewed Journal]

Springer Science+Business Media New York 2016 ;ISSN: 0022-3239 ;EISSN: 1573-2878 ;DOI: 10.1007/s10957-016-0892-3

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