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An Efficient Augmented Lagrangian Method for Support Vector Machine [article]

Yinqiao Yan, Qingna Li
<span title="2020-03-06">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
solved via the quadratically convergent semismooth Newton's method.  ...  By tackling the nonsmooth term in the model with Moreau-Yosida regularization and the proximal operator, the subproblem in augmented Lagrangian method reduces to a nonsmooth linear system, which can be  ...  Chao Ding from Chinese Academy of Sciences for providing the important reference of Prof. Jie Sun's PhD thesis. We would also like to thank Dr. Xudong Li from Fudan University for helpful discussions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.06800v2">arXiv:1912.06800v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jmurqnicvndnpbpmow6qfjbxua">fatcat:jmurqnicvndnpbpmow6qfjbxua</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200327053151/https://arxiv.org/pdf/1912.06800v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.06800v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Generalized Damped Newton Algorithms in Nonsmooth Optimization via Second-Order Subdifferentials [article]

Pham Duy Khanh, Boris Mordukhovich, Vo Thanh Phat, Dat Ba Tran
<span title="2022-01-18">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
First we develop a globally superlinearly convergent damped Newton-type algorithm for the class of continuously differentiable functions with Lipschitzian gradients, which are nonsmooth of second order  ...  The paper proposes and develops new globally convergent algorithms of the generalized damped Newton type for solving important classes of nonsmooth optimization problems.  ...  Our thanks also go to Alexey Izmailov for his useful remarks on the algorithm developed in Section 3 and to Michal Kočvara and Defeng Sun for helpful discussions on numerical experiments to solve Lasso  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.10555v3">arXiv:2101.10555v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7l5362tpwnhd3awoe5f2zujiye">fatcat:7l5362tpwnhd3awoe5f2zujiye</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220124053257/https://arxiv.org/pdf/2101.10555v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/26/62/266201abaf06f92aa3d84afb5e91b2ef399a9455.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.10555v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems

Xudong Li, Defeng Sun, Kim-Chuan Toh
<span title="">2018</span> <i title="Society for Industrial &amp; Applied Mathematics (SIAM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q3xjdl4ok5hbzabogkpxxajhfy" style="color: black;">SIAM Journal on Optimization</a> </i> &nbsp;
We develop a fast and robust algorithm for solving large scale convex composite optimization models with an emphasis on the 1 -regularized least squares regression (Lasso) problems.  ...  through the semismooth Newton method, we are able to propose an algorithm, called Ssnal, to efficiently solve the aforementioned difficult problems.  ...  Ying Cui at National University of Singapore and Dr. Chao Ding at Chinese Academy of Sciences for numerous discussions on the error bound conditions and the metric subregularity.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/16m1097572">doi:10.1137/16m1097572</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/is4qdjpvpngh3mebh3oq5cm424">fatcat:is4qdjpvpngh3mebh3oq5cm424</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190305000050/http://pdfs.semanticscholar.org/cc4e/063ba41a6d92014a6bdd682da32da8cd71f5.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cc/4e/cc4e063ba41a6d92014a6bdd682da32da8cd71f5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1137/16m1097572"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems [article]

Xudong Li, Defeng Sun, Kim-Chuan Toh
<span title="2017-05-03">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We develop a fast and robust algorithm for solving large scale convex composite optimization models with an emphasis on the ℓ_1-regularized least squares regression (Lasso) problems.  ...  through the semismooth Newton method, we are able to propose an algorithm, called Ssnal, to efficiently solve the aforementioned difficult problems.  ...  Ying Cui at National University of Singapore and Dr. Chao Ding at Chinese Academy of Sciences for numerous discussions on the error bound conditions and the metric subregularity.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1607.05428v3">arXiv:1607.05428v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5qiersdtarcjpg5jznjse6piau">fatcat:5qiersdtarcjpg5jznjse6piau</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200903051525/https://arxiv.org/pdf/1607.05428v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/23/81/2381eb981901c2af6456dc78c97b97da8f0306f4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1607.05428v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Forward-backward truncated Newton methods for convex composite optimization [article]

Panagiotis Patrinos, Lorenzo Stella, Alberto Bemporad
<span title="2014-02-28">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper proposes two proximal Newton-CG methods for convex nonsmooth optimization problems in composite form.  ...  Furthermore, they are computationally attractive since each Newton iteration requires the approximate solution of a linear system of usually small dimension.  ...  This is the case for example of Newton methods based on a trust-region approach, as well as quasi-Newton methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1402.6655v2">arXiv:1402.6655v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nnhllrmcavbvnhvmdspqyvu4dq">fatcat:nnhllrmcavbvnhvmdspqyvu4dq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200902212425/https://arxiv.org/pdf/1402.6655v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/22/79/2279fc94f2adfcd04b44e04c2b193243550125b3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1402.6655v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

MARS: A second-order reduction algorithm for high-dimensional sparse precision matrices estimation [article]

Qian LI, Binyan Jiang, Defeng Sun
<span title="2021-06-25">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Moreover, for the sub-problem in each iteration, other than solving the primal problem directly, we develop a semismooth Newton augmented Lagrangian algorithm with global linear convergence on the dual  ...  As a result, our algorithm is capable of handling datasets with very high dimensions that may go beyond the capacity of the existing methods.  ...  An inexact alternating direction method of multipliers for convex composite conic programming with nonlinear constraints.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.13508v1">arXiv:2106.13508v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fs36mmka4rew5lgvsbq4vfcvoq">fatcat:fs36mmka4rew5lgvsbq4vfcvoq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210630070059/https://arxiv.org/pdf/2106.13508v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/11/9d/119d5e9312ca396a0b5efda425094a1885eccb27.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.13508v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

An adaptive proximal point algorithm framework and application to large-scale optimization [article]

Meng Lu, Zheng Qu
<span title="2021-02-25">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We investigate the proximal point algorithm (PPA) and its inexact extensions under an error bound condition, which guarantees a global linear convergence if the proximal regularization parameter is larger  ...  We illustrate the performance of AGPPA by applying it to solve large-scale linear programming (LP) problem.  ...  Acknowledgement The computations were performed using research computing facilities offered by Information Technology Services, the University of Hong Kong.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.08784v2">arXiv:2008.08784v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vya6bijnhvfkraogidrbejfz2y">fatcat:vya6bijnhvfkraogidrbejfz2y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210306060511/https://arxiv.org/pdf/2008.08784v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/6a/836ab026d50a1e0a42d2bf689886ac38d55e5d40.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.08784v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Proximal Gradient Algorithms: Applications in Signal Processing [article]

Niccolò Antonello, Lorenzo Stella, Panagiotis Patrinos, Toon van Waterschoot
<span title="2020-01-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper focuses on the recent enhanced variants of the proximal gradient numerical optimization algorithm, which combine quasi-Newton methods with forward-adjoint oracles to tackle large-scale problems  ...  These proximal gradient algorithms are here described in an easy-to-understand way, illustrating how they are able to address a wide variety of problems arising in signal processing.  ...  These offer the possibility of using quasi-Newton methods reaching solutions of high accuracy with a speed that was previously beyond the reach of most first-order methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1803.01621v4">arXiv:1803.01621v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aybrk3icwrcxpnvle3eaxmwgw4">fatcat:aybrk3icwrcxpnvle3eaxmwgw4</a> </span>
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A Decomposition Augmented Lagrangian Method for Low-rank Semidefinite Programming [article]

Yifei Wang, Kangkang Deng, Haoyang Liu, Zaiwen Wen
<span title="2021-09-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The nonsmooth regularization and other general linear constraints are handled by the augmented Lagrangian method. Therefore, each subproblem can be solved by a semismooth Newton method on a manifold.  ...  We develop a decomposition method based on the augmented Lagrangian framework to solve a broad family of semidefinite programming problems possibly with nonlinear objective functions, nonsmooth regularization  ...  A complexity analysis of the semismooth Newton method is established for the factorized problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.11707v1">arXiv:2109.11707v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/etusql7i3ve4llpuhb27dpywhy">fatcat:etusql7i3ve4llpuhb27dpywhy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210928082206/https://arxiv.org/pdf/2109.11707v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3e/64/3e647a55f82c94338426719026e3a87694f3e83e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.11707v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

14th International Symposium on Mathematical Programming

<span title="">1990</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/o653fqsowvcyrhw6p4gdmpdkwm" style="color: black;">Mathematical programming</a> </i> &nbsp;
It is shown that for the success of the variant dom must ful ll a regularity property and that the choice of the normal vectors must meet some demands.Both requirements are ful lled if dom is polyhedral  ...  A v ariation of bundle methods is presented by means of which such a t ype of problems can be solved.In this variation a dynamic polyhedral model of dom is generated by means of normal vectors.In addition  ...  Keywords: Newton method -convex quadratic splines -regularized Newton direction Our main objective i s t o d e v elop a Newton method that nds a minimizer of a convex quadratic spline fx in nitely many  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bf01580875">doi:10.1007/bf01580875</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3jtclwmntzgjxkqs5uecombdaa">fatcat:3jtclwmntzgjxkqs5uecombdaa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20030716130717/http://rosowww.epfl.ch:80/ismp97/prog.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/bf01580875"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate [article]

Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
<span title="2021-11-12">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we prove that a variant of IRLS converges with a global linear rate to a sparse solution, i.e., with a linear error decrease occurring immediately from any initialization, if the measurements  ...  Iteratively Reweighted Least Squares (IRLS) is a widely used algorithm for this purpose due its excellent numerical performance.  ...  Acknowledgments and Disclosure of Funding The authors want to thank Massimo Fornasier for inspiring discussions and the anonymous reviewers for their detailed comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.12250v3">arXiv:2012.12250v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h2q4zi6r5rasvaymi55hoz4jju">fatcat:h2q4zi6r5rasvaymi55hoz4jju</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211123030852/https://arxiv.org/pdf/2012.12250v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5e/9e/5e9ef603612b516796fa4e2513058564c771a716.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.12250v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization [article]

Heng Yang, Luca Carlone
<span title="2022-05-29">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
thousands of constraints to high accuracy; (iii) STRIDE safeguards existing fast heuristics for robust estimation (e.g., RANSAC or Graduated Non-Convexity), i.e., it certifies global optimality if the  ...  We propose the first general and scalable framework to design certifiable algorithms for robust geometric perception in the presence of outliers.  ...  Lasserre for the discussion about Lasserre's hierarchy and TSSOS; Ling Liang and Kim-Chuan Toh for the discussion about SDP solvers; Bo Chen and Tat-Jun Chin for the SPEED data; and Jingnan Shi for the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.03349v2">arXiv:2109.03349v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2pmnk3cu2jffndhb75ynmatwiy">fatcat:2pmnk3cu2jffndhb75ynmatwiy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220605155728/https://arxiv.org/pdf/2109.03349v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0a/bf/0abfc8a68b6e4e0e1b4fdc2a64d705b8b4fd2acc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.03349v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

High-dimensional nonsmooth convex optimization via optimal subgradient methods

Masoud Ahookhosh
<span title="">2015</span> <span class="release-stage">unpublished</span>
Among first-order methods, the subgradient methods have a simple form and can deal with general convex optimization (without considering the structure of problems, in contrast to proximal-based and Nesterov-type  ...  In this thesis, motivated by the need for fast and reliable methods to solve general nonsmooth convex optimization, we develop some subgradient methods obtaining the optimal complexity of first-order methods  ...  The problems (6.29) and (6.31), such as Example 6.1.6, can be solved by the semismooth Newton method or the smoothing Newton method [142] , the quasi-Newton methods [150, 108] , the secant method [140  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.25365/thesis.39947">doi:10.25365/thesis.39947</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l3zv3dyo4nerfirasvaqwsa5am">fatcat:l3zv3dyo4nerfirasvaqwsa5am</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200221050245/http://othes.univie.ac.at/39947/1/2015-06-15_1149808.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/03/8c/038c8877b6fc40639e7c1a87f4fa87b4039a962f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.25365/thesis.39947"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

2-D blind deconvolution by partitioning into coupled 1-D problems using discrete Radon transforms

H. Ahn, A.E. Yagle
<i title="IEEE Comput. Soc. Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/anlh4tvwprcrtoxv5d4h6a7rye" style="color: black;">Proceedings., International Conference on Image Processing</a> </i> &nbsp;
We present results among others for the registration of MR images to a brain atlas.  ...  It proposes a discrete formulation of the Chan-Vese segmentation model for vector valued images.  ...  Jerome Darbon Mathematics UCLA jerome@math.ucla.edu MS9 Semismooth Newton and Active Set Methods for Sparse Reconstruction The problem of sparse reconstruction by Basis Pursuit is equivalent to regularization  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icip.1995.537409">doi:10.1109/icip.1995.537409</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icip/AhnY95.html">dblp:conf/icip/AhnY95</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ecos74jfmfgonkuk5gsq2essp4">fatcat:ecos74jfmfgonkuk5gsq2essp4</a> </span>
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Analysis and optimisation of a variational model for mixed Gaussian and Salt & Pepper noise removal [article]

Luca Calatron, Kostas Papafitsoros, University, My, University, My
<span title="2019-06-28">2019</span>
Using these theoretical results, we then analytically study a bilevel optimisation strategy for automatically selecting the parameters of the model by means of a training set.  ...  The data discrepancy term of the model combines L1 and L2 terms in an infimal convolution fashion and it is appropriate for the joint removal of Gaussian and Salt & Pepper noise.  ...  Both authors would like to thank the Isaac Newton Institute for Mathematical Sciences for support and hospitality during the program "Variational Methods and Effective Algorithms for Imaging and Vision  ... 
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