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You searched for `subject:(Non convex optimization)`

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Showing records 1 – 30 of
62 total matches.

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1.
Uthayakumar, R.
Study on convergence of *optimization* problems;.

Degree: 2014, INFLIBNET

URL: http://shodhganga.inflibnet.ac.in/handle/10603/17964

►

In this thesis, various notions of convergence of sequence of sets and functions and their applications in the convergence of the optimal values under the… (more)

Subjects/Keywords: Convergence; Convex; Functions; Non-convex; Optimization; Sets

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Uthayakumar, R. (2014). Study on convergence of optimization problems;. (Thesis). INFLIBNET. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/17964

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Uthayakumar, R. “Study on convergence of optimization problems;.” 2014. Thesis, INFLIBNET. Accessed April 21, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/17964.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Uthayakumar, R. “Study on convergence of optimization problems;.” 2014. Web. 21 Apr 2019.

Vancouver:

Uthayakumar R. Study on convergence of optimization problems;. [Internet] [Thesis]. INFLIBNET; 2014. [cited 2019 Apr 21]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/17964.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Uthayakumar R. Study on convergence of optimization problems;. [Thesis]. INFLIBNET; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/17964

Not specified: Masters Thesis or Doctoral Dissertation

Princeton University

2.
Ma, Tengyu.
* Non*-

Degree: PhD, 2017, Princeton University

URL: http://arks.princeton.edu/ark:/88435/dsp01th83m199d

► *Non*-*convex* *optimization* is ubiquitous in modern machine learning: recent breakthroughs in deep learning require optimizing *non*-*convex* training objective functions; problems that admit accurate *convex* relaxation…
(more)

Subjects/Keywords: machine learning; non-convex optimization

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APA (6^{th} Edition):

Ma, T. (2017). Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01th83m199d

Chicago Manual of Style (16^{th} Edition):

Ma, Tengyu. “Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding .” 2017. Doctoral Dissertation, Princeton University. Accessed April 21, 2019. http://arks.princeton.edu/ark:/88435/dsp01th83m199d.

MLA Handbook (7^{th} Edition):

Ma, Tengyu. “Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding .” 2017. Web. 21 Apr 2019.

Vancouver:

Ma T. Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Apr 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp01th83m199d.

Council of Science Editors:

Ma T. Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp01th83m199d

University of Texas – Austin

3.
Park, Dohyung.
Efficient *non*-*convex* algorithms for large-scale learning problems.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/46581

► The emergence of modern large-scale datasets has led to a huge interest in the problem of learning hidden complex structures. Not only can models from…
(more)

Subjects/Keywords: Machine learning; Non-convex optimization

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APA (6^{th} Edition):

Park, D. (2016). Efficient non-convex algorithms for large-scale learning problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46581

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Park, Dohyung. “Efficient non-convex algorithms for large-scale learning problems.” 2016. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/46581.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Park, Dohyung. “Efficient non-convex algorithms for large-scale learning problems.” 2016. Web. 21 Apr 2019.

Vancouver:

Park D. Efficient non-convex algorithms for large-scale learning problems. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/46581.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Park D. Efficient non-convex algorithms for large-scale learning problems. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46581

Not specified: Masters Thesis or Doctoral Dissertation

Carnegie Mellon University

4. Xiong, Xuehan. Supervised Descent Method.

Degree: 2015, Carnegie Mellon University

URL: http://repository.cmu.edu/dissertations/652

► In this dissertation, we focus on solving Nonlinear Least Squares problems using a supervised approach. In particular, we developed a Supervised Descent Method (SDM), performed…
(more)

Subjects/Keywords: nonlinear optimization; global optimization; non-convex optimization; nonlinear least squares; face alignment; facial feature tracking

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APA (6^{th} Edition):

Xiong, X. (2015). Supervised Descent Method. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/652

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Xiong, Xuehan. “Supervised Descent Method.” 2015. Thesis, Carnegie Mellon University. Accessed April 21, 2019. http://repository.cmu.edu/dissertations/652.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Xiong, Xuehan. “Supervised Descent Method.” 2015. Web. 21 Apr 2019.

Vancouver:

Xiong X. Supervised Descent Method. [Internet] [Thesis]. Carnegie Mellon University; 2015. [cited 2019 Apr 21]. Available from: http://repository.cmu.edu/dissertations/652.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xiong X. Supervised Descent Method. [Thesis]. Carnegie Mellon University; 2015. Available from: http://repository.cmu.edu/dissertations/652

Not specified: Masters Thesis or Doctoral Dissertation

UCLA

5.
Siegel, Jonathan.
Accelerated First-Order *Optimization* with Orthogonality Constraints.

Degree: Mathematics, 2018, UCLA

URL: http://www.escholarship.org/uc/item/1457756r

► *Optimization* problems with orthogonality constraints have many applications in science and engineering.In these applications, one often deals with large-scale problems which are ill-conditioned near the…
(more)

Subjects/Keywords: Mathematics; Applied mathematics; Compressed Modes; Non-convex Optimization; Scientific Computing

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APA (6^{th} Edition):

Siegel, J. (2018). Accelerated First-Order Optimization with Orthogonality Constraints. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/1457756r

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Siegel, Jonathan. “Accelerated First-Order Optimization with Orthogonality Constraints.” 2018. Thesis, UCLA. Accessed April 21, 2019. http://www.escholarship.org/uc/item/1457756r.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Siegel, Jonathan. “Accelerated First-Order Optimization with Orthogonality Constraints.” 2018. Web. 21 Apr 2019.

Vancouver:

Siegel J. Accelerated First-Order Optimization with Orthogonality Constraints. [Internet] [Thesis]. UCLA; 2018. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/1457756r.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Siegel J. Accelerated First-Order Optimization with Orthogonality Constraints. [Thesis]. UCLA; 2018. Available from: http://www.escholarship.org/uc/item/1457756r

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

6. Asiaeetaheri, Amir. High Dimensional Learning with Structure Inducing Constraints and Regularizers.

Degree: PhD, Computer Science, 2017, University of Minnesota

URL: http://hdl.handle.net/11299/191407

► Explosive growth in data generation through science and technology calls for new computational and analytical tools. To the statistical machine learning community, one major challenge…
(more)

Subjects/Keywords: Convex Optimization; High Dimensional Learning; Influence Maximization; Non-asymptotic Error Bound

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Asiaeetaheri, A. (2017). High Dimensional Learning with Structure Inducing Constraints and Regularizers. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/191407

Chicago Manual of Style (16^{th} Edition):

Asiaeetaheri, Amir. “High Dimensional Learning with Structure Inducing Constraints and Regularizers.” 2017. Doctoral Dissertation, University of Minnesota. Accessed April 21, 2019. http://hdl.handle.net/11299/191407.

MLA Handbook (7^{th} Edition):

Asiaeetaheri, Amir. “High Dimensional Learning with Structure Inducing Constraints and Regularizers.” 2017. Web. 21 Apr 2019.

Vancouver:

Asiaeetaheri A. High Dimensional Learning with Structure Inducing Constraints and Regularizers. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11299/191407.

Council of Science Editors:

Asiaeetaheri A. High Dimensional Learning with Structure Inducing Constraints and Regularizers. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/191407

University of Minnesota

7.
Kadkhodaie Elyaderani, Mojtaba.
A Computational and Statistical Study of *Convex* and Nonconvex *Optimization* with Applications to Structured Source Demixing and Matrix Factorization Problems.

Degree: PhD, Electrical/Computer Engineering, 2017, University of Minnesota

URL: http://hdl.handle.net/11299/191334

► Modern machine learning problems that emerge from real-world applications typically involve estimating high dimensional model parameters, whose number may be of the same order as…
(more)

Subjects/Keywords: Alternating Direction Method of Multipliers; Convex Optimization; Group Lasso; Local Convergence Analysis; Low-rank Matrix Factorization; Non-Convex Optimization

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APA (6^{th} Edition):

Kadkhodaie Elyaderani, M. (2017). A Computational and Statistical Study of Convex and Nonconvex Optimization with Applications to Structured Source Demixing and Matrix Factorization Problems. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/191334

Chicago Manual of Style (16^{th} Edition):

Kadkhodaie Elyaderani, Mojtaba. “A Computational and Statistical Study of Convex and Nonconvex Optimization with Applications to Structured Source Demixing and Matrix Factorization Problems.” 2017. Doctoral Dissertation, University of Minnesota. Accessed April 21, 2019. http://hdl.handle.net/11299/191334.

MLA Handbook (7^{th} Edition):

Kadkhodaie Elyaderani, Mojtaba. “A Computational and Statistical Study of Convex and Nonconvex Optimization with Applications to Structured Source Demixing and Matrix Factorization Problems.” 2017. Web. 21 Apr 2019.

Vancouver:

Kadkhodaie Elyaderani M. A Computational and Statistical Study of Convex and Nonconvex Optimization with Applications to Structured Source Demixing and Matrix Factorization Problems. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11299/191334.

Council of Science Editors:

Kadkhodaie Elyaderani M. A Computational and Statistical Study of Convex and Nonconvex Optimization with Applications to Structured Source Demixing and Matrix Factorization Problems. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/191334

University of Texas – Austin

8. Bhojanapalli, Venkata Sesha Pavana Srinadh. Large scale matrix factorization with guarantees: sampling and bi-linearity.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/32832

► Low rank matrix factorization is an important step in many high dimensional machine learning algorithms. Traditional algorithms for factorization do not scale well with the…
(more)

Subjects/Keywords: Matrix completion; Non-convex optimization; Low rank approximation; Semi-definite optimization; Tensor factorization; Scalable algorithms

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APA (6^{th} Edition):

Bhojanapalli, V. S. P. S. (2015). Large scale matrix factorization with guarantees: sampling and bi-linearity. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32832

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/32832.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Web. 21 Apr 2019.

Vancouver:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/32832.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32832

Not specified: Masters Thesis or Doctoral Dissertation

Universitat de Valencia

9.
Huang, Xiaoge.
* Non*-

Degree: 2013, Universitat de Valencia

URL: http://hdl.handle.net/10550/29185

► In this thesis, we explore interweave communication systems in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio…
(more)

Subjects/Keywords: Quasi-Nash Equilibrium; Non-cooperative Game; Non-convex Optimization; Cognitive Radio Networks

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APA (6^{th} Edition):

Huang, X. (2013). Non-convex distributed power allocation games in cognitive radio networks . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/29185

Chicago Manual of Style (16^{th} Edition):

Huang, Xiaoge. “Non-convex distributed power allocation games in cognitive radio networks .” 2013. Doctoral Dissertation, Universitat de Valencia. Accessed April 21, 2019. http://hdl.handle.net/10550/29185.

MLA Handbook (7^{th} Edition):

Huang, Xiaoge. “Non-convex distributed power allocation games in cognitive radio networks .” 2013. Web. 21 Apr 2019.

Vancouver:

Huang X. Non-convex distributed power allocation games in cognitive radio networks . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2013. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10550/29185.

Council of Science Editors:

Huang X. Non-convex distributed power allocation games in cognitive radio networks . [Doctoral Dissertation]. Universitat de Valencia; 2013. Available from: http://hdl.handle.net/10550/29185

Australian National University

10.
Deng, Huizhong.
Shape Clustering and Spatial-temporal Constraint for *Non*-rigid Structure from Motion
.

Degree: 2017, Australian National University

URL: http://hdl.handle.net/1885/113634

► *Non*-rigid Structure-from-Motion (NRSfM) is an active research eld in computer vision. The task of NRSfM is to simultaneously recover camera motion and 3D structure from…
(more)

Subjects/Keywords: Non-rigid Structure-from-Motion; sparse; dense; reconstructability; shape clustering; spatial-temporal; convex optimization; convex optimisation; simple

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APA (6^{th} Edition):

Deng, H. (2017). Shape Clustering and Spatial-temporal Constraint for Non-rigid Structure from Motion . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/113634

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Deng, Huizhong. “Shape Clustering and Spatial-temporal Constraint for Non-rigid Structure from Motion .” 2017. Thesis, Australian National University. Accessed April 21, 2019. http://hdl.handle.net/1885/113634.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Deng, Huizhong. “Shape Clustering and Spatial-temporal Constraint for Non-rigid Structure from Motion .” 2017. Web. 21 Apr 2019.

Vancouver:

Deng H. Shape Clustering and Spatial-temporal Constraint for Non-rigid Structure from Motion . [Internet] [Thesis]. Australian National University; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1885/113634.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Deng H. Shape Clustering and Spatial-temporal Constraint for Non-rigid Structure from Motion . [Thesis]. Australian National University; 2017. Available from: http://hdl.handle.net/1885/113634

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

11.
Das, Puja.
Online *convex* *optimization* and its application to online portfolio selection.

Degree: PhD, Computer science, 2014, University of Minnesota

URL: http://hdl.handle.net/11299/163662

► Today, whether we consider the data from the internet, consumers, financial markets, a common feature emerges: all of them involve huge amounts of dynamic data…
(more)

Subjects/Keywords: Alternating direction method of multipliers; Constrained optimization; Meta optimization; Non-smooth composite objective; Online convex optimization; Online portfolio selection

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Das, P. (2014). Online convex optimization and its application to online portfolio selection. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/163662

Chicago Manual of Style (16^{th} Edition):

Das, Puja. “Online convex optimization and its application to online portfolio selection.” 2014. Doctoral Dissertation, University of Minnesota. Accessed April 21, 2019. http://hdl.handle.net/11299/163662.

MLA Handbook (7^{th} Edition):

Das, Puja. “Online convex optimization and its application to online portfolio selection.” 2014. Web. 21 Apr 2019.

Vancouver:

Das P. Online convex optimization and its application to online portfolio selection. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11299/163662.

Council of Science Editors:

Das P. Online convex optimization and its application to online portfolio selection. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/163662

12.
Hess, Roxana.
Some approximation schemes in polynomial *optimization* : Quelques schémas d'approximation en optimisation polynomiale.

Degree: Docteur es, Automatique, 2017, Université Toulouse III – Paul Sabatier

URL: http://www.theses.fr/2017TOU30129

►

Cette thèse est dédiée à l'étude de la hiérarchie moments-sommes-de-carrés, une famille de problèmes de programmation semi-définie en optimisation polynomiale, couramment appelée hiérarchie de Lasserre.… (more)

Subjects/Keywords: Optimisation non-convexe; Optimisation non-lisse; Approximations polynomiales; Optimisation semi-algébrique; Optimisation semi-définie positive; Non-convex optimization; Non-smooth optimization; Polynomial approximations; Semialgebraic optimization; Semidefinite programming

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APA (6^{th} Edition):

Hess, R. (2017). Some approximation schemes in polynomial optimization : Quelques schémas d'approximation en optimisation polynomiale. (Doctoral Dissertation). Université Toulouse III – Paul Sabatier. Retrieved from http://www.theses.fr/2017TOU30129

Chicago Manual of Style (16^{th} Edition):

Hess, Roxana. “Some approximation schemes in polynomial optimization : Quelques schémas d'approximation en optimisation polynomiale.” 2017. Doctoral Dissertation, Université Toulouse III – Paul Sabatier. Accessed April 21, 2019. http://www.theses.fr/2017TOU30129.

MLA Handbook (7^{th} Edition):

Hess, Roxana. “Some approximation schemes in polynomial optimization : Quelques schémas d'approximation en optimisation polynomiale.” 2017. Web. 21 Apr 2019.

Vancouver:

Hess R. Some approximation schemes in polynomial optimization : Quelques schémas d'approximation en optimisation polynomiale. [Internet] [Doctoral dissertation]. Université Toulouse III – Paul Sabatier; 2017. [cited 2019 Apr 21]. Available from: http://www.theses.fr/2017TOU30129.

Council of Science Editors:

Hess R. Some approximation schemes in polynomial optimization : Quelques schémas d'approximation en optimisation polynomiale. [Doctoral Dissertation]. Université Toulouse III – Paul Sabatier; 2017. Available from: http://www.theses.fr/2017TOU30129

University of California – Irvine

13.
Janzamin, Majid.
* Non*-

Degree: Electrical and Computer Engineering, 2016, University of California – Irvine

URL: http://www.escholarship.org/uc/item/7p90p57n

► In the last decade, machine learning algorithms have been substantially developed and they have gained tremendous empirical success. But, there is limited theoretical understanding about…
(more)

Subjects/Keywords: Computer science; Latent Representations; Machine Learning; Neural Networks; Non-convex Optimization; Tensor Decomposition

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APA (6^{th} Edition):

Janzamin, M. (2016). Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods. (Thesis). University of California – Irvine. Retrieved from http://www.escholarship.org/uc/item/7p90p57n

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Janzamin, Majid. “Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods.” 2016. Thesis, University of California – Irvine. Accessed April 21, 2019. http://www.escholarship.org/uc/item/7p90p57n.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Janzamin, Majid. “Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods.” 2016. Web. 21 Apr 2019.

Vancouver:

Janzamin M. Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods. [Internet] [Thesis]. University of California – Irvine; 2016. [cited 2019 Apr 21]. Available from: http://www.escholarship.org/uc/item/7p90p57n.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Janzamin M. Non-convex Optimization in Machine Learning: Provable Guarantees Using Tensor Methods. [Thesis]. University of California – Irvine; 2016. Available from: http://www.escholarship.org/uc/item/7p90p57n

Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

14. Cetin, H. Spectrum Sharing among Cellular Operators from a Game Theoretical Cognitive and Cooperative Networking Perspective:.

Degree: 2012, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:a54bccdc-7ec4-44ba-aba9-088d63c716c1

► The demand for wireless services and the need for high data-rates are growing rapidly. Future generation networks are expected to provide high data-rates in the…
(more)

Subjects/Keywords: Spectrum Sharing; 3G and 4G networking; Interference Mitigation; Game Theory; Non-convex Optimization; Beamforming

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APA (6^{th} Edition):

Cetin, H. (2012). Spectrum Sharing among Cellular Operators from a Game Theoretical Cognitive and Cooperative Networking Perspective:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a54bccdc-7ec4-44ba-aba9-088d63c716c1

Chicago Manual of Style (16^{th} Edition):

Cetin, H. “Spectrum Sharing among Cellular Operators from a Game Theoretical Cognitive and Cooperative Networking Perspective:.” 2012. Masters Thesis, Delft University of Technology. Accessed April 21, 2019. http://resolver.tudelft.nl/uuid:a54bccdc-7ec4-44ba-aba9-088d63c716c1.

MLA Handbook (7^{th} Edition):

Cetin, H. “Spectrum Sharing among Cellular Operators from a Game Theoretical Cognitive and Cooperative Networking Perspective:.” 2012. Web. 21 Apr 2019.

Vancouver:

Cetin H. Spectrum Sharing among Cellular Operators from a Game Theoretical Cognitive and Cooperative Networking Perspective:. [Internet] [Masters thesis]. Delft University of Technology; 2012. [cited 2019 Apr 21]. Available from: http://resolver.tudelft.nl/uuid:a54bccdc-7ec4-44ba-aba9-088d63c716c1.

Council of Science Editors:

Cetin H. Spectrum Sharing among Cellular Operators from a Game Theoretical Cognitive and Cooperative Networking Perspective:. [Masters Thesis]. Delft University of Technology; 2012. Available from: http://resolver.tudelft.nl/uuid:a54bccdc-7ec4-44ba-aba9-088d63c716c1

15.
Yi, Xinyang.
Learning with latent structures, robustness and *non*-linearity : *non*-*convex* approaches.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/46474

► *Non*-*convex* *optimization* based algorithms are ubiquitous in machine learning and statistical estimation, especially in dealing with complex models that are noisy, *non*-linear or contain latent…
(more)

Subjects/Keywords: Statistical machine learning; High dimensional statistics; Non-convex optimization; Mixed linear regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Yi, X. (2016). Learning with latent structures, robustness and non-linearity : non-convex approaches. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46474

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Yi, Xinyang. “Learning with latent structures, robustness and non-linearity : non-convex approaches.” 2016. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/46474.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yi, Xinyang. “Learning with latent structures, robustness and non-linearity : non-convex approaches.” 2016. Web. 21 Apr 2019.

Vancouver:

Yi X. Learning with latent structures, robustness and non-linearity : non-convex approaches. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/46474.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yi X. Learning with latent structures, robustness and non-linearity : non-convex approaches. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46474

Not specified: Masters Thesis or Doctoral Dissertation

16.
Mierswa, Ingo.
* Non*-

Degree: 2009, Technische Universität Dortmund

URL: http://hdl.handle.net/2003/26104

Subjects/Keywords: Data mining; Multi-objective optimization; Non-convex optimization; 004

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Mierswa, I. (2009). Non-convex and multi-objective optimization in data mining. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/26104

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Mierswa, Ingo. “Non-convex and multi-objective optimization in data mining.” 2009. Thesis, Technische Universität Dortmund. Accessed April 21, 2019. http://hdl.handle.net/2003/26104.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Mierswa, Ingo. “Non-convex and multi-objective optimization in data mining.” 2009. Web. 21 Apr 2019.

Vancouver:

Mierswa I. Non-convex and multi-objective optimization in data mining. [Internet] [Thesis]. Technische Universität Dortmund; 2009. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2003/26104.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mierswa I. Non-convex and multi-objective optimization in data mining. [Thesis]. Technische Universität Dortmund; 2009. Available from: http://hdl.handle.net/2003/26104

Not specified: Masters Thesis or Doctoral Dissertation

Université Catholique de Louvain

17.
Degraux, Kévin.
Methods for solving regularized inverse problems : from *non*-Euclidean fidelities to computational imaging applications.

Degree: 2017, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/191756

►

Many branches of science and engineering are concerned with the problem of recording signals from physical phenomena. However, an acquisition system does not always directly… (more)

Subjects/Keywords: Signal Processing; Sparsity; Non-smooth Optimization; Inverse Problems; Convex Optimization; Compressed Sensing; Computational Imaging; Dictionary Learning; Hyperspectral; Multispectral

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APA (6^{th} Edition):

Degraux, K. (2017). Methods for solving regularized inverse problems : from non-Euclidean fidelities to computational imaging applications. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/191756

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Degraux, Kévin. “Methods for solving regularized inverse problems : from non-Euclidean fidelities to computational imaging applications.” 2017. Thesis, Université Catholique de Louvain. Accessed April 21, 2019. http://hdl.handle.net/2078.1/191756.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Degraux, Kévin. “Methods for solving regularized inverse problems : from non-Euclidean fidelities to computational imaging applications.” 2017. Web. 21 Apr 2019.

Vancouver:

Degraux K. Methods for solving regularized inverse problems : from non-Euclidean fidelities to computational imaging applications. [Internet] [Thesis]. Université Catholique de Louvain; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2078.1/191756.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Degraux K. Methods for solving regularized inverse problems : from non-Euclidean fidelities to computational imaging applications. [Thesis]. Université Catholique de Louvain; 2017. Available from: http://hdl.handle.net/2078.1/191756

Not specified: Masters Thesis or Doctoral Dissertation

Iowa State University

18.
Ma, Xu.
Distributed approaches for solving *non*-*convex* optimizations under strong duality.

Degree: 2016, Iowa State University

URL: https://lib.dr.iastate.edu/etd/15769

► This dissertation studies *non*-*convex* optimizations under the strong duality condition. In general, *non*-*convex* problems are *non*-deterministic polynomial-time (NP) hard and hence are difficult to solve.…
(more)

Subjects/Keywords: distributed approaches; non-convex optimization; optimal power flow (OPF); optimization dynamics; primal-dual algorithm; QCQP; Electrical and Electronics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Ma, X. (2016). Distributed approaches for solving non-convex optimizations under strong duality. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/15769

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Ma, Xu. “Distributed approaches for solving non-convex optimizations under strong duality.” 2016. Thesis, Iowa State University. Accessed April 21, 2019. https://lib.dr.iastate.edu/etd/15769.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ma, Xu. “Distributed approaches for solving non-convex optimizations under strong duality.” 2016. Web. 21 Apr 2019.

Vancouver:

Ma X. Distributed approaches for solving non-convex optimizations under strong duality. [Internet] [Thesis]. Iowa State University; 2016. [cited 2019 Apr 21]. Available from: https://lib.dr.iastate.edu/etd/15769.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ma X. Distributed approaches for solving non-convex optimizations under strong duality. [Thesis]. Iowa State University; 2016. Available from: https://lib.dr.iastate.edu/etd/15769

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

19.
Wang, Gang.
* Non*-

Degree: PhD, Electrical Engineering, 2018, University of Minnesota

URL: http://hdl.handle.net/11299/198408

► High-dimensional signal estimation plays a fundamental role in various science and engineering applications, including optical and medical imaging, wireless communications, and power system monitoring. The…
(more)

Subjects/Keywords: Amplitude flow; Information-theoretic limit; Linear convergence to global optimum; Non-convex optimization; Sparsity; Stochastic optimization

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Wang, G. (2018). Non-Convex Phase Retrieval Algorithms and Performance Analysis. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/198408

Chicago Manual of Style (16^{th} Edition):

Wang, Gang. “Non-Convex Phase Retrieval Algorithms and Performance Analysis.” 2018. Doctoral Dissertation, University of Minnesota. Accessed April 21, 2019. http://hdl.handle.net/11299/198408.

MLA Handbook (7^{th} Edition):

Wang, Gang. “Non-Convex Phase Retrieval Algorithms and Performance Analysis.” 2018. Web. 21 Apr 2019.

Vancouver:

Wang G. Non-Convex Phase Retrieval Algorithms and Performance Analysis. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11299/198408.

Council of Science Editors:

Wang G. Non-Convex Phase Retrieval Algorithms and Performance Analysis. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/198408

McMaster University

20. Yazhemsky, Dennis Ion. A Real-Time Capable Adaptive Optimal Controller for a Commuter Train.

Degree: MASc, 2017, McMaster University

URL: http://hdl.handle.net/11375/21469

►

This research formulates and implements a novel closed-loop optimal control system that drives a train between two stations in an optimal time, energy efficient, or… (more)

Subjects/Keywords: Optimal Control; Commuter Train; Numerical Optimization; Convex; Second Order Cone Program; Multi-Vehicle; Real-Time; Sparse Optimization; Non-Convex Optimization; Energy Optimal; Time Optimal; Closed-Loop; Embedded Systems; Convex Solver; Non-Linear Programming

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Yazhemsky, D. I. (2017). A Real-Time Capable Adaptive Optimal Controller for a Commuter Train. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/21469

Chicago Manual of Style (16^{th} Edition):

Yazhemsky, Dennis Ion. “A Real-Time Capable Adaptive Optimal Controller for a Commuter Train.” 2017. Masters Thesis, McMaster University. Accessed April 21, 2019. http://hdl.handle.net/11375/21469.

MLA Handbook (7^{th} Edition):

Yazhemsky, Dennis Ion. “A Real-Time Capable Adaptive Optimal Controller for a Commuter Train.” 2017. Web. 21 Apr 2019.

Vancouver:

Yazhemsky DI. A Real-Time Capable Adaptive Optimal Controller for a Commuter Train. [Internet] [Masters thesis]. McMaster University; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/11375/21469.

Council of Science Editors:

Yazhemsky DI. A Real-Time Capable Adaptive Optimal Controller for a Commuter Train. [Masters Thesis]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/21469

21.
Huang, Xiaoge.
* Non*-

Degree: 2018, TDX

URL: http://hdl.handle.net/10803/568295

► In this thesis, we explore interweave communication systems in cognitive radio networks where the overall objective is to maximize the sum-rate of each cognitive radio…
(more)

Subjects/Keywords: Quasi-Nash Equilibrium; Non-cooperative Game; Non-convex Optimization; Cognitive Radio Networks; UNESCO::CIENCIAS TECNOLÓGICAS::Tecnología de las telecomunicaciones::Otras

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Huang, X. (2018). Non-convex distributed power allocation games in cognitive radio networks. (Thesis). TDX. Retrieved from http://hdl.handle.net/10803/568295

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Huang, Xiaoge. “Non-convex distributed power allocation games in cognitive radio networks.” 2018. Thesis, TDX. Accessed April 21, 2019. http://hdl.handle.net/10803/568295.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Huang, Xiaoge. “Non-convex distributed power allocation games in cognitive radio networks.” 2018. Web. 21 Apr 2019.

Vancouver:

Huang X. Non-convex distributed power allocation games in cognitive radio networks. [Internet] [Thesis]. TDX; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10803/568295.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Huang X. Non-convex distributed power allocation games in cognitive radio networks. [Thesis]. TDX; 2018. Available from: http://hdl.handle.net/10803/568295

Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

22. Dhillon, Harpreet Singh. Optimal Sum-Rate of Multi-Band MIMO Interference Channel.

Degree: MS, Electrical and Computer Engineering, 2010, Virginia Tech

URL: http://hdl.handle.net/10919/34766

► While the channel capacity of an isolated noise-limited wireless link is well-understood, the same is not true for the interference-limited wireless links that coexist in…
(more)

Subjects/Keywords: capacity; sum-rate maximization; non-linear non-convex optimization; Interference channel; global optimal solution; MIMO; power control

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APA (6^{th} Edition):

Dhillon, H. S. (2010). Optimal Sum-Rate of Multi-Band MIMO Interference Channel. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/34766

Chicago Manual of Style (16^{th} Edition):

Dhillon, Harpreet Singh. “Optimal Sum-Rate of Multi-Band MIMO Interference Channel.” 2010. Masters Thesis, Virginia Tech. Accessed April 21, 2019. http://hdl.handle.net/10919/34766.

MLA Handbook (7^{th} Edition):

Dhillon, Harpreet Singh. “Optimal Sum-Rate of Multi-Band MIMO Interference Channel.” 2010. Web. 21 Apr 2019.

Vancouver:

Dhillon HS. Optimal Sum-Rate of Multi-Band MIMO Interference Channel. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10919/34766.

Council of Science Editors:

Dhillon HS. Optimal Sum-Rate of Multi-Band MIMO Interference Channel. [Masters Thesis]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/34766

University of Alberta

23. Chen, Ke. Robust matrix rank reduction methods for seismic data processing.

Degree: MS, Department of Physics, 2013, University of Alberta

URL: https://era.library.ualberta.ca/files/n870zs375

► An important step of seismic data processing entails signal de-noising. Traditional de-noising methods assume Gaussian noise model and their performance degrades in the presence of…
(more)

Subjects/Keywords: Matrix rank reduction; Seismic data reconstruction; Non-Gaussian noise; Convex optimization; Seismic data denoising; Robust statistics

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APA (6^{th} Edition):

Chen, K. (2013). Robust matrix rank reduction methods for seismic data processing. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/n870zs375

Chicago Manual of Style (16^{th} Edition):

Chen, Ke. “Robust matrix rank reduction methods for seismic data processing.” 2013. Masters Thesis, University of Alberta. Accessed April 21, 2019. https://era.library.ualberta.ca/files/n870zs375.

MLA Handbook (7^{th} Edition):

Chen, Ke. “Robust matrix rank reduction methods for seismic data processing.” 2013. Web. 21 Apr 2019.

Vancouver:

Chen K. Robust matrix rank reduction methods for seismic data processing. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2019 Apr 21]. Available from: https://era.library.ualberta.ca/files/n870zs375.

Council of Science Editors:

Chen K. Robust matrix rank reduction methods for seismic data processing. [Masters Thesis]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/n870zs375

University of Waterloo

24.
Motahari, Seyed Abolfazl.
Interference Management in *Non*-cooperative Networks.

Degree: 2009, University of Waterloo

URL: http://hdl.handle.net/10012/4824

► Spectrum sharing is known as a key solution to accommodate the increasing number of users and the growing demand for throughput in wireless networks. While…
(more)

Subjects/Keywords: Interference Channels; Interference Alignment; Diophantine Approximation; Convex Optimization; Non-cooperative Networks; Interference Management; Gaussian Channels; Random Codebooks; Structural Codes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Motahari, S. A. (2009). Interference Management in Non-cooperative Networks. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/4824

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Motahari, Seyed Abolfazl. “Interference Management in Non-cooperative Networks.” 2009. Thesis, University of Waterloo. Accessed April 21, 2019. http://hdl.handle.net/10012/4824.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Motahari, Seyed Abolfazl. “Interference Management in Non-cooperative Networks.” 2009. Web. 21 Apr 2019.

Vancouver:

Motahari SA. Interference Management in Non-cooperative Networks. [Internet] [Thesis]. University of Waterloo; 2009. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10012/4824.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Motahari SA. Interference Management in Non-cooperative Networks. [Thesis]. University of Waterloo; 2009. Available from: http://hdl.handle.net/10012/4824

Not specified: Masters Thesis or Doctoral Dissertation

25.
White, Christopher Dale.
Optimality guarantees for *non*-*convex* low rank matrix recovery problems.

Degree: Mathematics, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/32534

► Low rank matrices lie at the heart of many techniques in scientific computing and machine learning. In this thesis, we examine various scenarios in which…
(more)

Subjects/Keywords: Optimization; Non-convex; Low rank matrix

…*optimization* problem with a matrix rank constraint is inherently *non*-*convex*. This is easy to see by… …to solve the *non*-*convex* *optimization* problem
min
U ∈Rn×k
S − UUT
2
F
(1.9)… …form
yi := kaTi Xk22
(2.1)
by solving the *non*-*convex* *optimization* problem
m
1 X… …class of *non*-*convex*
problems arising in machine learning applications such as matrix… …Recovery from Quadratic Samples
Recall that we aim to solve the *non*-*convex* inverse problem of…

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

White, C. D. (2015). Optimality guarantees for non-convex low rank matrix recovery problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32534

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

White, Christopher Dale. “Optimality guarantees for non-convex low rank matrix recovery problems.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/32534.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

White, Christopher Dale. “Optimality guarantees for non-convex low rank matrix recovery problems.” 2015. Web. 21 Apr 2019.

Vancouver:

White CD. Optimality guarantees for non-convex low rank matrix recovery problems. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/32534.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

White CD. Optimality guarantees for non-convex low rank matrix recovery problems. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32534

Not specified: Masters Thesis or Doctoral Dissertation

Cornell University

26.
Yuan, Yang.
PROVABLE AND PRACTICAL ALGORITHMS FOR *NON*-*CONVEX* PROBLEMS IN MACHINE LEARNING
.

Degree: 2018, Cornell University

URL: http://hdl.handle.net/1813/59273

► Machine learning has become one of the most exciting research areas in the world, with various applications. However, there exists a noticeable gap between theory…
(more)

Subjects/Keywords: Artificial intelligence; Computer science; Hyperparameter tuning; Local minima; Non-convex optimization; Saddle points; Stochastic Gradient Descent; machine learning

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APA (6^{th} Edition):

Yuan, Y. (2018). PROVABLE AND PRACTICAL ALGORITHMS FOR NON-CONVEX PROBLEMS IN MACHINE LEARNING . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/59273

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Yuan, Yang. “PROVABLE AND PRACTICAL ALGORITHMS FOR NON-CONVEX PROBLEMS IN MACHINE LEARNING .” 2018. Thesis, Cornell University. Accessed April 21, 2019. http://hdl.handle.net/1813/59273.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yuan, Yang. “PROVABLE AND PRACTICAL ALGORITHMS FOR NON-CONVEX PROBLEMS IN MACHINE LEARNING .” 2018. Web. 21 Apr 2019.

Vancouver:

Yuan Y. PROVABLE AND PRACTICAL ALGORITHMS FOR NON-CONVEX PROBLEMS IN MACHINE LEARNING . [Internet] [Thesis]. Cornell University; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1813/59273.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yuan Y. PROVABLE AND PRACTICAL ALGORITHMS FOR NON-CONVEX PROBLEMS IN MACHINE LEARNING . [Thesis]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59273

Not specified: Masters Thesis or Doctoral Dissertation

University of Pennsylvania

27. Ma, Zhuang. Canonical Correlation Analysis And Network Data Modeling: Statistical And Computational Properties.

Degree: 2017, University of Pennsylvania

URL: https://repository.upenn.edu/edissertations/2460

► Classical decision theory evaluates an estimator mostly by its statistical properties, either the closeness to the underlying truth or the predictive ability for new observations.…
(more)

Subjects/Keywords: Canonical Correlation Analysis; computational efficiency; dimension reduction; minimax rates; network data modeling; non-convex optimization; Statistics and Probability

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APA (6^{th} Edition):

Ma, Z. (2017). Canonical Correlation Analysis And Network Data Modeling: Statistical And Computational Properties. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/2460

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Ma, Zhuang. “Canonical Correlation Analysis And Network Data Modeling: Statistical And Computational Properties.” 2017. Thesis, University of Pennsylvania. Accessed April 21, 2019. https://repository.upenn.edu/edissertations/2460.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Ma, Zhuang. “Canonical Correlation Analysis And Network Data Modeling: Statistical And Computational Properties.” 2017. Web. 21 Apr 2019.

Vancouver:

Ma Z. Canonical Correlation Analysis And Network Data Modeling: Statistical And Computational Properties. [Internet] [Thesis]. University of Pennsylvania; 2017. [cited 2019 Apr 21]. Available from: https://repository.upenn.edu/edissertations/2460.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ma Z. Canonical Correlation Analysis And Network Data Modeling: Statistical And Computational Properties. [Thesis]. University of Pennsylvania; 2017. Available from: https://repository.upenn.edu/edissertations/2460

Not specified: Masters Thesis or Doctoral Dissertation

University of Florida

28.
Zhu, Jiajie.
Efficient Sparse *Optimization* Algorithms: Designing *Non*-*convex* and Distributed Algorithms for Machine Learning and Engineering Applications.

Degree: PhD, Mathematics, 2015, University of Florida

URL: http://ufdc.ufl.edu/UFE0049460

Subjects/Keywords: algorithm; machine-learning; non-convex; optimization; sparsity

Record Details Similar Records

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Zhu, J. (2015). Efficient Sparse Optimization Algorithms: Designing Non-convex and Distributed Algorithms for Machine Learning and Engineering Applications. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0049460

Chicago Manual of Style (16^{th} Edition):

Zhu, Jiajie. “Efficient Sparse Optimization Algorithms: Designing Non-convex and Distributed Algorithms for Machine Learning and Engineering Applications.” 2015. Doctoral Dissertation, University of Florida. Accessed April 21, 2019. http://ufdc.ufl.edu/UFE0049460.

MLA Handbook (7^{th} Edition):

Zhu, Jiajie. “Efficient Sparse Optimization Algorithms: Designing Non-convex and Distributed Algorithms for Machine Learning and Engineering Applications.” 2015. Web. 21 Apr 2019.

Vancouver:

Zhu J. Efficient Sparse Optimization Algorithms: Designing Non-convex and Distributed Algorithms for Machine Learning and Engineering Applications. [Internet] [Doctoral dissertation]. University of Florida; 2015. [cited 2019 Apr 21]. Available from: http://ufdc.ufl.edu/UFE0049460.

Council of Science Editors:

Zhu J. Efficient Sparse Optimization Algorithms: Designing Non-convex and Distributed Algorithms for Machine Learning and Engineering Applications. [Doctoral Dissertation]. University of Florida; 2015. Available from: http://ufdc.ufl.edu/UFE0049460

University of Lund

29.
Grussler, Christian.
Rank Reduction with *Convex* Constraints.

Degree: 2017, University of Lund

URL: http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf

► This thesis addresses problems which require low-rank solutions under *convex* constraints. In particular, the focus lies on model reduction of positive systems, as well as…
(more)

Subjects/Keywords: Reglerteknik; low-rank approximation; model reduction; non-convex optimization; Douglas-Rachford; matrix completion; overlapping norm; k-support norm; atomic norm

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APA (6^{th} Edition):

Grussler, C. (2017). Rank Reduction with Convex Constraints. (Doctoral Dissertation). University of Lund. Retrieved from http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf

Chicago Manual of Style (16^{th} Edition):

Grussler, Christian. “Rank Reduction with Convex Constraints.” 2017. Doctoral Dissertation, University of Lund. Accessed April 21, 2019. http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf.

MLA Handbook (7^{th} Edition):

Grussler, Christian. “Rank Reduction with Convex Constraints.” 2017. Web. 21 Apr 2019.

Vancouver:

Grussler C. Rank Reduction with Convex Constraints. [Internet] [Doctoral dissertation]. University of Lund; 2017. [cited 2019 Apr 21]. Available from: http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf.

Council of Science Editors:

Grussler C. Rank Reduction with Convex Constraints. [Doctoral Dissertation]. University of Lund; 2017. Available from: http://lup.lub.lu.se/record/54cb814f-59fe-4bc9-a7ef-773cbcf06889 ; http://portal.research.lu.se/ws/files/19595129/Thesis.pdf

Universitat de Valencia

30.
Shah, Santosh.
Joint *Optimization* of Sensor Selection and Routing for Distributed Estimation in Wireless Sensor Networks
.

Degree: 2014, Universitat de Valencia

URL: http://hdl.handle.net/10550/33655

► Avances recientes en redes inalámbricos de sensores (WSNs, Wireless Sensor Networks) han posibilitado que pequeños sensores, baratos y con recursos limitados tanto en sensado, comunicación,…
(more)

Subjects/Keywords: multihop routing; NP-hard; parameter estimation; adaptive quantization; sensor selection; lower bound; non-convex optimization; energy efficient; wireless sensor networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^{th} Edition):

Shah, S. (2014). Joint Optimization of Sensor Selection and Routing for Distributed Estimation in Wireless Sensor Networks . (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/33655

Chicago Manual of Style (16^{th} Edition):

Shah, Santosh. “Joint Optimization of Sensor Selection and Routing for Distributed Estimation in Wireless Sensor Networks .” 2014. Doctoral Dissertation, Universitat de Valencia. Accessed April 21, 2019. http://hdl.handle.net/10550/33655.

MLA Handbook (7^{th} Edition):

Shah, Santosh. “Joint Optimization of Sensor Selection and Routing for Distributed Estimation in Wireless Sensor Networks .” 2014. Web. 21 Apr 2019.

Vancouver:

Shah S. Joint Optimization of Sensor Selection and Routing for Distributed Estimation in Wireless Sensor Networks . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2014. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10550/33655.

Council of Science Editors:

Shah S. Joint Optimization of Sensor Selection and Routing for Distributed Estimation in Wireless Sensor Networks . [Doctoral Dissertation]. Universitat de Valencia; 2014. Available from: http://hdl.handle.net/10550/33655