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337 total matches.

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- 2009 – 2013 (125)
- 2004 – 2008 (30)

Universities

Department

- Electrical Engineering (12)
- Mathematics (11)
- Electrical and Computer Engineering (10)
- Industrial and Systems Engineering (10)
- Informatique (10)

Degrees

- PhD (78)
- Docteur es (39)
- MS (10)
- Master (10)

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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 June 20, 2018. 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. 20 Jun 2018.

Vancouver:

Uthayakumar R. Study on convergence of optimization problems;. [Internet] [Thesis]. INFLIBNET; 2014. [cited 2018 Jun 20]. 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

Victoria University of Wellington

2.
Jellyman, Dayle Raymond.
*Convex**Optimization* for Distributed Acoustic Beamforming.

Degree: 2017, Victoria University of Wellington

URL: http://hdl.handle.net/10063/6650

► Beamforming filter *optimization* can be performed over a distributed wireless sensor network, but the output calculation remains either centralized or linked in time to the…
(more)

Subjects/Keywords: Distributed; Beamforming; Convex optimization

Record Details Similar Records

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APA (6^{th} Edition):

Jellyman, D. R. (2017). Convex Optimization for Distributed Acoustic Beamforming. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/6650

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

Jellyman, Dayle Raymond. “Convex Optimization for Distributed Acoustic Beamforming.” 2017. Masters Thesis, Victoria University of Wellington. Accessed June 20, 2018. http://hdl.handle.net/10063/6650.

MLA Handbook (7^{th} Edition):

Jellyman, Dayle Raymond. “Convex Optimization for Distributed Acoustic Beamforming.” 2017. Web. 20 Jun 2018.

Vancouver:

Jellyman DR. Convex Optimization for Distributed Acoustic Beamforming. [Internet] [Masters thesis]. Victoria University of Wellington; 2017. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/10063/6650.

Council of Science Editors:

Jellyman DR. Convex Optimization for Distributed Acoustic Beamforming. [Masters Thesis]. Victoria University of Wellington; 2017. Available from: http://hdl.handle.net/10063/6650

NSYSU

3. Zhang, Shu-Bin. Study on Digital Filter Design and Coefficient Quantization.

Degree: Master, Communications Engineering, 2011, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727111-135237

► In this thesis, the basic theory is *convex* *optimization* theory[1]. And we study the problem about how to transfer to *convex* *optimization* problem from the…
(more)

Subjects/Keywords: Filter; Optimization; Convex; Bits; Quantization

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APA (6^{th} Edition):

Zhang, S. (2011). Study on Digital Filter Design and Coefficient Quantization. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727111-135237

Not specified: Masters Thesis or Doctoral Dissertation

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

Zhang, Shu-Bin. “Study on Digital Filter Design and Coefficient Quantization.” 2011. Thesis, NSYSU. Accessed June 20, 2018. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727111-135237.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Zhang, Shu-Bin. “Study on Digital Filter Design and Coefficient Quantization.” 2011. Web. 20 Jun 2018.

Vancouver:

Zhang S. Study on Digital Filter Design and Coefficient Quantization. [Internet] [Thesis]. NSYSU; 2011. [cited 2018 Jun 20]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727111-135237.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang S. Study on Digital Filter Design and Coefficient Quantization. [Thesis]. NSYSU; 2011. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727111-135237

Not specified: Masters Thesis or Doctoral Dissertation

Princeton University

4.
Ma, Tengyu.
Non-*convex* *Optimization* for Machine Learning: Design, Analysis, and Understanding
.

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 June 20, 2018. 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. 20 Jun 2018.

Vancouver:

Ma T. Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2018 Jun 20]. 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

5.
Cosentino, Alessandro.
Quantum State Local Distinguishability via *Convex* * Optimization*.

Degree: 2015, University of Waterloo

URL: http://hdl.handle.net/10012/9572

► Entanglement and nonlocality play a fundamental role in quantum computing. To understand the interplay between these phenomena, researchers have considered the model of local operations…
(more)

Subjects/Keywords: quantum information; convex optimization

Record Details Similar Records

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

APA (6^{th} Edition):

Cosentino, A. (2015). Quantum State Local Distinguishability via Convex Optimization. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/9572

Not specified: Masters Thesis or Doctoral Dissertation

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

Cosentino, Alessandro. “Quantum State Local Distinguishability via Convex Optimization.” 2015. Thesis, University of Waterloo. Accessed June 20, 2018. http://hdl.handle.net/10012/9572.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Cosentino, Alessandro. “Quantum State Local Distinguishability via Convex Optimization.” 2015. Web. 20 Jun 2018.

Vancouver:

Cosentino A. Quantum State Local Distinguishability via Convex Optimization. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/10012/9572.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cosentino A. Quantum State Local Distinguishability via Convex Optimization. [Thesis]. University of Waterloo; 2015. Available from: http://hdl.handle.net/10012/9572

Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

6. Taghavi, Soraya. Quantum computation and optimized error correction.

Degree: PhD, Electrical Engineering, 2010, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/308762/rec/5352

► Two subjects in the area of quantum computation are considered here. In the first chapter I present a universal model for a quantum Robot. Chapters…
(more)

Subjects/Keywords: quantum error correction; convex optimization

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APA (6^{th} Edition):

Taghavi, S. (2010). Quantum computation and optimized error correction. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/308762/rec/5352

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

Taghavi, Soraya. “Quantum computation and optimized error correction.” 2010. Doctoral Dissertation, University of Southern California. Accessed June 20, 2018. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/308762/rec/5352.

MLA Handbook (7^{th} Edition):

Taghavi, Soraya. “Quantum computation and optimized error correction.” 2010. Web. 20 Jun 2018.

Vancouver:

Taghavi S. Quantum computation and optimized error correction. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2018 Jun 20]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/308762/rec/5352.

Council of Science Editors:

Taghavi S. Quantum computation and optimized error correction. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/308762/rec/5352

University of Texas – Austin

7.
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

Record Details Similar Records

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

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 June 20, 2018. 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. 20 Jun 2018.

Vancouver:

Park D. Efficient non-convex algorithms for large-scale learning problems. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2018 Jun 20]. 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

University of Texas – Austin

8. Berning, Andrew Walter, Jr. Verification of successive convexification algorithm.

Degree: Aerospace Engineering, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/41579

► In this report, I describe a technique which allows a non-*convex* optimal control problem to be expressed and solved in a *convex* manner. I then…
(more)

Subjects/Keywords: Convex; Convexification; Optimization; Verification

Record Details Similar Records

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

APA (6^{th} Edition):

Berning, Andrew Walter, J. (2016). Verification of successive convexification algorithm. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/41579

Not specified: Masters Thesis or Doctoral Dissertation

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

Berning, Andrew Walter, Jr. “Verification of successive convexification algorithm.” 2016. Thesis, University of Texas – Austin. Accessed June 20, 2018. http://hdl.handle.net/2152/41579.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Berning, Andrew Walter, Jr. “Verification of successive convexification algorithm.” 2016. Web. 20 Jun 2018.

Vancouver:

Berning, Andrew Walter J. Verification of successive convexification algorithm. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/2152/41579.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Berning, Andrew Walter J. Verification of successive convexification algorithm. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/41579

Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

9.
Choi, Hyungjin.
Quantication of the Impact of Uncertainty in Power Systems using *Convex* * Optimization*.

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

URL: http://hdl.handle.net/11299/190457

► Rampant integration of renewable resources (e.g., photovoltaic and wind-energy conversion systems) and uncontrollable and elastic loads (e.g., plug-in hybrid electric vehicles) are rapidly transforming power…
(more)

Subjects/Keywords: Convex Optimization; Power Systems; Sensitivity; Stability; Uncertainty

Record Details Similar Records

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APA (6^{th} Edition):

Choi, H. (2017). Quantication of the Impact of Uncertainty in Power Systems using Convex Optimization. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/190457

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

Choi, Hyungjin. “Quantication of the Impact of Uncertainty in Power Systems using Convex Optimization.” 2017. Doctoral Dissertation, University of Minnesota. Accessed June 20, 2018. http://hdl.handle.net/11299/190457.

MLA Handbook (7^{th} Edition):

Choi, Hyungjin. “Quantication of the Impact of Uncertainty in Power Systems using Convex Optimization.” 2017. Web. 20 Jun 2018.

Vancouver:

Choi H. Quantication of the Impact of Uncertainty in Power Systems using Convex Optimization. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/11299/190457.

Council of Science Editors:

Choi H. Quantication of the Impact of Uncertainty in Power Systems using Convex Optimization. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/190457

University of Edinburgh

10. Liu, Weigang. Enhancing physical layer security in wireless networks with cooperative approaches.

Degree: PhD, 2016, University of Edinburgh

URL: http://hdl.handle.net/1842/19565

► Motivated by recent developments in wireless communication, this thesis aims to characterize the secrecy performance in several types of typical wireless networks. Advanced techniques are…
(more)

Subjects/Keywords: physical layer security; convex optimization; stochastic geometry

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APA (6^{th} Edition):

Liu, W. (2016). Enhancing physical layer security in wireless networks with cooperative approaches. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/19565

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

Liu, Weigang. “Enhancing physical layer security in wireless networks with cooperative approaches.” 2016. Doctoral Dissertation, University of Edinburgh. Accessed June 20, 2018. http://hdl.handle.net/1842/19565.

MLA Handbook (7^{th} Edition):

Liu, Weigang. “Enhancing physical layer security in wireless networks with cooperative approaches.” 2016. Web. 20 Jun 2018.

Vancouver:

Liu W. Enhancing physical layer security in wireless networks with cooperative approaches. [Internet] [Doctoral dissertation]. University of Edinburgh; 2016. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/1842/19565.

Council of Science Editors:

Liu W. Enhancing physical layer security in wireless networks with cooperative approaches. [Doctoral Dissertation]. University of Edinburgh; 2016. Available from: http://hdl.handle.net/1842/19565

Iowa State University

11. Li, Chong. Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds.

Degree: 2013, Iowa State University

URL: https://lib.dr.iastate.edu/etd/13421

► Since the success of obtaining the capacity (i.e. the maximal achievable transmission rate under which the message can be recovered with arbitrarily small probability of…
(more)

Subjects/Keywords: Capacity; Convex Optimization; Feedback; Information Theory; Engineering

Record Details Similar Records

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

APA (6^{th} Edition):

Li, C. (2013). Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/13421

Not specified: Masters Thesis or Doctoral Dissertation

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

Li, Chong. “Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds.” 2013. Thesis, Iowa State University. Accessed June 20, 2018. https://lib.dr.iastate.edu/etd/13421.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Li, Chong. “Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds.” 2013. Web. 20 Jun 2018.

Vancouver:

Li C. Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds. [Internet] [Thesis]. Iowa State University; 2013. [cited 2018 Jun 20]. Available from: https://lib.dr.iastate.edu/etd/13421.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li C. Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds. [Thesis]. Iowa State University; 2013. Available from: https://lib.dr.iastate.edu/etd/13421

Not specified: Masters Thesis or Doctoral Dissertation

Universidade Nova

12. Soares, Diogo Lopes. Design of multidimensional compact constellations with high power efficiency.

Degree: 2013, Universidade Nova

URL: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11111

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia
*Advisors/Committee Members: Dinis, Rui, Beko, Marko.*

Subjects/Keywords: Multidimensional constellations; Power efficiency; Convex optimization

Record Details Similar Records

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

APA (6^{th} Edition):

Soares, D. L. (2013). Design of multidimensional compact constellations with high power efficiency. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11111

Not specified: Masters Thesis or Doctoral Dissertation

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

Soares, Diogo Lopes. “Design of multidimensional compact constellations with high power efficiency.” 2013. Thesis, Universidade Nova. Accessed June 20, 2018. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11111.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Soares, Diogo Lopes. “Design of multidimensional compact constellations with high power efficiency.” 2013. Web. 20 Jun 2018.

Vancouver:

Soares DL. Design of multidimensional compact constellations with high power efficiency. [Internet] [Thesis]. Universidade Nova; 2013. [cited 2018 Jun 20]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11111.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Soares DL. Design of multidimensional compact constellations with high power efficiency. [Thesis]. Universidade Nova; 2013. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11111

Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

13.
Karimi, Mehdi.
*Convex**Optimization* via Domain-Driven Barriers and Primal-Dual Interior-Point Methods.

Degree: 2017, University of Waterloo

URL: http://hdl.handle.net/10012/12209

► This thesis studies the theory and implementation of infeasible-start primal-dual interior-point methods for *convex* *optimization* problems. *Convex* *optimization* has applications in many fields of engineering…
(more)

Subjects/Keywords: convex optimization; primal-dual interior-point methods

Record Details Similar Records

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APA (6^{th} Edition):

Karimi, M. (2017). Convex Optimization via Domain-Driven Barriers and Primal-Dual Interior-Point Methods. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/12209

Not specified: Masters Thesis or Doctoral Dissertation

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

Karimi, Mehdi. “Convex Optimization via Domain-Driven Barriers and Primal-Dual Interior-Point Methods.” 2017. Thesis, University of Waterloo. Accessed June 20, 2018. http://hdl.handle.net/10012/12209.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Karimi, Mehdi. “Convex Optimization via Domain-Driven Barriers and Primal-Dual Interior-Point Methods.” 2017. Web. 20 Jun 2018.

Vancouver:

Karimi M. Convex Optimization via Domain-Driven Barriers and Primal-Dual Interior-Point Methods. [Internet] [Thesis]. University of Waterloo; 2017. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/10012/12209.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Karimi M. Convex Optimization via Domain-Driven Barriers and Primal-Dual Interior-Point Methods. [Thesis]. University of Waterloo; 2017. Available from: http://hdl.handle.net/10012/12209

Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

14.
Zhang, H.M.
Distributed *Convex* *Optimization*: A Study on the Primal-Dual Method of Multipliers:.

Degree: 2015, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:932db0bb-da4c-4ffe-892a-036d01a8071b

► The Primal-Dual Method of Multipliers (PDMM) is a new algorithm that solves *convex* *optimization* problems in a distributed manner. This study focuses on the convergence…
(more)

Subjects/Keywords: convex optimization; distributed signal processing; ADMM; PDMM

Record Details Similar Records

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

APA (6^{th} Edition):

Zhang, H. M. (2015). Distributed Convex Optimization: A Study on the Primal-Dual Method of Multipliers:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:932db0bb-da4c-4ffe-892a-036d01a8071b

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

Zhang, H M. “Distributed Convex Optimization: A Study on the Primal-Dual Method of Multipliers:.” 2015. Masters Thesis, Delft University of Technology. Accessed June 20, 2018. http://resolver.tudelft.nl/uuid:932db0bb-da4c-4ffe-892a-036d01a8071b.

MLA Handbook (7^{th} Edition):

Zhang, H M. “Distributed Convex Optimization: A Study on the Primal-Dual Method of Multipliers:.” 2015. Web. 20 Jun 2018.

Vancouver:

Zhang HM. Distributed Convex Optimization: A Study on the Primal-Dual Method of Multipliers:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2018 Jun 20]. Available from: http://resolver.tudelft.nl/uuid:932db0bb-da4c-4ffe-892a-036d01a8071b.

Council of Science Editors:

Zhang HM. Distributed Convex Optimization: A Study on the Primal-Dual Method of Multipliers:. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:932db0bb-da4c-4ffe-892a-036d01a8071b

15.
Umenberger, Jack.
* Convex* Identifcation of Stable Dynamical Systems
.

Degree: 2017, University of Sydney

URL: http://hdl.handle.net/2123/17321

► This thesis concerns the scalable application of *convex* *optimization* to data-driven modeling of dynamical systems, termed system identi cation in the control community. Two problems…
(more)

Subjects/Keywords: system identification; convex optimization; positive systems

Record Details Similar Records

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

APA (6^{th} Edition):

Umenberger, J. (2017). Convex Identifcation of Stable Dynamical Systems . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/17321

Not specified: Masters Thesis or Doctoral Dissertation

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

Umenberger, Jack. “Convex Identifcation of Stable Dynamical Systems .” 2017. Thesis, University of Sydney. Accessed June 20, 2018. http://hdl.handle.net/2123/17321.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Umenberger, Jack. “Convex Identifcation of Stable Dynamical Systems .” 2017. Web. 20 Jun 2018.

Vancouver:

Umenberger J. Convex Identifcation of Stable Dynamical Systems . [Internet] [Thesis]. University of Sydney; 2017. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/2123/17321.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Umenberger J. Convex Identifcation of Stable Dynamical Systems . [Thesis]. University of Sydney; 2017. Available from: http://hdl.handle.net/2123/17321

Not specified: Masters Thesis or Doctoral Dissertation

University of Ontario Institute of Technology

16.
Takeva-Velkova, Viliyana.
* Optimization* algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI).

Degree: 2010, University of Ontario Institute of Technology

URL: http://hdl.handle.net/10155/104

► Magnetic Resonance Imaging (MRI) is an essential instrument in clinical diag- nosis; however, it is burdened by a slow data acquisition process due to physical…
(more)

Subjects/Keywords: Compressive sensing; Sparse MRI; Convex optimization

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APA (6^{th} Edition):

Takeva-Velkova, V. (2010). Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI). (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/104

Not specified: Masters Thesis or Doctoral Dissertation

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

Takeva-Velkova, Viliyana. “Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI).” 2010. Thesis, University of Ontario Institute of Technology. Accessed June 20, 2018. http://hdl.handle.net/10155/104.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Takeva-Velkova, Viliyana. “Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI).” 2010. Web. 20 Jun 2018.

Vancouver:

Takeva-Velkova V. Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI). [Internet] [Thesis]. University of Ontario Institute of Technology; 2010. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/10155/104.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Takeva-Velkova V. Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI). [Thesis]. University of Ontario Institute of Technology; 2010. Available from: http://hdl.handle.net/10155/104

Not specified: Masters Thesis or Doctoral Dissertation

Université Catholique de Louvain

17. Orban de Xivry, François-Xavier. Nearest stable system.

Degree: 2013, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/132586

►

Stability is a universal concept which we experience in our everyday lives. It plays a central role in the study of dynamical systems and is… (more)

Subjects/Keywords: Stability; Dynamical system; Convex optimization; Nonconvex; Nonsmooth

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APA (6^{th} Edition):

Orban de Xivry, F. (2013). Nearest stable system. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/132586

Not specified: Masters Thesis or Doctoral Dissertation

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

Orban de Xivry, François-Xavier. “Nearest stable system.” 2013. Thesis, Université Catholique de Louvain. Accessed June 20, 2018. http://hdl.handle.net/2078.1/132586.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Orban de Xivry, François-Xavier. “Nearest stable system.” 2013. Web. 20 Jun 2018.

Vancouver:

Orban de Xivry F. Nearest stable system. [Internet] [Thesis]. Université Catholique de Louvain; 2013. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/2078.1/132586.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Orban de Xivry F. Nearest stable system. [Thesis]. Université Catholique de Louvain; 2013. Available from: http://hdl.handle.net/2078.1/132586

Not specified: Masters Thesis or Doctoral Dissertation

University of Ghana

18.
Katsekpor, T.
Iterative Methods for Large Scale *Convex* * Optimization*
.

Degree: 2017, University of Ghana

URL: http://ugspace.ug.edu.gh/handle/123456789/23393

► This thesis presents a detailed description and analysis of Bregman’s iterative method for *convex* programming with linear constraints. Row and block action methods for large…
(more)

Subjects/Keywords: Iterative Methods; Large Scale Convex; Optimization

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APA (6^{th} Edition):

Katsekpor, T. (2017). Iterative Methods for Large Scale Convex Optimization . (Doctoral Dissertation). University of Ghana. Retrieved from http://ugspace.ug.edu.gh/handle/123456789/23393

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

Katsekpor, T. “Iterative Methods for Large Scale Convex Optimization .” 2017. Doctoral Dissertation, University of Ghana. Accessed June 20, 2018. http://ugspace.ug.edu.gh/handle/123456789/23393.

MLA Handbook (7^{th} Edition):

Katsekpor, T. “Iterative Methods for Large Scale Convex Optimization .” 2017. Web. 20 Jun 2018.

Vancouver:

Katsekpor T. Iterative Methods for Large Scale Convex Optimization . [Internet] [Doctoral dissertation]. University of Ghana; 2017. [cited 2018 Jun 20]. Available from: http://ugspace.ug.edu.gh/handle/123456789/23393.

Council of Science Editors:

Katsekpor T. Iterative Methods for Large Scale Convex Optimization . [Doctoral Dissertation]. University of Ghana; 2017. Available from: http://ugspace.ug.edu.gh/handle/123456789/23393

Georgia Tech

19.
Lan, Guanghui.
*Convex**optimization* under inexact first-order information.

Degree: PhD, Industrial and Systems Engineering, 2009, Georgia Tech

URL: http://hdl.handle.net/1853/29732

► In this thesis we investigate the design and complexity analysis of the algorithms to solve *convex* programming problems under inexact first-order information. In the first…
(more)

Subjects/Keywords: Convex optimization; Stochastic programming; First-order methods; Uncertainty; Mathematical optimization; Convex functions; First-order logic

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APA (6^{th} Edition):

Lan, G. (2009). Convex optimization under inexact first-order information. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/29732

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

Lan, Guanghui. “Convex optimization under inexact first-order information.” 2009. Doctoral Dissertation, Georgia Tech. Accessed June 20, 2018. http://hdl.handle.net/1853/29732.

MLA Handbook (7^{th} Edition):

Lan, Guanghui. “Convex optimization under inexact first-order information.” 2009. Web. 20 Jun 2018.

Vancouver:

Lan G. Convex optimization under inexact first-order information. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/1853/29732.

Council of Science Editors:

Lan G. Convex optimization under inexact first-order information. [Doctoral Dissertation]. Georgia Tech; 2009. Available from: http://hdl.handle.net/1853/29732

Penn State University

20. Kang, Bosung. Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP).

Degree: PhD, Electrical Engineering, 2015, Penn State University

URL: https://etda.libraries.psu.edu/catalog/26539

► Estimating the disturbance or clutter covariance is a centrally important problem in radar space time adaptive processing (STAP) since estimation of the disturbance or interference…
(more)

Subjects/Keywords: convex optimization; STAP; radar signal processing; constrained optimization; detection and estimation

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APA (6^{th} Edition):

Kang, B. (2015). Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP). (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/26539

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

Kang, Bosung. “Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP).” 2015. Doctoral Dissertation, Penn State University. Accessed June 20, 2018. https://etda.libraries.psu.edu/catalog/26539.

MLA Handbook (7^{th} Edition):

Kang, Bosung. “Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP).” 2015. Web. 20 Jun 2018.

Vancouver:

Kang B. Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP). [Internet] [Doctoral dissertation]. Penn State University; 2015. [cited 2018 Jun 20]. Available from: https://etda.libraries.psu.edu/catalog/26539.

Council of Science Editors:

Kang B. Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP). [Doctoral Dissertation]. Penn State University; 2015. Available from: https://etda.libraries.psu.edu/catalog/26539

Lehigh University

21.
Kuang, Xiaolong.
Conic Programming Approaches for Polynomial *Optimization*: Theory and Applications.

Degree: PhD, Industrial Engineering, 2017, Lehigh University

URL: https://preserve.lehigh.edu/etd/2952

► Historically, polynomials are among the most popular class of functions used for empirical modeling in science and engineering. Polynomials are easy to evaluate, appear naturally…
(more)

Subjects/Keywords: Conic Programming; Convex Optimization; Polynomial Optimization; Industrial Engineering

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APA (6^{th} Edition):

Kuang, X. (2017). Conic Programming Approaches for Polynomial Optimization: Theory and Applications. (Doctoral Dissertation). Lehigh University. Retrieved from https://preserve.lehigh.edu/etd/2952

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

Kuang, Xiaolong. “Conic Programming Approaches for Polynomial Optimization: Theory and Applications.” 2017. Doctoral Dissertation, Lehigh University. Accessed June 20, 2018. https://preserve.lehigh.edu/etd/2952.

MLA Handbook (7^{th} Edition):

Kuang, Xiaolong. “Conic Programming Approaches for Polynomial Optimization: Theory and Applications.” 2017. Web. 20 Jun 2018.

Vancouver:

Kuang X. Conic Programming Approaches for Polynomial Optimization: Theory and Applications. [Internet] [Doctoral dissertation]. Lehigh University; 2017. [cited 2018 Jun 20]. Available from: https://preserve.lehigh.edu/etd/2952.

Council of Science Editors:

Kuang X. Conic Programming Approaches for Polynomial Optimization: Theory and Applications. [Doctoral Dissertation]. Lehigh University; 2017. Available from: https://preserve.lehigh.edu/etd/2952

University of Oxford

22.
Banjac, Goran.
Operator splitting methods for *convex* *optimization* : analysis and implementation.

Degree: PhD, 2018, University of Oxford

URL: https://ora.ox.ac.uk/objects/uuid:17ac73af-9fdf-4cf6-a946-3048da3fc9c2 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740972

► *Convex* *optimization* problems are a class of mathematical problems which arise in numerous applications. Although interior-point methods can in principle solve these problems efficiently, they…
(more)

Subjects/Keywords: Mathematical optimization; Convex optimization; Operator splitting methods; Infeasibility detection; Linear convergence

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APA (6^{th} Edition):

Banjac, G. (2018). Operator splitting methods for convex optimization : analysis and implementation. (Doctoral Dissertation). University of Oxford. Retrieved from https://ora.ox.ac.uk/objects/uuid:17ac73af-9fdf-4cf6-a946-3048da3fc9c2 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740972

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

Banjac, Goran. “Operator splitting methods for convex optimization : analysis and implementation.” 2018. Doctoral Dissertation, University of Oxford. Accessed June 20, 2018. https://ora.ox.ac.uk/objects/uuid:17ac73af-9fdf-4cf6-a946-3048da3fc9c2 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740972.

MLA Handbook (7^{th} Edition):

Banjac, Goran. “Operator splitting methods for convex optimization : analysis and implementation.” 2018. Web. 20 Jun 2018.

Vancouver:

Banjac G. Operator splitting methods for convex optimization : analysis and implementation. [Internet] [Doctoral dissertation]. University of Oxford; 2018. [cited 2018 Jun 20]. Available from: https://ora.ox.ac.uk/objects/uuid:17ac73af-9fdf-4cf6-a946-3048da3fc9c2 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740972.

Council of Science Editors:

Banjac G. Operator splitting methods for convex optimization : analysis and implementation. [Doctoral Dissertation]. University of Oxford; 2018. Available from: https://ora.ox.ac.uk/objects/uuid:17ac73af-9fdf-4cf6-a946-3048da3fc9c2 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740972

University of California – Berkeley

23.
Godwin, Mark Franklin.
Quasi-Newton Algorithms for Non-smooth Online Strongly *Convex* * Optimization*.

Degree: Mechanical Engineering, 2011, University of California – Berkeley

URL: http://www.escholarship.org/uc/item/7fw187gd

► The growing prevalence of networked systems with local sensing and computational capability will result in an increasing array of online and large scale *optimization* problems.…
(more)

Subjects/Keywords: Electrical engineering; Mechanical engineering; Computer science; online convex optimization; quasi-Newton; strongly convex

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APA (6^{th} Edition):

Godwin, M. F. (2011). Quasi-Newton Algorithms for Non-smooth Online Strongly Convex Optimization. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/7fw187gd

Not specified: Masters Thesis or Doctoral Dissertation

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

Godwin, Mark Franklin. “Quasi-Newton Algorithms for Non-smooth Online Strongly Convex Optimization.” 2011. Thesis, University of California – Berkeley. Accessed June 20, 2018. http://www.escholarship.org/uc/item/7fw187gd.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Godwin, Mark Franklin. “Quasi-Newton Algorithms for Non-smooth Online Strongly Convex Optimization.” 2011. Web. 20 Jun 2018.

Vancouver:

Godwin MF. Quasi-Newton Algorithms for Non-smooth Online Strongly Convex Optimization. [Internet] [Thesis]. University of California – Berkeley; 2011. [cited 2018 Jun 20]. Available from: http://www.escholarship.org/uc/item/7fw187gd.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Godwin MF. Quasi-Newton Algorithms for Non-smooth Online Strongly Convex Optimization. [Thesis]. University of California – Berkeley; 2011. Available from: http://www.escholarship.org/uc/item/7fw187gd

Not specified: Masters Thesis or Doctoral Dissertation

Université Catholique de Louvain

24.
Taylor, Adrien.
* Convex* interpolation and performance estimation of first-order methods for

Degree: 2017, Université Catholique de Louvain

URL: http://hdl.handle.net/2078.1/182881

►

The goal of this thesis is to show how to derive in a completely automated way exact and global worst-case guarantees for first-order methods in… (more)

Subjects/Keywords: Convex optimization; Convex analysis; First-order methods; Worst-case analysis; Semidefinite programming; Performance Estimation; Steepest descent; Convex interpolation

Record Details Similar Records

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

APA (6^{th} Edition):

Taylor, A. (2017). Convex interpolation and performance estimation of first-order methods for convex optimization. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/182881

Not specified: Masters Thesis or Doctoral Dissertation

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

Taylor, Adrien. “Convex interpolation and performance estimation of first-order methods for convex optimization.” 2017. Thesis, Université Catholique de Louvain. Accessed June 20, 2018. http://hdl.handle.net/2078.1/182881.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Taylor, Adrien. “Convex interpolation and performance estimation of first-order methods for convex optimization.” 2017. Web. 20 Jun 2018.

Vancouver:

Taylor A. Convex interpolation and performance estimation of first-order methods for convex optimization. [Internet] [Thesis]. Université Catholique de Louvain; 2017. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/2078.1/182881.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Taylor A. Convex interpolation and performance estimation of first-order methods for convex optimization. [Thesis]. Université Catholique de Louvain; 2017. Available from: http://hdl.handle.net/2078.1/182881

Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

25.
Ahmadi, Hesamoddin.
On the Analysis of Data-driven and Distributed Algorithms
for *Convex* *Optimization* Problems.

Degree: PhD, Industrial Engineering, 2016, Penn State University

URL: https://etda.libraries.psu.edu/catalog/29502

► This dissertation considers the resolution of three *optimization* problems. Of these, the first two problems are closely related and focus on solving *optimization* problems in…
(more)

Subjects/Keywords: Optimization and learning; distributed optimization in power system; convex optimization; augmented Lagrangian

Record Details Similar Records

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

APA (6^{th} Edition):

Ahmadi, H. (2016). On the Analysis of Data-driven and Distributed Algorithms for Convex Optimization Problems. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/29502

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

Ahmadi, Hesamoddin. “On the Analysis of Data-driven and Distributed Algorithms for Convex Optimization Problems.” 2016. Doctoral Dissertation, Penn State University. Accessed June 20, 2018. https://etda.libraries.psu.edu/catalog/29502.

MLA Handbook (7^{th} Edition):

Ahmadi, Hesamoddin. “On the Analysis of Data-driven and Distributed Algorithms for Convex Optimization Problems.” 2016. Web. 20 Jun 2018.

Vancouver:

Ahmadi H. On the Analysis of Data-driven and Distributed Algorithms for Convex Optimization Problems. [Internet] [Doctoral dissertation]. Penn State University; 2016. [cited 2018 Jun 20]. Available from: https://etda.libraries.psu.edu/catalog/29502.

Council of Science Editors:

Ahmadi H. On the Analysis of Data-driven and Distributed Algorithms for Convex Optimization Problems. [Doctoral Dissertation]. Penn State University; 2016. Available from: https://etda.libraries.psu.edu/catalog/29502

Carnegie Mellon University

26. 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

Record Details Similar Records

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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 June 20, 2018. 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. 20 Jun 2018.

Vancouver:

Xiong X. Supervised Descent Method. [Internet] [Thesis]. Carnegie Mellon University; 2015. [cited 2018 Jun 20]. 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

University of Minnesota

27. Devulapalli, Raghuveer. Geometric partitioning algorithms for fair division of geographic resources.

Degree: PhD, Industrial and Systems Engineering, 2014, University of Minnesota

URL: http://hdl.handle.net/11299/165305

► This dissertation focuses on a fundamental but under-researched problem: how does one divide a piece of territory into smaller pieces in an efficient way? In…
(more)

Subjects/Keywords: Computational Geometry; Convex Optimization; Geometric Algorithms; Geometric Optimization; Infinite Dimensional Optimization; Operations Research

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APA (6^{th} Edition):

Devulapalli, R. (2014). Geometric partitioning algorithms for fair division of geographic resources. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/165305

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

Devulapalli, Raghuveer. “Geometric partitioning algorithms for fair division of geographic resources.” 2014. Doctoral Dissertation, University of Minnesota. Accessed June 20, 2018. http://hdl.handle.net/11299/165305.

MLA Handbook (7^{th} Edition):

Devulapalli, Raghuveer. “Geometric partitioning algorithms for fair division of geographic resources.” 2014. Web. 20 Jun 2018.

Vancouver:

Devulapalli R. Geometric partitioning algorithms for fair division of geographic resources. [Internet] [Doctoral dissertation]. University of Minnesota; 2014. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/11299/165305.

Council of Science Editors:

Devulapalli R. Geometric partitioning algorithms for fair division of geographic resources. [Doctoral Dissertation]. University of Minnesota; 2014. Available from: http://hdl.handle.net/11299/165305

University of Western Ontario

28. Baxter, John SH. Contributions of Continuous Max-Flow Theory to Medical Image Processing.

Degree: 2017, University of Western Ontario

URL: https://ir.lib.uwo.ca/etd/4602

► Discrete graph cuts and continuous max-flow theory have created a paradigm shift in many areas of medical image processing. As previous methods limited themselves to…
(more)

Subjects/Keywords: optimization-based segmentation; image enhancement; variational optimization; convex optimization; Biomedical Engineering and Bioengineering

Record Details Similar Records

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APA (6^{th} Edition):

Baxter, J. S. (2017). Contributions of Continuous Max-Flow Theory to Medical Image Processing. (Thesis). University of Western Ontario. Retrieved from https://ir.lib.uwo.ca/etd/4602

Not specified: Masters Thesis or Doctoral Dissertation

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

Baxter, John SH. “Contributions of Continuous Max-Flow Theory to Medical Image Processing.” 2017. Thesis, University of Western Ontario. Accessed June 20, 2018. https://ir.lib.uwo.ca/etd/4602.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Baxter, John SH. “Contributions of Continuous Max-Flow Theory to Medical Image Processing.” 2017. Web. 20 Jun 2018.

Vancouver:

Baxter JS. Contributions of Continuous Max-Flow Theory to Medical Image Processing. [Internet] [Thesis]. University of Western Ontario; 2017. [cited 2018 Jun 20]. Available from: https://ir.lib.uwo.ca/etd/4602.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Baxter JS. Contributions of Continuous Max-Flow Theory to Medical Image Processing. [Thesis]. University of Western Ontario; 2017. Available from: https://ir.lib.uwo.ca/etd/4602

Not specified: Masters Thesis or Doctoral Dissertation

Northeastern University

29.
Cheng, Yongfang.
Robust model fitting via *convex* *optimization* techniques.

Degree: PhD, Department of Electrical and Computer Engineering, 2016, Northeastern University

URL: http://hdl.handle.net/2047/D20236918

► The past few years have witnessed an unprecedented growth in data acquisition capabilities due to the tremendous advances in information sensing. Although these developments have…
(more)

Subjects/Keywords: convex optimization; model fitting; model invalidation; robust; subspace clustering; Mathematical optimization; Convex functions; Cluster analysis; Regression analysis; Mathematical models

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

APA (6^{th} Edition):

Cheng, Y. (2016). Robust model fitting via convex optimization techniques. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20236918

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

Cheng, Yongfang. “Robust model fitting via convex optimization techniques.” 2016. Doctoral Dissertation, Northeastern University. Accessed June 20, 2018. http://hdl.handle.net/2047/D20236918.

MLA Handbook (7^{th} Edition):

Cheng, Yongfang. “Robust model fitting via convex optimization techniques.” 2016. Web. 20 Jun 2018.

Vancouver:

Cheng Y. Robust model fitting via convex optimization techniques. [Internet] [Doctoral dissertation]. Northeastern University; 2016. [cited 2018 Jun 20]. Available from: http://hdl.handle.net/2047/D20236918.

Council of Science Editors:

Cheng Y. Robust model fitting via convex optimization techniques. [Doctoral Dissertation]. Northeastern University; 2016. Available from: http://hdl.handle.net/2047/D20236918

University of Minnesota

30.
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

Record Details Similar Records

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

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 June 20, 2018. 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. 20 Jun 2018.

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 2018 Jun 20]. 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