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You searched for subject:(Convex Optimization). Showing records 1 – 30 of 429 total matches.

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

Degree: 2014, INFLIBNET

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

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

APA (6th 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 (16th Edition):

Uthayakumar, R. “Study on convergence of optimization problems;.” 2014. Thesis, INFLIBNET. Accessed October 15, 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 (7th Edition):

Uthayakumar, R. “Study on convergence of optimization problems;.” 2014. Web. 15 Oct 2019.

Vancouver:

Uthayakumar R. Study on convergence of optimization problems;. [Internet] [Thesis]. INFLIBNET; 2014. [cited 2019 Oct 15]. 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

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

 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

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APA (6th 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 (16th Edition):

Jellyman, Dayle Raymond. “Convex Optimization for Distributed Acoustic Beamforming.” 2017. Masters Thesis, Victoria University of Wellington. Accessed October 15, 2019. http://hdl.handle.net/10063/6650.

MLA Handbook (7th Edition):

Jellyman, Dayle Raymond. “Convex Optimization for Distributed Acoustic Beamforming.” 2017. Web. 15 Oct 2019.

Vancouver:

Jellyman DR. Convex Optimization for Distributed Acoustic Beamforming. [Internet] [Masters thesis]. Victoria University of Wellington; 2017. [cited 2019 Oct 15]. 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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhang, Shu-Bin. “Study on Digital Filter Design and Coefficient Quantization.” 2011. Thesis, NSYSU. Accessed October 15, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727111-135237.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhang, Shu-Bin. “Study on Digital Filter Design and Coefficient Quantization.” 2011. Web. 15 Oct 2019.

Vancouver:

Zhang S. Study on Digital Filter Design and Coefficient Quantization. [Internet] [Thesis]. NSYSU; 2011. [cited 2019 Oct 15]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727111-135237.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Southern California

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

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

 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 (6th 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/5355

Chicago Manual of Style (16th Edition):

Taghavi, Soraya. “Quantum computation and optimized error correction.” 2010. Doctoral Dissertation, University of Southern California. Accessed October 15, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/308762/rec/5355.

MLA Handbook (7th Edition):

Taghavi, Soraya. “Quantum computation and optimized error correction.” 2010. Web. 15 Oct 2019.

Vancouver:

Taghavi S. Quantum computation and optimized error correction. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2019 Oct 15]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/308762/rec/5355.

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/5355


Université Catholique de Louvain

5. Martin, Benoît. Autonomous microgrids for rural electrification : joint investment planning of power generation and distribution through convex optimization.

Degree: 2018, Université Catholique de Louvain

Autonomous microgrid planning requires to consider both investments in network and generation assets as there is no connection to another power system. In this problem,… (more)

Subjects/Keywords: Planning; Microgrid; Convex optimization

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APA (6th Edition):

Martin, B. (2018). Autonomous microgrids for rural electrification : joint investment planning of power generation and distribution through convex optimization. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/214246

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Martin, Benoît. “Autonomous microgrids for rural electrification : joint investment planning of power generation and distribution through convex optimization.” 2018. Thesis, Université Catholique de Louvain. Accessed October 15, 2019. http://hdl.handle.net/2078.1/214246.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Martin, Benoît. “Autonomous microgrids for rural electrification : joint investment planning of power generation and distribution through convex optimization.” 2018. Web. 15 Oct 2019.

Vancouver:

Martin B. Autonomous microgrids for rural electrification : joint investment planning of power generation and distribution through convex optimization. [Internet] [Thesis]. Université Catholique de Louvain; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/2078.1/214246.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Martin B. Autonomous microgrids for rural electrification : joint investment planning of power generation and distribution through convex optimization. [Thesis]. Université Catholique de Louvain; 2018. Available from: http://hdl.handle.net/2078.1/214246

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

6. Cosentino, Alessandro. Quantum State Local Distinguishability via Convex Optimization.

Degree: 2015, University of Waterloo

 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

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APA (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Cosentino, Alessandro. “Quantum State Local Distinguishability via Convex Optimization.” 2015. Thesis, University of Waterloo. Accessed October 15, 2019. http://hdl.handle.net/10012/9572.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Cosentino, Alessandro. “Quantum State Local Distinguishability via Convex Optimization.” 2015. Web. 15 Oct 2019.

Vancouver:

Cosentino A. Quantum State Local Distinguishability via Convex Optimization. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10012/9572.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rutgers University

7. Yao, Wang, 1985-. Approximate versions of the alternating direction method of multipliers.

Degree: PhD, Operations Research, 2016, Rutgers University

Convex optimization is at the core of many of today's analysis tools for large datasets, and in particular machine learning methods. This thesis will develop… (more)

Subjects/Keywords: Mathematical optimization; Convex functions

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APA (6th Edition):

Yao, Wang, 1. (2016). Approximate versions of the alternating direction method of multipliers. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/51517/

Chicago Manual of Style (16th Edition):

Yao, Wang, 1985-. “Approximate versions of the alternating direction method of multipliers.” 2016. Doctoral Dissertation, Rutgers University. Accessed October 15, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/51517/.

MLA Handbook (7th Edition):

Yao, Wang, 1985-. “Approximate versions of the alternating direction method of multipliers.” 2016. Web. 15 Oct 2019.

Vancouver:

Yao, Wang 1. Approximate versions of the alternating direction method of multipliers. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2019 Oct 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/51517/.

Council of Science Editors:

Yao, Wang 1. Approximate versions of the alternating direction method of multipliers. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/51517/


University of Texas – Austin

8. Wang, Ye, Ph. D. Novel convex optimization techniques for circuit analysis and synthesis.

Degree: PhD, Electrical and Computer Engineering, 2018, University of Texas – Austin

 Technology scaling brings about the need for computationally efficient methods for circuit analysis, optimization, and synthesis. Convex optimization is a special class of mathematical optimization(more)

Subjects/Keywords: Convex optimization; EDA problems

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APA (6th Edition):

Wang, Ye, P. D. (2018). Novel convex optimization techniques for circuit analysis and synthesis. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/67661

Chicago Manual of Style (16th Edition):

Wang, Ye, Ph D. “Novel convex optimization techniques for circuit analysis and synthesis.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed October 15, 2019. http://hdl.handle.net/2152/67661.

MLA Handbook (7th Edition):

Wang, Ye, Ph D. “Novel convex optimization techniques for circuit analysis and synthesis.” 2018. Web. 15 Oct 2019.

Vancouver:

Wang, Ye PD. Novel convex optimization techniques for circuit analysis and synthesis. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/2152/67661.

Council of Science Editors:

Wang, Ye PD. Novel convex optimization techniques for circuit analysis and synthesis. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/67661


University of Texas – Austin

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

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

 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

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APA (6th Edition):

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

Chicago Manual of Style (16th Edition):

Berning, Andrew Walter, Jr. “Verification of successive convexification algorithm.” 2016. Masters Thesis, University of Texas – Austin. Accessed October 15, 2019. http://hdl.handle.net/2152/41579.

MLA Handbook (7th Edition):

Berning, Andrew Walter, Jr. “Verification of successive convexification algorithm.” 2016. Web. 15 Oct 2019.

Vancouver:

Berning, Andrew Walter J. Verification of successive convexification algorithm. [Internet] [Masters thesis]. University of Texas – Austin; 2016. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/2152/41579.

Council of Science Editors:

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


Princeton University

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

Degree: PhD, 2017, Princeton University

 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 (6th 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 (16th Edition):

Ma, Tengyu. “Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding .” 2017. Doctoral Dissertation, Princeton University. Accessed October 15, 2019. http://arks.princeton.edu/ark:/88435/dsp01th83m199d.

MLA Handbook (7th Edition):

Ma, Tengyu. “Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding .” 2017. Web. 15 Oct 2019.

Vancouver:

Ma T. Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Oct 15]. 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 Ontario Institute of Technology

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

Degree: 2010, University of Ontario Institute of Technology

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Takeva-Velkova, Viliyana. “Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI).” 2010. Thesis, University of Ontario Institute of Technology. Accessed October 15, 2019. http://hdl.handle.net/10155/104.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Takeva-Velkova, Viliyana. “Optimization algorithms in compressive sensing (CS) sparse magnetic resonance imaging (MRI).” 2010. Web. 15 Oct 2019.

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 2019 Oct 15]. Available from: http://hdl.handle.net/10155/104.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Ghana

12. Katsekpor, T. Iterative Methods for Large Scale Convex Optimization .

Degree: 2017, University of Ghana

 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 (6th 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 (16th Edition):

Katsekpor, T. “Iterative Methods for Large Scale Convex Optimization .” 2017. Doctoral Dissertation, University of Ghana. Accessed October 15, 2019. http://ugspace.ug.edu.gh/handle/123456789/23393.

MLA Handbook (7th Edition):

Katsekpor, T. “Iterative Methods for Large Scale Convex Optimization .” 2017. Web. 15 Oct 2019.

Vancouver:

Katsekpor T. Iterative Methods for Large Scale Convex Optimization . [Internet] [Doctoral dissertation]. University of Ghana; 2017. [cited 2019 Oct 15]. 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


Universidade Nova

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

Degree: 2013, Universidade Nova

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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Soares, Diogo Lopes. “Design of multidimensional compact constellations with high power efficiency.” 2013. Thesis, Universidade Nova. Accessed October 15, 2019. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11111.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Soares, Diogo Lopes. “Design of multidimensional compact constellations with high power efficiency.” 2013. Web. 15 Oct 2019.

Vancouver:

Soares DL. Design of multidimensional compact constellations with high power efficiency. [Internet] [Thesis]. Universidade Nova; 2013. [cited 2019 Oct 15]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/11111.

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

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

 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

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APA (6th 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 (16th Edition):

Zhang, H M. “Distributed Convex Optimization: A Study on the Primal-Dual Method of Multipliers:.” 2015. Masters Thesis, Delft University of Technology. Accessed October 15, 2019. http://resolver.tudelft.nl/uuid:932db0bb-da4c-4ffe-892a-036d01a8071b.

MLA Handbook (7th Edition):

Zhang, H M. “Distributed Convex Optimization: A Study on the Primal-Dual Method of Multipliers:.” 2015. Web. 15 Oct 2019.

Vancouver:

Zhang HM. Distributed Convex Optimization: A Study on the Primal-Dual Method of Multipliers:. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2019 Oct 15]. 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


Iowa State University

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

Degree: 2013, Iowa State University

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Li, Chong. “Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds.” 2013. Thesis, Iowa State University. Accessed October 15, 2019. https://lib.dr.iastate.edu/etd/13421.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Li, Chong. “Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds.” 2013. Web. 15 Oct 2019.

Vancouver:

Li C. Fundamental limitations on communication channels with noisy feedback: information flow, capacity and bounds. [Internet] [Thesis]. Iowa State University; 2013. [cited 2019 Oct 15]. Available from: https://lib.dr.iastate.edu/etd/13421.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université Catholique de Louvain

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

Degree: 2013, Université Catholique de Louvain

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 (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Orban de Xivry, François-Xavier. “Nearest stable system.” 2013. Thesis, Université Catholique de Louvain. Accessed October 15, 2019. http://hdl.handle.net/2078.1/132586.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Orban de Xivry, François-Xavier. “Nearest stable system.” 2013. Web. 15 Oct 2019.

Vancouver:

Orban de Xivry F. Nearest stable system. [Internet] [Thesis]. Université Catholique de Louvain; 2013. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/2078.1/132586.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Waterloo

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

Degree: 2017, University of Waterloo

 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

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APA (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Karimi, Mehdi. “Convex Optimization via Domain-Driven Barriers and Primal-Dual Interior-Point Methods.” 2017. Thesis, University of Waterloo. Accessed October 15, 2019. http://hdl.handle.net/10012/12209.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Karimi, Mehdi. “Convex Optimization via Domain-Driven Barriers and Primal-Dual Interior-Point Methods.” 2017. Web. 15 Oct 2019.

Vancouver:

Karimi M. Convex Optimization via Domain-Driven Barriers and Primal-Dual Interior-Point Methods. [Internet] [Thesis]. University of Waterloo; 2017. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10012/12209.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Minnesota

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

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

 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

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APA (6th 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 (16th Edition):

Choi, Hyungjin. “Quantication of the Impact of Uncertainty in Power Systems using Convex Optimization.” 2017. Doctoral Dissertation, University of Minnesota. Accessed October 15, 2019. http://hdl.handle.net/11299/190457.

MLA Handbook (7th Edition):

Choi, Hyungjin. “Quantication of the Impact of Uncertainty in Power Systems using Convex Optimization.” 2017. Web. 15 Oct 2019.

Vancouver:

Choi H. Quantication of the Impact of Uncertainty in Power Systems using Convex Optimization. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2019 Oct 15]. 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

19. Umenberger, Jack. Convex Identifcation of Stable Dynamical Systems .

Degree: 2017, University of Sydney

 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

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APA (6th Edition):

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Umenberger, Jack. “Convex Identifcation of Stable Dynamical Systems .” 2017. Thesis, University of Sydney. Accessed October 15, 2019. http://hdl.handle.net/2123/17321.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Umenberger, Jack. “Convex Identifcation of Stable Dynamical Systems .” 2017. Web. 15 Oct 2019.

Vancouver:

Umenberger J. Convex Identifcation of Stable Dynamical Systems . [Internet] [Thesis]. University of Sydney; 2017. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/2123/17321.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Tartu University

20. Makkeh, Abdullah. Applications of optimization in some complex systems .

Degree: 2018, Tartu University

 Matemaatiline optimeerimine on optimeerimisülesandele optimaalse lahendi leidmine, see tähendab, et leitakse reaalarvuliste väärtustega funktsiooni (eesmärkfunktsiooni) teatud kitsendusi rahuldav maksimaalne või minimaalne väärtus. Matemaatiline optimeerimine on… (more)

Subjects/Keywords: complex systems; optimization; convex sets; software

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

APA (6th Edition):

Makkeh, A. (2018). Applications of optimization in some complex systems . (Thesis). Tartu University. Retrieved from http://hdl.handle.net/10062/61143

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Makkeh, Abdullah. “Applications of optimization in some complex systems .” 2018. Thesis, Tartu University. Accessed October 15, 2019. http://hdl.handle.net/10062/61143.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Makkeh, Abdullah. “Applications of optimization in some complex systems .” 2018. Web. 15 Oct 2019.

Vancouver:

Makkeh A. Applications of optimization in some complex systems . [Internet] [Thesis]. Tartu University; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10062/61143.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Makkeh A. Applications of optimization in some complex systems . [Thesis]. Tartu University; 2018. Available from: http://hdl.handle.net/10062/61143

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Loughborough University

21. Rossetti, Gaia. Mathematical optimization techniques for cognitive radar networks.

Degree: PhD, 2018, Loughborough University

 This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from… (more)

Subjects/Keywords: Waveform optimization; Convex optimization; Robust optimization; Cognitive radars

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APA (6th Edition):

Rossetti, G. (2018). Mathematical optimization techniques for cognitive radar networks. (Doctoral Dissertation). Loughborough University. Retrieved from https://dspace.lboro.ac.uk/2134/33419 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747937

Chicago Manual of Style (16th Edition):

Rossetti, Gaia. “Mathematical optimization techniques for cognitive radar networks.” 2018. Doctoral Dissertation, Loughborough University. Accessed October 15, 2019. https://dspace.lboro.ac.uk/2134/33419 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747937.

MLA Handbook (7th Edition):

Rossetti, Gaia. “Mathematical optimization techniques for cognitive radar networks.” 2018. Web. 15 Oct 2019.

Vancouver:

Rossetti G. Mathematical optimization techniques for cognitive radar networks. [Internet] [Doctoral dissertation]. Loughborough University; 2018. [cited 2019 Oct 15]. Available from: https://dspace.lboro.ac.uk/2134/33419 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747937.

Council of Science Editors:

Rossetti G. Mathematical optimization techniques for cognitive radar networks. [Doctoral Dissertation]. Loughborough University; 2018. Available from: https://dspace.lboro.ac.uk/2134/33419 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.747937


University of Waterloo

22. Linhares Rodrigues, Andre. Approximation Algorithms for Distributionally Robust Stochastic Optimization.

Degree: 2019, University of Waterloo

 Two-stage stochastic optimization is a widely used framework for modeling uncertainty, where we have a probability distribution over possible realizations of the data, called scenarios,… (more)

Subjects/Keywords: approximation algorithms; stochastic optimization; discrete optimization; convex optimization

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APA (6th Edition):

Linhares Rodrigues, A. (2019). Approximation Algorithms for Distributionally Robust Stochastic Optimization. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14639

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Linhares Rodrigues, Andre. “Approximation Algorithms for Distributionally Robust Stochastic Optimization.” 2019. Thesis, University of Waterloo. Accessed October 15, 2019. http://hdl.handle.net/10012/14639.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Linhares Rodrigues, Andre. “Approximation Algorithms for Distributionally Robust Stochastic Optimization.” 2019. Web. 15 Oct 2019.

Vancouver:

Linhares Rodrigues A. Approximation Algorithms for Distributionally Robust Stochastic Optimization. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10012/14639.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Linhares Rodrigues A. Approximation Algorithms for Distributionally Robust Stochastic Optimization. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14639

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Loughborough University

23. Rossetti, Gaia. Mathematical optimization techniques for cognitive radar networks.

Degree: PhD, 2018, Loughborough University

 This thesis discusses mathematical optimization techniques for waveform design in cognitive radars. These techniques have been designed with an increasing level of sophistication, starting from… (more)

Subjects/Keywords: Waveform optimization; Convex optimization; Robust optimization; Cognitive radars

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

APA (6th Edition):

Rossetti, G. (2018). Mathematical optimization techniques for cognitive radar networks. (Doctoral Dissertation). Loughborough University. Retrieved from http://hdl.handle.net/2134/33419

Chicago Manual of Style (16th Edition):

Rossetti, Gaia. “Mathematical optimization techniques for cognitive radar networks.” 2018. Doctoral Dissertation, Loughborough University. Accessed October 15, 2019. http://hdl.handle.net/2134/33419.

MLA Handbook (7th Edition):

Rossetti, Gaia. “Mathematical optimization techniques for cognitive radar networks.” 2018. Web. 15 Oct 2019.

Vancouver:

Rossetti G. Mathematical optimization techniques for cognitive radar networks. [Internet] [Doctoral dissertation]. Loughborough University; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/2134/33419.

Council of Science Editors:

Rossetti G. Mathematical optimization techniques for cognitive radar networks. [Doctoral Dissertation]. Loughborough University; 2018. Available from: http://hdl.handle.net/2134/33419


Georgia Tech

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

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

 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 (6th 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 (16th Edition):

Lan, Guanghui. “Convex optimization under inexact first-order information.” 2009. Doctoral Dissertation, Georgia Tech. Accessed October 15, 2019. http://hdl.handle.net/1853/29732.

MLA Handbook (7th Edition):

Lan, Guanghui. “Convex optimization under inexact first-order information.” 2009. Web. 15 Oct 2019.

Vancouver:

Lan G. Convex optimization under inexact first-order information. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2019 Oct 15]. 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

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

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

 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 (6th 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 (16th Edition):

Kang, Bosung. “Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP).” 2015. Doctoral Dissertation, Penn State University. Accessed October 15, 2019. https://etda.libraries.psu.edu/catalog/26539.

MLA Handbook (7th Edition):

Kang, Bosung. “Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP).” 2015. Web. 15 Oct 2019.

Vancouver:

Kang B. Robust Covariance Matrix Estimation for Radar Space-Time Adaptive Processing (STAP). [Internet] [Doctoral dissertation]. Penn State University; 2015. [cited 2019 Oct 15]. 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


University of Oxford

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

Degree: PhD, 2018, University of Oxford

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 (6th 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 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740972

Chicago Manual of Style (16th Edition):

Banjac, Goran. “Operator splitting methods for convex optimization : analysis and implementation.” 2018. Doctoral Dissertation, University of Oxford. Accessed October 15, 2019. https://ora.ox.ac.uk/objects/uuid:17ac73af-9fdf-4cf6-a946-3048da3fc9c2 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740972.

MLA Handbook (7th Edition):

Banjac, Goran. “Operator splitting methods for convex optimization : analysis and implementation.” 2018. Web. 15 Oct 2019.

Vancouver:

Banjac G. Operator splitting methods for convex optimization : analysis and implementation. [Internet] [Doctoral dissertation]. University of Oxford; 2018. [cited 2019 Oct 15]. Available from: https://ora.ox.ac.uk/objects/uuid:17ac73af-9fdf-4cf6-a946-3048da3fc9c2 ; https://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 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.740972


Lehigh University

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

Degree: PhD, Industrial Engineering, 2017, Lehigh University

 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 (6th 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 (16th Edition):

Kuang, Xiaolong. “Conic Programming Approaches for Polynomial Optimization: Theory and Applications.” 2017. Doctoral Dissertation, Lehigh University. Accessed October 15, 2019. https://preserve.lehigh.edu/etd/2952.

MLA Handbook (7th Edition):

Kuang, Xiaolong. “Conic Programming Approaches for Polynomial Optimization: Theory and Applications.” 2017. Web. 15 Oct 2019.

Vancouver:

Kuang X. Conic Programming Approaches for Polynomial Optimization: Theory and Applications. [Internet] [Doctoral dissertation]. Lehigh University; 2017. [cited 2019 Oct 15]. 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


Northeastern University

28. Moharrer, Armin. Distributing Frank-Wolfe via map-reduce.

Degree: MS, Department of Electrical and Computer Engineering, 2018, Northeastern University

 Large-scale optimization problems abound in data mining and machine learning applications, and the computational challenges they pose are often addressed through parallelization. We identify structural… (more)

Subjects/Keywords: convex optimization; distributed algorithms; distributed optimization; Frank-Wolfe; map-reduce; spark

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APA (6th Edition):

Moharrer, A. (2018). Distributing Frank-Wolfe via map-reduce. (Masters Thesis). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20290439

Chicago Manual of Style (16th Edition):

Moharrer, Armin. “Distributing Frank-Wolfe via map-reduce.” 2018. Masters Thesis, Northeastern University. Accessed October 15, 2019. http://hdl.handle.net/2047/D20290439.

MLA Handbook (7th Edition):

Moharrer, Armin. “Distributing Frank-Wolfe via map-reduce.” 2018. Web. 15 Oct 2019.

Vancouver:

Moharrer A. Distributing Frank-Wolfe via map-reduce. [Internet] [Masters thesis]. Northeastern University; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/2047/D20290439.

Council of Science Editors:

Moharrer A. Distributing Frank-Wolfe via map-reduce. [Masters Thesis]. Northeastern University; 2018. Available from: http://hdl.handle.net/2047/D20290439


Texas A&M University

29. Wang, Xu. Contributions to Resource Allocation in Cognitive Radio Networks.

Degree: PhD, Electrical Engineering, 2017, Texas A&M University

 The continuous increase in the number of wireless devices and the huge demand for higher data rates have promoted the development of new wireless communications… (more)

Subjects/Keywords: Cognitive radio; resource allocation; spectrum sensing; convex optimization; monotonic optimization

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APA (6th Edition):

Wang, X. (2017). Contributions to Resource Allocation in Cognitive Radio Networks. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/165774

Chicago Manual of Style (16th Edition):

Wang, Xu. “Contributions to Resource Allocation in Cognitive Radio Networks.” 2017. Doctoral Dissertation, Texas A&M University. Accessed October 15, 2019. http://hdl.handle.net/1969.1/165774.

MLA Handbook (7th Edition):

Wang, Xu. “Contributions to Resource Allocation in Cognitive Radio Networks.” 2017. Web. 15 Oct 2019.

Vancouver:

Wang X. Contributions to Resource Allocation in Cognitive Radio Networks. [Internet] [Doctoral dissertation]. Texas A&M University; 2017. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/1969.1/165774.

Council of Science Editors:

Wang X. Contributions to Resource Allocation in Cognitive Radio Networks. [Doctoral Dissertation]. Texas A&M University; 2017. Available from: http://hdl.handle.net/1969.1/165774


University of California – Berkeley

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

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

 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 (6th 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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Godwin, Mark Franklin. “Quasi-Newton Algorithms for Non-smooth Online Strongly Convex Optimization.” 2011. Thesis, University of California – Berkeley. Accessed October 15, 2019. http://www.escholarship.org/uc/item/7fw187gd.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Godwin, Mark Franklin. “Quasi-Newton Algorithms for Non-smooth Online Strongly Convex Optimization.” 2011. Web. 15 Oct 2019.

Vancouver:

Godwin MF. Quasi-Newton Algorithms for Non-smooth Online Strongly Convex Optimization. [Internet] [Thesis]. University of California – Berkeley; 2011. [cited 2019 Oct 15]. Available from: http://www.escholarship.org/uc/item/7fw187gd.

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

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

[1] [2] [3] [4] [5] … [15]

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