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Georgia Tech

1. Zhou, Yi. Stochastic algorithms for distributed optimization and machine learning.

Degree: PhD, Industrial and Systems Engineering, 2018, Georgia Tech

URL: http://hdl.handle.net/1853/60256

► In the big data era, machine learning acts as a powerful tool to help us make predictions and decisions. It has strong ties to the…
(more)

Subjects/Keywords: Randomized algorithms; Stochastic optimization; Distributed optimization; Machine learning; Distributed machine learning; Finite-sum optimization

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APA (6^{th} Edition):

Zhou, Y. (2018). Stochastic algorithms for distributed optimization and machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60256

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

Zhou, Yi. “Stochastic algorithms for distributed optimization and machine learning.” 2018. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/60256.

MLA Handbook (7^{th} Edition):

Zhou, Yi. “Stochastic algorithms for distributed optimization and machine learning.” 2018. Web. 06 May 2021.

Vancouver:

Zhou Y. Stochastic algorithms for distributed optimization and machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/60256.

Council of Science Editors:

Zhou Y. Stochastic algorithms for distributed optimization and machine learning. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60256

Georgia Tech

2. Curry, Stewart. Statistical inference for optimization models: Sensitivity analysis and uncertainty quantification.

Degree: PhD, Industrial and Systems Engineering, 2019, Georgia Tech

URL: http://hdl.handle.net/1853/62265

► In recent years, the optimization, statistics and machine learning communities have built momentum in bridging methodologies across domains by developing solutions to challenging optimization problems…
(more)

Subjects/Keywords: Linear programming; Sensitivity analysis; Parametric programming; Tolerance sensitivity; Stochastic programming; Simplex method; Statistical inference; Bayesian statistics; Uncertainty quantification; Dental care access; Healthcare access; Quadratic programming

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APA (6^{th} Edition):

Curry, S. (2019). Statistical inference for optimization models: Sensitivity analysis and uncertainty quantification. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62265

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

Curry, Stewart. “Statistical inference for optimization models: Sensitivity analysis and uncertainty quantification.” 2019. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/62265.

MLA Handbook (7^{th} Edition):

Curry, Stewart. “Statistical inference for optimization models: Sensitivity analysis and uncertainty quantification.” 2019. Web. 06 May 2021.

Vancouver:

Curry S. Statistical inference for optimization models: Sensitivity analysis and uncertainty quantification. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/62265.

Council of Science Editors:

Curry S. Statistical inference for optimization models: Sensitivity analysis and uncertainty quantification. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62265

Georgia Tech

3. Ainsworth, Nathan Grey. Towards a distributed control regime for robust synchronization and power sharing of inverter-based ac power networks.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/53985

► The objective of the proposed research is 1) to develop a general dynamic condition sufficient to ensure frequency synchronization of inverter-based AC power networks, and…
(more)

Subjects/Keywords: Power systems; Control systems

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APA (6^{th} Edition):

Ainsworth, N. G. (2014). Towards a distributed control regime for robust synchronization and power sharing of inverter-based ac power networks. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53985

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

Ainsworth, Nathan Grey. “Towards a distributed control regime for robust synchronization and power sharing of inverter-based ac power networks.” 2014. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/53985.

MLA Handbook (7^{th} Edition):

Ainsworth, Nathan Grey. “Towards a distributed control regime for robust synchronization and power sharing of inverter-based ac power networks.” 2014. Web. 06 May 2021.

Vancouver:

Ainsworth NG. Towards a distributed control regime for robust synchronization and power sharing of inverter-based ac power networks. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/53985.

Council of Science Editors:

Ainsworth NG. Towards a distributed control regime for robust synchronization and power sharing of inverter-based ac power networks. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/53985

Georgia Tech

4. Lorca Galvez, Alvaro Hugo. Robust optimization for renewable energy integration in power system operations.

Degree: PhD, Industrial and Systems Engineering, 2016, Georgia Tech

URL: http://hdl.handle.net/1853/55653

► Optimization provides critical support for the operation of electric power systems. As power systems evolve, enhanced operational methodologies are required, and innovative optimization models have…
(more)

Subjects/Keywords: Robust optimization; Power system operations; Renewable energy

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APA (6^{th} Edition):

Lorca Galvez, A. H. (2016). Robust optimization for renewable energy integration in power system operations. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55653

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

Lorca Galvez, Alvaro Hugo. “Robust optimization for renewable energy integration in power system operations.” 2016. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/55653.

MLA Handbook (7^{th} Edition):

Lorca Galvez, Alvaro Hugo. “Robust optimization for renewable energy integration in power system operations.” 2016. Web. 06 May 2021.

Vancouver:

Lorca Galvez AH. Robust optimization for renewable energy integration in power system operations. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/55653.

Council of Science Editors:

Lorca Galvez AH. Robust optimization for renewable energy integration in power system operations. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55653

Georgia Tech

5. Feizollahi, Mohammadjavad. Large-scale unit commitment: Decentralized mixed integer programming approaches.

Degree: PhD, Industrial and Systems Engineering, 2015, Georgia Tech

URL: http://hdl.handle.net/1853/56169

► We investigate theory and application of decentralized optimization for mixed integer programming (MIP) problems. Our focus is on loosely coupled MIPs where different blocks of…
(more)

Subjects/Keywords: Decentralized optimization; Augmented Lagrangian; Unit commitment; Mixed integer programming

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APA (6^{th} Edition):

Feizollahi, M. (2015). Large-scale unit commitment: Decentralized mixed integer programming approaches. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56169

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

Feizollahi, Mohammadjavad. “Large-scale unit commitment: Decentralized mixed integer programming approaches.” 2015. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/56169.

MLA Handbook (7^{th} Edition):

Feizollahi, Mohammadjavad. “Large-scale unit commitment: Decentralized mixed integer programming approaches.” 2015. Web. 06 May 2021.

Vancouver:

Feizollahi M. Large-scale unit commitment: Decentralized mixed integer programming approaches. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/56169.

Council of Science Editors:

Feizollahi M. Large-scale unit commitment: Decentralized mixed integer programming approaches. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/56169

Georgia Tech

6. Suk, Tonghoon. Resource allocation algorithms in stochastic systems.

Degree: PhD, Industrial and Systems Engineering, 2016, Georgia Tech

URL: http://hdl.handle.net/1853/56341

► My dissertation work examines resource allocation algorithms in stochastic systems. I use applied probability methodology to investigate large-scaled stochastic systems. Specifically, my research focuses on…
(more)

Subjects/Keywords: Queueing systems; Scheduling algorithms

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APA (6^{th} Edition):

Suk, T. (2016). Resource allocation algorithms in stochastic systems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/56341

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

Suk, Tonghoon. “Resource allocation algorithms in stochastic systems.” 2016. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/56341.

MLA Handbook (7^{th} Edition):

Suk, Tonghoon. “Resource allocation algorithms in stochastic systems.” 2016. Web. 06 May 2021.

Vancouver:

Suk T. Resource allocation algorithms in stochastic systems. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/56341.

Council of Science Editors:

Suk T. Resource allocation algorithms in stochastic systems. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/56341

Georgia Tech

7. Zhou, Zhiqiang. Theory and applications of first-order methods for convex optimization with function constraints.

Degree: PhD, Industrial and Systems Engineering, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/63664

► This dissertation focuses on the development of efficient first-order methods for function constrained convex optimization and their applications in a few different areas, including healthcare,…
(more)

Subjects/Keywords: First-order methods; Function constrained optimization; Machine learning

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APA (6^{th} Edition):

Zhou, Z. (2020). Theory and applications of first-order methods for convex optimization with function constraints. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63664

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

Zhou, Zhiqiang. “Theory and applications of first-order methods for convex optimization with function constraints.” 2020. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/63664.

MLA Handbook (7^{th} Edition):

Zhou, Zhiqiang. “Theory and applications of first-order methods for convex optimization with function constraints.” 2020. Web. 06 May 2021.

Vancouver:

Zhou Z. Theory and applications of first-order methods for convex optimization with function constraints. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/63664.

Council of Science Editors:

Zhou Z. Theory and applications of first-order methods for convex optimization with function constraints. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63664

Georgia Tech

8. Boob, Digvijay Pravin. Convex and structured nonconvex optimization for modern machine learning: Complexity and algorithms.

Degree: PhD, Industrial and Systems Engineering, 2020, Georgia Tech

URL: http://hdl.handle.net/1853/63673

► In this thesis, we investigate various optimization problems motivated by applications in modern-day machine learning. In the first part, we look at the computational complexity…
(more)

Subjects/Keywords: Computational complexity; NP-hardness; Function constrained optimization; Convex composite optimization; Nonconvex composite optimization; Stochastic optimization; Sparse-constrained nonconvex optimization; Packing and covering LPs

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APA (6^{th} Edition):

Boob, D. P. (2020). Convex and structured nonconvex optimization for modern machine learning: Complexity and algorithms. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63673

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

Boob, Digvijay Pravin. “Convex and structured nonconvex optimization for modern machine learning: Complexity and algorithms.” 2020. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/63673.

MLA Handbook (7^{th} Edition):

Boob, Digvijay Pravin. “Convex and structured nonconvex optimization for modern machine learning: Complexity and algorithms.” 2020. Web. 06 May 2021.

Vancouver:

Boob DP. Convex and structured nonconvex optimization for modern machine learning: Complexity and algorithms. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/63673.

Council of Science Editors:

Boob DP. Convex and structured nonconvex optimization for modern machine learning: Complexity and algorithms. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63673

9. Guzman Paredes, Cristobal. Information, complexity and structure in convex optimization.

Degree: PhD, Industrial and Systems Engineering, 2015, Georgia Tech

URL: http://hdl.handle.net/1853/53577

► This thesis is focused on the limits of performance of large-scale convex optimization algorithms. Classical theory of oracle complexity, first proposed by *Nemirovski* and Yudin…
(more)

Subjects/Keywords: Convex optimization; Optimization algorithms; Complexity theory; Lower bounds

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APA (6^{th} Edition):

Guzman Paredes, C. (2015). Information, complexity and structure in convex optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53577

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

Guzman Paredes, Cristobal. “Information, complexity and structure in convex optimization.” 2015. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/53577.

MLA Handbook (7^{th} Edition):

Guzman Paredes, Cristobal. “Information, complexity and structure in convex optimization.” 2015. Web. 06 May 2021.

Vancouver:

Guzman Paredes C. Information, complexity and structure in convex optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/53577.

Council of Science Editors:

Guzman Paredes C. Information, complexity and structure in convex optimization. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53577

10. Kilinc-Karzan, Fatma. Tractable relaxations and efficient algorithmic techniques for large-scale optimization.

Degree: PhD, Industrial and Systems Engineering, 2011, Georgia Tech

URL: http://hdl.handle.net/1853/41141

► In this thesis, we develop tractable relaxations and efficient algorithms for large-scale optimization. Our developments are motivated by a recent paradigm, Compressed Sensing (CS), which…
(more)

Subjects/Keywords: First order methods; Tractable relaxations; Convex programming; Signal processing; Mathematical optimization; Compressed sensing; Mathematical optimization; Algorithms; Signal processing

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APA (6^{th} Edition):

Kilinc-Karzan, F. (2011). Tractable relaxations and efficient algorithmic techniques for large-scale optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/41141

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

Kilinc-Karzan, Fatma. “Tractable relaxations and efficient algorithmic techniques for large-scale optimization.” 2011. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/41141.

MLA Handbook (7^{th} Edition):

Kilinc-Karzan, Fatma. “Tractable relaxations and efficient algorithmic techniques for large-scale optimization.” 2011. Web. 06 May 2021.

Vancouver:

Kilinc-Karzan F. Tractable relaxations and efficient algorithmic techniques for large-scale optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/41141.

Council of Science Editors:

Kilinc-Karzan F. Tractable relaxations and efficient algorithmic techniques for large-scale optimization. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/41141

11. He, Niao. Saddle point techniques in convex composite and error-in-measurement optimization.

Degree: PhD, Industrial and Systems Engineering, 2015, Georgia Tech

URL: http://hdl.handle.net/1853/54400

► This dissertation aims to develop efficient algorithms with improved scalability and stability properties for large-scale optimization and optimization under uncertainty, and to bridge some of…
(more)

Subjects/Keywords: Nonsmooth optimization; Composite minimization; First order methods; Stochastic optimization; Mirror prox

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APA (6^{th} Edition):

He, N. (2015). Saddle point techniques in convex composite and error-in-measurement optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54400

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

He, Niao. “Saddle point techniques in convex composite and error-in-measurement optimization.” 2015. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/54400.

MLA Handbook (7^{th} Edition):

He, Niao. “Saddle point techniques in convex composite and error-in-measurement optimization.” 2015. Web. 06 May 2021.

Vancouver:

He N. Saddle point techniques in convex composite and error-in-measurement optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/54400.

Council of Science Editors:

He N. Saddle point techniques in convex composite and error-in-measurement optimization. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/54400

12. Ouyang, Hua. Optimal stochastic and distributed algorithms for machine learning.

Degree: PhD, Computer Science, 2013, Georgia Tech

URL: http://hdl.handle.net/1853/49091

► Stochastic and data-distributed optimization algorithms have received lots of attention from the machine learning community due to the tremendous demand from the large-scale learning and…
(more)

Subjects/Keywords: Machine learning; BigData; Optimization; Stochastic optimization; Convergence rate; Distributed learning; Optimal methods; ADMM; Kernel method; SVM; Machine learning; Computer algorithms; Mathematical optimization

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APA (6^{th} Edition):

Ouyang, H. (2013). Optimal stochastic and distributed algorithms for machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/49091

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

Ouyang, Hua. “Optimal stochastic and distributed algorithms for machine learning.” 2013. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/49091.

MLA Handbook (7^{th} Edition):

Ouyang, Hua. “Optimal stochastic and distributed algorithms for machine learning.” 2013. Web. 06 May 2021.

Vancouver:

Ouyang H. Optimal stochastic and distributed algorithms for machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/49091.

Council of Science Editors:

Ouyang H. Optimal stochastic and distributed algorithms for machine learning. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/49091

13. Cox, Bruce. Applications of accuracy certificates for problems with convex structure.

Degree: PhD, Industrial and Systems Engineering, 2011, Georgia Tech

URL: http://hdl.handle.net/1853/39489

► Applications of accuracy certificates for problems with convex structure This dissertation addresses the efficient generation and potential applications of accuracy certificates in the framework…
(more)

Subjects/Keywords: Accuracy certificates; Convex optimization; Vector algebra; Linear programming; Convex functions; Convex domains

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APA (6^{th} Edition):

Cox, B. (2011). Applications of accuracy certificates for problems with convex structure. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/39489

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

Cox, Bruce. “Applications of accuracy certificates for problems with convex structure.” 2011. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/39489.

MLA Handbook (7^{th} Edition):

Cox, Bruce. “Applications of accuracy certificates for problems with convex structure.” 2011. Web. 06 May 2021.

Vancouver:

Cox B. Applications of accuracy certificates for problems with convex structure. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/39489.

Council of Science Editors:

Cox B. Applications of accuracy certificates for problems with convex structure. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/39489

14. Tekaya, Wajdi. Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems.

Degree: PhD, Industrial and Systems Engineering, 2013, Georgia Tech

URL: http://hdl.handle.net/1853/47582

► The main objective of this thesis is to investigate risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning…
(more)

Subjects/Keywords: Multistage stochastic programming; Dynamic equations; Stochastic dual dynamic programming; Sample average approximation; Risk averse; Average value-at-risk; Case studies; Robust optimization; Risk neutral and risk averse approaches; Stochastic programming; Hydrothermal electric power systems; Risk management; Robust optimization

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APA (6^{th} Edition):

Tekaya, W. (2013). Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/47582

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

Tekaya, Wajdi. “Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems.” 2013. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/47582.

MLA Handbook (7^{th} Edition):

Tekaya, Wajdi. “Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems.” 2013. Web. 06 May 2021.

Vancouver:

Tekaya W. Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/47582.

Council of Science Editors:

Tekaya W. Risk neutral and risk averse approaches to multistage stochastic programming with applications to hydrothermal operation planning problems. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/47582

15. Ortiz Diaz, Camilo. Block-decomposition and accelerated gradient methods for large-scale convex optimization.

Degree: PhD, Industrial and Systems Engineering, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/53438

► In this thesis, we develop block-decomposition (BD) methods and variants of accelerated *9gradient methods for large-scale conic programming and convex optimization, respectively. The BD methods,…
(more)

Subjects/Keywords: Semidefinite programing; Large-scale; Conjugate gradient; Accelerated gradient methods; Convex optimization; Quadratic programming; Complexity; Proximal; Extragradient; Block-decomposition; Conic optimization

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APA (6^{th} Edition):

Ortiz Diaz, C. (2014). Block-decomposition and accelerated gradient methods for large-scale convex optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53438

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

Ortiz Diaz, Camilo. “Block-decomposition and accelerated gradient methods for large-scale convex optimization.” 2014. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/53438.

MLA Handbook (7^{th} Edition):

Ortiz Diaz, Camilo. “Block-decomposition and accelerated gradient methods for large-scale convex optimization.” 2014. Web. 06 May 2021.

Vancouver:

Ortiz Diaz C. Block-decomposition and accelerated gradient methods for large-scale convex optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/53438.

Council of Science Editors:

Ortiz Diaz C. Block-decomposition and accelerated gradient methods for large-scale convex optimization. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/53438

16. Moran Ramirez, Diego Alejandro. Fundamental properties of convex mixed-integer programs.

Degree: PhD, Industrial and Systems Engineering, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/52309

► In this Ph.D. dissertation research, we lay the mathematical foundations of various fundamental concepts in convex mixed-integer programs (MIPs), that is, optimization problems where all…
(more)

Subjects/Keywords: Integer programming; Cutting planes; Convex hull; Integer hull; Optimization; Split cuts

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APA (6^{th} Edition):

Moran Ramirez, D. A. (2014). Fundamental properties of convex mixed-integer programs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52309

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

Moran Ramirez, Diego Alejandro. “Fundamental properties of convex mixed-integer programs.” 2014. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/52309.

MLA Handbook (7^{th} Edition):

Moran Ramirez, Diego Alejandro. “Fundamental properties of convex mixed-integer programs.” 2014. Web. 06 May 2021.

Vancouver:

Moran Ramirez DA. Fundamental properties of convex mixed-integer programs. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/52309.

Council of Science Editors:

Moran Ramirez DA. Fundamental properties of convex mixed-integer programs. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52309

17. Xiao, Ying. New tools for unsupervised learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

URL: http://hdl.handle.net/1853/52995

► In an unsupervised learning problem, one is given an unlabelled dataset and hopes to find some hidden structure; the prototypical example is clustering similar data.…
(more)

Subjects/Keywords: Tensor; Spectral decomposition; Unsupervised learning; Independent component analysis; Fourier transform; Gaussian mixture model; Feature selection

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APA (6^{th} Edition):

Xiao, Y. (2014). New tools for unsupervised learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52995

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

Xiao, Ying. “New tools for unsupervised learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/52995.

MLA Handbook (7^{th} Edition):

Xiao, Ying. “New tools for unsupervised learning.” 2014. Web. 06 May 2021.

Vancouver:

Xiao Y. New tools for unsupervised learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/52995.

Council of Science Editors:

Xiao Y. New tools for unsupervised learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52995

18. Cakmak, Ulas. On risk-averse and robust inventory problems.

Degree: PhD, Industrial and Systems Engineering, 2012, Georgia Tech

URL: http://hdl.handle.net/1853/44745

► The thesis focuses on the analysis of various extensions of the classical multi-period single-item stochastic inventory problem. Specifically, we investigate two particular approaches of modeling…
(more)

Subjects/Keywords: Inventory management; Risk-averse models; Dynamic robust models; Coherent risk measures; Inventory control; Risk management; Robust optimization

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APA (6^{th} Edition):

Cakmak, U. (2012). On risk-averse and robust inventory problems. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/44745

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

Cakmak, Ulas. “On risk-averse and robust inventory problems.” 2012. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/44745.

MLA Handbook (7^{th} Edition):

Cakmak, Ulas. “On risk-averse and robust inventory problems.” 2012. Web. 06 May 2021.

Vancouver:

Cakmak U. On risk-averse and robust inventory problems. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/44745.

Council of Science Editors:

Cakmak U. On risk-averse and robust inventory problems. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/44745

19. Shu, Yan. Future aircraft networks and schedules.

Degree: PhD, Mathematics, 2011, Georgia Tech

URL: http://hdl.handle.net/1853/41221

► This thesis has focused on an aircraft schedule and network design problem that involves multiple types of aircraft and flight service. First, this thesis expands…
(more)

Subjects/Keywords: Timetable model; Fleet assignment model; Frequency assignment model; Scheduling; Transportation engineering; Scheduling; Mathematical optimization; Algorithms

Record Details Similar Records

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

APA (6^{th} Edition):

Shu, Y. (2011). Future aircraft networks and schedules. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/41221

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

Shu, Yan. “Future aircraft networks and schedules.” 2011. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/41221.

MLA Handbook (7^{th} Edition):

Shu, Yan. “Future aircraft networks and schedules.” 2011. Web. 06 May 2021.

Vancouver:

Shu Y. Future aircraft networks and schedules. [Internet] [Doctoral dissertation]. Georgia Tech; 2011. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/41221.

Council of Science Editors:

Shu Y. Future aircraft networks and schedules. [Doctoral Dissertation]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/41221

20. Lee, Ji Yun. Risk-informed decision for civil infrastructure exposed to natural hazards: sharing risk across multiple generations.

Degree: PhD, Civil and Environmental Engineering, 2015, Georgia Tech

URL: http://hdl.handle.net/1853/53965

► Civil infrastructure facilities play a central role in the economic, social and political health of modern society and their safety, integrity and functionality must be…
(more)

Subjects/Keywords: Civil infrastructure; Discounting; Intergenerational equity; Climate change; Hurricanes; Risk-informed decision; Structural engineering; Structural reliability; Sustainability

Record Details Similar Records

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

APA (6^{th} Edition):

Lee, J. Y. (2015). Risk-informed decision for civil infrastructure exposed to natural hazards: sharing risk across multiple generations. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53965

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

Lee, Ji Yun. “Risk-informed decision for civil infrastructure exposed to natural hazards: sharing risk across multiple generations.” 2015. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/53965.

MLA Handbook (7^{th} Edition):

Lee, Ji Yun. “Risk-informed decision for civil infrastructure exposed to natural hazards: sharing risk across multiple generations.” 2015. Web. 06 May 2021.

Vancouver:

Lee JY. Risk-informed decision for civil infrastructure exposed to natural hazards: sharing risk across multiple generations. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/53965.

Council of Science Editors:

Lee JY. Risk-informed decision for civil infrastructure exposed to natural hazards: sharing risk across multiple generations. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53965

21. Xie, Weijun. Relaxations and approximations of chance constrained stochastic programs.

Degree: PhD, Industrial and Systems Engineering, 2017, Georgia Tech

URL: http://hdl.handle.net/1853/58678

► A chance constrained stochastic programming (CCSP) problem involves constraints with random parameters that are required to be satisfied with a prespecified probability threshold. Such constraints…
(more)

Subjects/Keywords: chance constraint; approximation algorithm; Lagrangian relaxation; distributionally robust; convex program

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

APA (6^{th} Edition):

Xie, W. (2017). Relaxations and approximations of chance constrained stochastic programs. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58678

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

Xie, Weijun. “Relaxations and approximations of chance constrained stochastic programs.” 2017. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/58678.

MLA Handbook (7^{th} Edition):

Xie, Weijun. “Relaxations and approximations of chance constrained stochastic programs.” 2017. Web. 06 May 2021.

Vancouver:

Xie W. Relaxations and approximations of chance constrained stochastic programs. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/58678.

Council of Science Editors:

Xie W. Relaxations and approximations of chance constrained stochastic programs. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58678

Georgia Tech

22. Shepardson, Dylan. Algorithms for inverting Hodgkin-Huxley type neuron models.

Degree: PhD, Algorithms, Combinatorics, and Optimization, 2009, Georgia Tech

URL: http://hdl.handle.net/1853/31686

► The study of neurons is of fundamental importance in biology and medicine. Neurons are the most basic unit of information processing in the nervous system…
(more)

Subjects/Keywords: Inverse problems; Hodgkin-Huxley; Neuroscience; Computational neuroscience; Neuron modeling; Optimization; Parameter optimization; Algorithms; Neurons; Neurosciences

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

APA (6^{th} Edition):

Shepardson, D. (2009). Algorithms for inverting Hodgkin-Huxley type neuron models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/31686

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

Shepardson, Dylan. “Algorithms for inverting Hodgkin-Huxley type neuron models.” 2009. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/31686.

MLA Handbook (7^{th} Edition):

Shepardson, Dylan. “Algorithms for inverting Hodgkin-Huxley type neuron models.” 2009. Web. 06 May 2021.

Vancouver:

Shepardson D. Algorithms for inverting Hodgkin-Huxley type neuron models. [Internet] [Doctoral dissertation]. Georgia Tech; 2009. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/31686.

Council of Science Editors:

Shepardson D. Algorithms for inverting Hodgkin-Huxley type neuron models. [Doctoral Dissertation]. Georgia Tech; 2009. Available from: http://hdl.handle.net/1853/31686

Georgia Tech

23. O'Neal, Jerome W. The Use of Preconditioned Iterative Linear Solvers in Interior-Point Methods and Related Topics.

Degree: PhD, Industrial and Systems Engineering, 2005, Georgia Tech

URL: http://hdl.handle.net/1853/11647

► Over the last 25 years, interior-point methods (IPMs) have emerged as a viable class of algorithms for solving various forms of conic optimization problems. Most…
(more)

Subjects/Keywords: Maximum weight basis preconditioner; Iterative solvers; Inexact search directions; Adaptive preconditioning; Conjugate gradient; Interior-point methods; Conjugate gradient methods; Interior-point methods; Iterative methods (Mathematics); Mathematical optimization

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

APA (6^{th} Edition):

O'Neal, J. W. (2005). The Use of Preconditioned Iterative Linear Solvers in Interior-Point Methods and Related Topics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/11647

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

O'Neal, Jerome W. “The Use of Preconditioned Iterative Linear Solvers in Interior-Point Methods and Related Topics.” 2005. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/11647.

MLA Handbook (7^{th} Edition):

O'Neal, Jerome W. “The Use of Preconditioned Iterative Linear Solvers in Interior-Point Methods and Related Topics.” 2005. Web. 06 May 2021.

Vancouver:

O'Neal JW. The Use of Preconditioned Iterative Linear Solvers in Interior-Point Methods and Related Topics. [Internet] [Doctoral dissertation]. Georgia Tech; 2005. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/11647.

Council of Science Editors:

O'Neal JW. The Use of Preconditioned Iterative Linear Solvers in Interior-Point Methods and Related Topics. [Doctoral Dissertation]. Georgia Tech; 2005. Available from: http://hdl.handle.net/1853/11647

Georgia Tech

24. Lu, Zhaosong. Algorithm Design and Analysis for Large-Scale Semidefinite Programming and Nonlinear Programming.

Degree: PhD, Industrial and Systems Engineering, 2005, Georgia Tech

URL: http://hdl.handle.net/1853/7151

► The limiting behavior of weighted paths associated with the semidefinite program (SDP) map X^{1/2}SX^{1/2} was studied and some applications to error bound analysis and superlinear…
(more)

Subjects/Keywords: Semidefinite program; Weighted paths; Trust region subproblem; Maximum weight basis preconditioner; Preconditioned iterative linear solver; Convex quadratic program; Smooth saddle point problem; Mirror-prox algorithm

Record Details Similar Records

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

APA (6^{th} Edition):

Lu, Z. (2005). Algorithm Design and Analysis for Large-Scale Semidefinite Programming and Nonlinear Programming. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/7151

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

Lu, Zhaosong. “Algorithm Design and Analysis for Large-Scale Semidefinite Programming and Nonlinear Programming.” 2005. Doctoral Dissertation, Georgia Tech. Accessed May 06, 2021. http://hdl.handle.net/1853/7151.

MLA Handbook (7^{th} Edition):

Lu, Zhaosong. “Algorithm Design and Analysis for Large-Scale Semidefinite Programming and Nonlinear Programming.” 2005. Web. 06 May 2021.

Vancouver:

Lu Z. Algorithm Design and Analysis for Large-Scale Semidefinite Programming and Nonlinear Programming. [Internet] [Doctoral dissertation]. Georgia Tech; 2005. [cited 2021 May 06]. Available from: http://hdl.handle.net/1853/7151.

Council of Science Editors:

Lu Z. Algorithm Design and Analysis for Large-Scale Semidefinite Programming and Nonlinear Programming. [Doctoral Dissertation]. Georgia Tech; 2005. Available from: http://hdl.handle.net/1853/7151