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You searched for +publisher:"Georgia Tech" +contributor:("Nemirovski, Arkadi"). Showing records 1 – 24 of 24 total matches.

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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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 The limiting behavior of weighted paths associated with the semidefinite program (SDP) map X1/2SX1/2 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

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

APA (6th 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 (16th 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 (7th 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

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