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You searched for +publisher:"Princeton University" +contributor:("Powell, Warren B"). Showing records 1 – 9 of 9 total matches.

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Princeton University

1. Jiang, Daniel Ruoling. Risk-Neutral and Risk-Averse Approximate Dynamic Programming Methods .

Degree: PhD, 2016, Princeton University

 In this thesis, we propose approximate dynamic programming (ADP) methods for solving risk-neutral and risk-averse sequential decision problems under uncertainty, focusing on models that are… (more)

Subjects/Keywords: approximate dynamic programming; energy bidding; monotonicity; risk averse

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APA (6th Edition):

Jiang, D. R. (2016). Risk-Neutral and Risk-Averse Approximate Dynamic Programming Methods . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp016969z3194

Chicago Manual of Style (16th Edition):

Jiang, Daniel Ruoling. “Risk-Neutral and Risk-Averse Approximate Dynamic Programming Methods .” 2016. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp016969z3194.

MLA Handbook (7th Edition):

Jiang, Daniel Ruoling. “Risk-Neutral and Risk-Averse Approximate Dynamic Programming Methods .” 2016. Web. 21 Oct 2019.

Vancouver:

Jiang DR. Risk-Neutral and Risk-Averse Approximate Dynamic Programming Methods . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp016969z3194.

Council of Science Editors:

Jiang DR. Risk-Neutral and Risk-Averse Approximate Dynamic Programming Methods . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp016969z3194


Princeton University

2. Li, Yan. Optimal Learning in High Dimensions .

Degree: PhD, 2016, Princeton University

 Collecting information in the course of sequential decision-making can be extremely challenging in high-dimensional settings, where the number of measurement budget is much smaller than… (more)

Subjects/Keywords: Bayesian Optimization; High-dimensional Statistics; Optimal Learning

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APA (6th Edition):

Li, Y. (2016). Optimal Learning in High Dimensions . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp014m90dx99b

Chicago Manual of Style (16th Edition):

Li, Yan. “Optimal Learning in High Dimensions .” 2016. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp014m90dx99b.

MLA Handbook (7th Edition):

Li, Yan. “Optimal Learning in High Dimensions .” 2016. Web. 21 Oct 2019.

Vancouver:

Li Y. Optimal Learning in High Dimensions . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp014m90dx99b.

Council of Science Editors:

Li Y. Optimal Learning in High Dimensions . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp014m90dx99b


Princeton University

3. Cheng, Bolong. Local Approximation and Hierarchical Methods for Stochastic Optimization .

Degree: PhD, 2017, Princeton University

 In this thesis, we present local and hierarchical approximation methods for two classes of stochastic optimization problems: optimal learning and Markov decision processes. For the… (more)

Subjects/Keywords: battery optimization; dynamic programming; local approximation; optimal learning; renewable energy; stochastic optimization

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APA (6th Edition):

Cheng, B. (2017). Local Approximation and Hierarchical Methods for Stochastic Optimization . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01jw827f27t

Chicago Manual of Style (16th Edition):

Cheng, Bolong. “Local Approximation and Hierarchical Methods for Stochastic Optimization .” 2017. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp01jw827f27t.

MLA Handbook (7th Edition):

Cheng, Bolong. “Local Approximation and Hierarchical Methods for Stochastic Optimization .” 2017. Web. 21 Oct 2019.

Vancouver:

Cheng B. Local Approximation and Hierarchical Methods for Stochastic Optimization . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp01jw827f27t.

Council of Science Editors:

Cheng B. Local Approximation and Hierarchical Methods for Stochastic Optimization . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp01jw827f27t


Princeton University

4. Wang, Yingfei. Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications .

Degree: PhD, 2017, Princeton University

 This thesis considers the problem of sequentially making decisions under uncertainty, exploring the ways where efficient information collection influences and improves decision-making strategies. Most previous… (more)

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APA (6th Edition):

Wang, Y. (2017). Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp0147429c74g

Chicago Manual of Style (16th Edition):

Wang, Yingfei. “Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications .” 2017. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp0147429c74g.

MLA Handbook (7th Edition):

Wang, Yingfei. “Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications .” 2017. Web. 21 Oct 2019.

Vancouver:

Wang Y. Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp0147429c74g.

Council of Science Editors:

Wang Y. Advances in decision-making under uncertainty: inference, finite-time analysis, and health applications . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp0147429c74g


Princeton University

5. Perkins, Raymond Theodore. Multistage Stochastic Programming with Parametric Cost Function Approximations .

Degree: PhD, 2018, Princeton University

 A widely used heuristic for solving stochastic optimization problems is to use a deterministic rolling horizon procedure which has been modified to handle uncertainty (e.g.… (more)

Subjects/Keywords: Cost Function Approximations; Stochastic Optimization; Stochastic Programming

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APA (6th Edition):

Perkins, R. T. (2018). Multistage Stochastic Programming with Parametric Cost Function Approximations . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp018c97kt118

Chicago Manual of Style (16th Edition):

Perkins, Raymond Theodore. “Multistage Stochastic Programming with Parametric Cost Function Approximations .” 2018. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp018c97kt118.

MLA Handbook (7th Edition):

Perkins, Raymond Theodore. “Multistage Stochastic Programming with Parametric Cost Function Approximations .” 2018. Web. 21 Oct 2019.

Vancouver:

Perkins RT. Multistage Stochastic Programming with Parametric Cost Function Approximations . [Internet] [Doctoral dissertation]. Princeton University; 2018. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp018c97kt118.

Council of Science Editors:

Perkins RT. Multistage Stochastic Programming with Parametric Cost Function Approximations . [Doctoral Dissertation]. Princeton University; 2018. Available from: http://arks.princeton.edu/ark:/88435/dsp018c97kt118

6. Salas, Daniel Federico. Approximate Dynamic Programming Algorithms for the Control of Grid-Level Storage in the Presence of Renewable Generation .

Degree: PhD, 2014, Princeton University

 In this dissertation, we present and benchmark an approximate dynamic programming algorithm that is capable of designing near-optimal control policies for time-dependent, finite-horizon energy storage… (more)

Subjects/Keywords: control; dynamic programming; energy systems; renewable energy; stochastic optimization

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APA (6th Edition):

Salas, D. F. (2014). Approximate Dynamic Programming Algorithms for the Control of Grid-Level Storage in the Presence of Renewable Generation . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp011v53jx135

Chicago Manual of Style (16th Edition):

Salas, Daniel Federico. “Approximate Dynamic Programming Algorithms for the Control of Grid-Level Storage in the Presence of Renewable Generation .” 2014. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp011v53jx135.

MLA Handbook (7th Edition):

Salas, Daniel Federico. “Approximate Dynamic Programming Algorithms for the Control of Grid-Level Storage in the Presence of Renewable Generation .” 2014. Web. 21 Oct 2019.

Vancouver:

Salas DF. Approximate Dynamic Programming Algorithms for the Control of Grid-Level Storage in the Presence of Renewable Generation . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp011v53jx135.

Council of Science Editors:

Salas DF. Approximate Dynamic Programming Algorithms for the Control of Grid-Level Storage in the Presence of Renewable Generation . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp011v53jx135

7. Scott, Warren Robert. Energy Storage Applications of the Knowledge Gradient for Calibrating Continuous Parameters, Approximate Policy Iteration using Bellman Error Minimization with Instrumental Variables, and Covariance Matrix Estimation using an Errors-in-Variables Factor Model .

Degree: PhD, 2012, Princeton University

 We describe an adaptation of the knowledge gradient, originally developed for discrete ranking and selection problems, to the problem of calibrating continuous parameters for the… (more)

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APA (6th Edition):

Scott, W. R. (2012). Energy Storage Applications of the Knowledge Gradient for Calibrating Continuous Parameters, Approximate Policy Iteration using Bellman Error Minimization with Instrumental Variables, and Covariance Matrix Estimation using an Errors-in-Variables Factor Model . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01hq37vn61b

Chicago Manual of Style (16th Edition):

Scott, Warren Robert. “Energy Storage Applications of the Knowledge Gradient for Calibrating Continuous Parameters, Approximate Policy Iteration using Bellman Error Minimization with Instrumental Variables, and Covariance Matrix Estimation using an Errors-in-Variables Factor Model .” 2012. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp01hq37vn61b.

MLA Handbook (7th Edition):

Scott, Warren Robert. “Energy Storage Applications of the Knowledge Gradient for Calibrating Continuous Parameters, Approximate Policy Iteration using Bellman Error Minimization with Instrumental Variables, and Covariance Matrix Estimation using an Errors-in-Variables Factor Model .” 2012. Web. 21 Oct 2019.

Vancouver:

Scott WR. Energy Storage Applications of the Knowledge Gradient for Calibrating Continuous Parameters, Approximate Policy Iteration using Bellman Error Minimization with Instrumental Variables, and Covariance Matrix Estimation using an Errors-in-Variables Factor Model . [Internet] [Doctoral dissertation]. Princeton University; 2012. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp01hq37vn61b.

Council of Science Editors:

Scott WR. Energy Storage Applications of the Knowledge Gradient for Calibrating Continuous Parameters, Approximate Policy Iteration using Bellman Error Minimization with Instrumental Variables, and Covariance Matrix Estimation using an Errors-in-Variables Factor Model . [Doctoral Dissertation]. Princeton University; 2012. Available from: http://arks.princeton.edu/ark:/88435/dsp01hq37vn61b

8. He, Xinyu. Optimal Learning for Nonlinear Parametric Belief Models .

Degree: PhD, 2017, Princeton University

 Many real-world optimization problems require making measurements to determine which choice works the best. Such measurements can be noisy and expensive, as might arise in… (more)

Subjects/Keywords: knowledge gradient; nonlinear parametric model; optimal learning

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APA (6th Edition):

He, X. (2017). Optimal Learning for Nonlinear Parametric Belief Models . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01m613n1193

Chicago Manual of Style (16th Edition):

He, Xinyu. “Optimal Learning for Nonlinear Parametric Belief Models .” 2017. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp01m613n1193.

MLA Handbook (7th Edition):

He, Xinyu. “Optimal Learning for Nonlinear Parametric Belief Models .” 2017. Web. 21 Oct 2019.

Vancouver:

He X. Optimal Learning for Nonlinear Parametric Belief Models . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp01m613n1193.

Council of Science Editors:

He X. Optimal Learning for Nonlinear Parametric Belief Models . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp01m613n1193


Princeton University

9. Kim, Jae Ho. Quantile Optimization in the Presence of Heavy-Tailed Stochastic Processes, and an application to Electricity Markets .

Degree: PhD, 2011, Princeton University

 In this thesis, we study the electricity market to construct stochastic models that helps us make various decisions under uncertainty. First, we propose an hour-ahead… (more)

Subjects/Keywords: dynamic programming; median-reversion; quantile; stochastic optimization

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APA (6th Edition):

Kim, J. H. (2011). Quantile Optimization in the Presence of Heavy-Tailed Stochastic Processes, and an application to Electricity Markets . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01p5547r386

Chicago Manual of Style (16th Edition):

Kim, Jae Ho. “Quantile Optimization in the Presence of Heavy-Tailed Stochastic Processes, and an application to Electricity Markets .” 2011. Doctoral Dissertation, Princeton University. Accessed October 21, 2019. http://arks.princeton.edu/ark:/88435/dsp01p5547r386.

MLA Handbook (7th Edition):

Kim, Jae Ho. “Quantile Optimization in the Presence of Heavy-Tailed Stochastic Processes, and an application to Electricity Markets .” 2011. Web. 21 Oct 2019.

Vancouver:

Kim JH. Quantile Optimization in the Presence of Heavy-Tailed Stochastic Processes, and an application to Electricity Markets . [Internet] [Doctoral dissertation]. Princeton University; 2011. [cited 2019 Oct 21]. Available from: http://arks.princeton.edu/ark:/88435/dsp01p5547r386.

Council of Science Editors:

Kim JH. Quantile Optimization in the Presence of Heavy-Tailed Stochastic Processes, and an application to Electricity Markets . [Doctoral Dissertation]. Princeton University; 2011. Available from: http://arks.princeton.edu/ark:/88435/dsp01p5547r386

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