Now showing items 124-143 of 157

    • Regression under a modern optimization lens 

      King, Angela, Ph. D. Massachusetts Institute of Technology (Massachusetts Institute of Technology, 2015)
      In the last twenty-five years (1990-2014), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 200 billion factor speedup in solving mixed integer optimization ...
    • Regulating exploration in multi-armed bandit problems with time patterns and dying arms 

      Tracà, Stefano (Massachusetts Institute of Technology, 2018)
      In retail, there are predictable yet dramatic time-dependent patterns in customer behavior, such as periodic changes in the number of visitors, or increases in customers just before major holidays. The standard paradigm ...
    • Resource allocation in stochastic processing networks : performance and scaling 

      Zhong, Yuan, Ph.D. Massachusetts Institute of Technology. Operations Research Center (Massachusetts Institute of Technology, 2012)
      This thesis addresses the design and analysis of resource allocation policies in largescale stochastic systems, motivated by examples such as the Internet, cloud facilities, wireless networks, etc. A canonical framework ...
    • Revenue management and learning in systems of reusable resources 

      Owen, Zachary Davis (Massachusetts Institute of Technology, 2018)
      Many problems in revenue management and operations management more generally can be framed as problems of resource allocation. This thesis focuses on developing policies and guarantees for resource allocation problems with ...
    • Reverse logistics for consumer electronics : forecasting failures, managing inventory, and matching warranties 

      Calmon, André du Pin (Massachusetts Institute of Technology, 2015)
      The goal of this thesis is to describe, model, and optimize reverse logistics systems commonly used in the Consumer Electronics industry. The context and motivation for this work stem from a collaboration with an industrial ...
    • Robust estimation, regression and ranking with applications in portfolio optimization 

      Nguyen, Tri-Dung, Ph. D. Massachusetts Institute of Technology (Massachusetts Institute of Technology, 2009)
      Classical methods of maximum likelihood and least squares rely a great deal on the correctness of the model assumptions. Since these assumptions are only approximations of reality, many robust statistical methods have been ...
    • Robust model selection and outlier detection in linear regressions 

      McCann, Lauren, Ph. D. Massachusetts Institute of Technology (Massachusetts Institute of Technology, 2006)
      In this thesis, we study the problems of robust model selection and outlier detection in linear regression. The results of data analysis based on linear regressions are highly sensitive to model choice and the existence ...
    • Robust optimization 

      Sim, Melvyn, 1971- (Massachusetts Institute of Technology, 2004)
      We propose new methodologies in robust optimization that promise greater tractability, both theoretically and practically than the classical robust framework. We cover a broad range of mathematical optimization problems, ...
    • A robust optimization approach to finance 

      Pachamanova, Dessislava A. (Dessislava Angelova), 1975- (Massachusetts Institute of Technology, 2002)
      An important issue in real-world optimization problems is how to treat uncertain coefficients. Robust optimization is a modeling methodology that takes a deterministic view: the optimal solution is required to remain ...
    • A robust optimization approach to online problems 

      Korolko, Nikita (Nikita E.) (Massachusetts Institute of Technology, 2017)
      In this thesis, we consider online optimization problems that are characterized by incrementally revealed input data and sequential irrevocable decisions that must be made without complete knowledge of the future. We employ ...
    • Robust optimization, game theory, and variational inequalities 

      Aghassi, Michele Leslie (Massachusetts Institute of Technology, 2005)
      We propose a robust optimization approach to analyzing three distinct classes of problems related to the notion of equilibrium: the nominal variational inequality (VI) problem over a polyhedron, the finite game under payoff ...
    • Robust, risk-sensitive, and data-driven control of Markov Decision Processes 

      Le Tallec, Yann (Massachusetts Institute of Technology, 2007)
      Markov Decision Processes (MDPs) model problems of sequential decision-making under uncertainty. They have been studied and applied extensively. Nonetheless, there are two major barriers that still hinder the applicability ...
    • Scheduling multiclass queueing networks and job shops using fluid and semidefinite relaxations 

      Sethuraman, Jayachandran, 1970- (Massachusetts Institute of Technology, 1999)
      Queueing networks serve a& useful models for a variety of problems arising in modern communications, computer, and manufacturing systems. Since the optimal control problem for queueing networks is well-known to be intractable, ...
    • Selfish versus coordinated routing in network games 

      Stier Moses, Nicolás E (Massachusetts Institute of Technology, 2004)
      A common assumption in network optimization models is that a central authority controls the whole system. However, in some applications there are independent users, and assuming that they will follow directions given by ...
    • Sequential data inference via matrix estimation : causal inference, cricket and retail 

      Amjad, Muhammad Jehangir (Massachusetts Institute of Technology, 2018)
      This thesis proposes a unified framework to capture the temporal and longitudinal variation across multiple instances of sequential data. Examples of such data include sales of a product over a period of time across several ...
    • Social networks : rational learning and information aggregation 

      Lobel, Ilan (Massachusetts Institute of Technology, 2009)
      This thesis studies the learning problem of a set of agents connected via a general social network. We address the question of how dispersed information spreads in social networks and whether the information is efficiently ...
    • Sparsity and robustness in modern statistical estimation 

      Copenhaver, Martin Steven (Massachusetts Institute of Technology, 2018)
      Two principles at the forefront of modern machine learning and statistics are sparse modeling and robustness. Sparse modeling enables the construction of simpler statistical models, with examples including the Lasso and ...
    • Statistical learning for decision making : interpretability, uncertainty, and inference 

      Letham, Benjamin (Massachusetts Institute of Technology, 2015)
      Data and predictive modeling are an increasingly important part of decision making. Here we present advances in several areas of statistical learning that are important for gaining insight from large amounts of data, and ...
    • Stochastic analysis via robust optimization 

      Youssef, Nataly (Massachusetts Institute of Technology, 2016)
      To evaluate the performance and optimize systems under uncertainty, two main avenues have been suggested in the literature: stochastic analysis and optimization describing the uncertainty probabilistically and robust ...
    • Stochastic models and data driven simulations for healthcare operations 

      Anderson, Ross Michael (Massachusetts Institute of Technology, 2014)
      This thesis considers problems in two areas in the healthcare operations: Kidney Paired Donation (KPD) and scheduling medical residents in hospitals. In both areas, we explore the implications of policy change through high ...